system

Abstract

The system according to this embodiment aims to automatically suggest bookmark locations based on the reader's reading pace and level of attention. [Solution] The system according to the embodiment comprises a sensing unit, a suggestion unit, a highlighting unit, and a display unit. The sensing unit senses the reader's reading pace and level of attention. The suggestion unit automatically suggests bookmark locations based on the information sensed by the sensing unit. The highlighting unit highlights keywords and phrases. The display unit visually displays pause locations and highlighted sections of the audiobook.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is not possible to automatically propose a bookmark position based on the reader's reading pace or attention level, and there is room for improvement in enhancing the reading experience.

[0005] The system according to the embodiment aims to automatically propose a bookmark position based on the reader's reading pace or attention level.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a sensing unit, a suggestion unit, a highlighting unit, and a display unit. The sensing unit senses the reader's reading pace and level of attention. The suggestion unit automatically suggests bookmark locations based on the information sensed by the sensing unit. The highlighting unit highlights keywords and phrases. The display unit visually displays pause locations and highlighted sections of the audiobook. [Effects of the Invention]

[0007] The system according to this embodiment can automatically suggest bookmark locations based on the reader's reading pace and level of attention. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The bookmark support system according to an embodiment of the present invention is a system that senses the reader's reading pace and level of attention and automatically suggests bookmark locations. This bookmark support system senses the reader's reading pace and level of attention, and the AI ​​automatically suggests bookmark locations based on the sensed information. It also has a function to highlight important keywords and phrases so that they can be easily referenced later. Furthermore, in the case of audiobooks, it also has a function to visually display pause locations and highlighted sections. For example, the bookmark support system senses the speed at which the reader turns pages and the time spent at specific locations. This information is input to the AI. Next, the AI ​​automatically suggests bookmark locations based on the sensed information. For example, it suggests locations where the reader has spent a long time or frequently returns to as bookmark locations. This allows the reader to read efficiently without missing important parts. Furthermore, it also has a function to highlight important keywords and phrases so that they can be easily referenced later. For example, the AI ​​analyzes the text and automatically highlights important keywords and phrases. This allows the reader to easily find important information later. In the case of audiobooks, it also has a function to visually display pause locations and highlighted sections. For example, AI can record the playback position of an audiobook and visually display paused or highlighted sections. This allows readers to easily identify important parts of the audiobook. This mechanism enables readers to read efficiently, avoid missing important information, and easily refer back to it later. Furthermore, the ability to visually identify important sections in audiobooks enhances the reading experience. Thus, a bookmark support system can sense the reader's reading pace and attention span and automatically suggest bookmark locations to support efficient reading.

[0029] The bookmark support system according to this embodiment comprises a sensing unit, a suggestion unit, a highlighting unit, and a display unit. The sensing unit senses the reader's reading pace and level of attention. For example, the sensing unit senses the speed at which the reader turns pages and the time spent at specific locations. The sensing unit may also be equipped with a camera for tracking the reader's gaze and a sensor for measuring heart rate. For example, the sensing unit uses an infrared camera to track the reader's gaze and collects data on the movement of their eyes. The sensing unit can also use an optical sensor to measure the reader's heart rate and collect data on changes in heart rate. The suggestion unit automatically suggests bookmark locations based on the information sensed by the sensing unit. For example, the suggestion unit suggests locations where the reader has spent a long time or frequently returns as bookmark locations. The suggestion unit can also estimate the reader's emotions and adjust the method of suggesting bookmark locations based on the estimated emotions. For example, the suggestion unit may suggest bookmark locations more frequently if the reader is excited and less frequently if the reader is relaxed. The highlighting section analyzes the text and automatically highlights important keywords and phrases. For example, it might use natural language processing techniques to analyze the text and extract important keywords and phrases. The highlighting section can also estimate the reader's emotions and adjust the highlighting method based on that estimation. For instance, if the reader is excited, it might use emphasized colors and effects; if the reader is relaxed, it might use calming colors and effects. The display section records the audiobook's playback position and visually displays paused and highlighted sections. For example, it might record the audiobook's playback time and chapters and visually display paused and highlighted sections. The display section can also estimate the reader's emotions and adjust the display method based on that estimation. For instance, if the reader is excited, it might use a visually stimulating display method; if the reader is relaxed, it might use a calming display method.As a result, the bookmark support system according to this embodiment can sense the reader's reading pace and level of attention, and automatically suggest bookmark locations, thereby supporting efficient reading.

[0030] The sensing unit detects the reader's reading pace and level of attention. For example, it can sense the speed at which the reader turns pages and the time spent at specific points in the text. Specifically, the sensing unit measures the page-turning speed in milliseconds and meticulously records how much time the reader spends on each page. Furthermore, the sensing unit can be equipped with a camera to track the reader's gaze and a sensor to measure heart rate. For example, the sensing unit can use an infrared camera to track the reader's gaze and collect data on eye movements. Eye-tracking technology is used to accurately understand which parts the reader is focusing on, and the duration of eye contact and movement patterns can be analyzed. The sensing unit can also use an optical sensor to measure the reader's heart rate and collect data on changes in heart rate. Changes in heart rate are used as an indicator of the reader's level of excitement or relaxation, allowing for real-time understanding of their emotional state while reading. In this way, the sensing unit can comprehensively sense the reader's reading behavior and physiological responses and provide detailed data. Furthermore, the sensing unit can transmit this data to a cloud server and share information in real time in conjunction with other system components. This allows the sensing unit to gain a deeper understanding of the reader's reading experience and build a foundation for providing support tailored to individual needs.

[0031] The suggestion unit automatically proposes bookmark locations based on information sensed by the sensing unit. For example, the suggestion unit suggests bookmark locations such as sections where the reader has spent a long time or frequently revisited. Specifically, the suggestion unit analyzes data provided by the sensing unit to identify sections that the reader paid particular attention to or reread multiple times. This allows it to automatically suggest sections that the reader found important as bookmarks. Furthermore, the suggestion unit can estimate the reader's emotions and adjust the bookmark suggestion method based on the estimated emotions. For example, if the reader is excited, the suggestion unit will suggest bookmark locations more frequently, and if the reader is relaxed, it will suggest bookmark locations less frequently. Heart rate data and eye-tracking data provided by the sensing unit can be used for emotion estimation. This allows the suggestion unit to provide flexible bookmark suggestions according to the reader's emotional state. In addition, the suggestion unit can learn past reading history and the reader's preferences and incorporate algorithms to suggest optimal bookmark locations for individual readers. This allows the suggestion unit to improve the reader's reading experience and support efficient reading.

[0032] The highlighting function analyzes text and automatically highlights important keywords and phrases. For example, it uses natural language processing techniques to analyze text and extract key keywords and phrases. Specifically, it uses topic modeling and keyword extraction algorithms to understand the context of the text and identify keywords and phrases containing important information. This allows readers to read efficiently without missing important information. Furthermore, the highlighting function can estimate the reader's emotions and adjust the highlighting style based on the estimated emotions. For example, if the reader is excited, it uses emphasized colors and effects for highlighting; if the reader is relaxed, it uses calming colors and effects. Emotion estimation can utilize heart rate and eye-tracking data provided by the sensing unit. This allows the highlighting function to provide flexible highlighting expressions that respond to the reader's emotional state. Additionally, the highlighting function can collect reader feedback and continuously improve the accuracy and style of highlighting. This enhances the reader's reading experience and effectively conveys important information.

[0033] The display unit records the playback position of the audiobook and visually displays paused and highlighted sections. Specifically, it records the audiobook's playback time and chapters, visually displaying paused and highlighted sections. More precisely, it records the audiobook's playback time in seconds and identifies the currently playing chapter or section. This allows the reader to easily resume playback from where it left off. Furthermore, the display unit can estimate the reader's emotions and adjust the display method based on that estimation. For example, it might use a visually stimulating display method if the reader is excited, and a calming display method if the reader is relaxed. Heart rate data and eye-tracking data provided by the sensing unit can be used for emotion estimation. This allows the display unit to provide a flexible display method tailored to the reader's emotional state. Additionally, the display unit can collect reader feedback and continuously improve the accuracy and effectiveness of the display method. This allows the display unit to enhance the reader's reading experience and effectively manage the audiobook's playback position and highlighted sections.

[0034] The sensing unit can sense the speed at which pages are turned and the time spent on each page. For example, the sensing unit can measure the speed at which pages are turned in seconds and count the number of pages. The sensing unit can also measure the time spent on specific pages or paragraphs. For example, the sensing unit can record the time a reader spends on a particular page and analyze the reading pace based on that data. This allows for an accurate understanding of the reader's reading pace by sensing the speed at which pages are turned and the time spent on each page. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input data on the speed at which pages are turned and the time spent on each page into a generating AI, and have the generating AI perform the reading pace analysis.

[0035] The suggestion unit can suggest bookmark locations based on the amount of time spent and the number of times the user returns to them. For example, the suggestion unit can suggest bookmark locations based on the amount of time the user spends there or more. It can also suggest bookmark locations based on the number of times the user returns to them or more. For example, the suggestion unit can identify locations that the user frequently returns to and suggest those locations as bookmark locations. This ensures that important sections are not missed by suggesting bookmark locations based on the amount of time spent there or the number of times the user returns to them. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on the amount of time spent and the number of times the user returns to a generating AI and have the generating AI perform the bookmark location suggestion.

[0036] The highlighting function can analyze text and automatically highlight keywords and phrases. For example, it can use natural language processing techniques to analyze text and extract important keywords and phrases. The highlighting function can also highlight based on the frequency and importance of keywords and phrases. For example, it can automatically highlight frequently occurring keywords and highly important phrases. This allows for easy later reference of important keywords and phrases. Some or all of the above processing in the highlighting function may be performed using AI, or not. For example, the highlighting function can input text data into a generating AI and have the generating AI perform keyword and phrase extraction and highlighting.

[0037] The display unit can record the playback position of an audiobook and visually display the pause position and highlighted sections. For example, the display unit can record the playback time and chapter of the audiobook and visually display the paused position and highlighted sections. The display unit can also record the time and page number at which playback stopped. For example, the display unit can record the time at which playback stopped and visually display that position. This allows users to easily identify important sections by recording the audiobook's playback position and visually displaying the paused position and highlighted sections. Some or all of the above processing in the display unit may be performed using AI, or not. For example, the display unit can input playback position and highlighted section data into a generating AI and have the generating AI perform the visual display.

[0038] The sensing unit may be equipped with a camera for tracking the reader's gaze and a sensor for measuring heart rate. For example, the sensing unit may use an infrared camera to track the reader's gaze and collect data on the movement of their eyes. The sensing unit may also use an optical sensor to measure the reader's heart rate and collect data on changes in heart rate. This allows for a more accurate understanding of the reader's reading pace and level of attention by sensing their gaze and heart rate. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit may input gaze tracking data and heart rate data into a generating AI and have the generating AI perform an analysis of the reading pace and level of attention.

[0039] The sensing unit can analyze the reader's reading history and optimize the sensing method based on past reading patterns. For example, if the reader has preferred speed reading in the past, the sensing unit will adopt a sensing method suitable for speed reading. Furthermore, if the reader has preferred a particular genre in the past, the sensing unit can adopt a sensing method suitable for that genre. Additionally, if the reader has read at night in the past, the sensing unit can adopt a sensing method suitable for nighttime reading. This allows the sensing unit to provide the reader with the most optimal sensing method by optimizing it based on past reading patterns. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input reading history data into a generating AI and have the generating AI perform the optimization of the sensing method.

[0040] The sensing unit can sense the reader's reading environment and adjust the reading pace sensing method based on the environment. For example, if the lighting is dim, the sensing unit can ease the reading pace sensing method to reduce eye strain. The sensing unit can also adjust the reading pace sensing method if the surroundings are noisy to maintain concentration. Furthermore, if the lighting is bright, the sensing unit can increase the reading pace sensing method to support efficient reading. In this way, by adjusting the sensing method according to the reading environment, the optimal reading pace can be sensed for the reader. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input reading environment data into a generating AI and have the generating AI perform the adjustment of the reading pace sensing method.

[0041] The sensing unit can analyze the reader's device usage history and improve the sensing method based on the device usage pattern. For example, if the reader has frequently used a smartphone in the past, the sensing unit will adopt a sensing method suitable for smartphones. Similarly, if the reader has frequently used a tablet in the past, the sensing unit can adopt a sensing method suitable for tablets. Furthermore, if the reader has frequently used a PC in the past, the sensing unit can adopt a sensing method suitable for PCs. This allows the sensing unit to provide the reader with the most suitable sensing method by optimizing the sensing method based on the device usage pattern. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input device usage history data into a generating AI and have the generating AI perform improvements to the sensing method.

[0042] The sensing unit can sense the reader's physical state and adjust the reading pace sensing method based on that state. For example, if the reader is tired, the sensing unit can ease the reading pace sensing method to reduce the reader's burden. Conversely, if the reader is relaxed, the sensing unit can increase the reading pace sensing method to support efficient reading. Furthermore, if the reader's posture is poor, the sensing unit can adjust the reading pace sensing method to encourage them to correct their posture. In this way, by adjusting the sensing method according to the reader's physical state, the optimal reading pace for the reader can be sensed. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input physical state data into a generating AI and have the generating AI perform the adjustment of the reading pace sensing method.

[0043] The suggestion unit can analyze the reader's reading history and improve its suggestion method based on past bookmark locations. For example, the suggestion unit can suggest the optimal bookmark locations based on the locations the reader has bookmarked in the past. It can also suggest frequently revisited sections as bookmark locations based on the reader's past reading history. Furthermore, the suggestion unit can analyze the reader's past reading history and suggest important sections as bookmark locations. By optimizing the suggestion method based on past bookmark locations, it can suggest the most suitable bookmark locations for the reader. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input reading history data into a generating AI and have the generating AI perform improvements to the suggestion method.

[0044] The suggestion unit can sense the reader's reading purpose and adjust the method of suggesting bookmark locations based on that purpose. For example, if the reader is reading for educational purposes, the suggestion unit will suggest important sections as bookmark locations. If the reader is reading for entertainment purposes, the suggestion unit can also suggest interesting sections as bookmark locations. Furthermore, if the reader is reading for research purposes, the suggestion unit can also suggest relevant sections as bookmark locations. By adjusting the method of suggesting bookmark locations according to the reading purpose, more appropriate bookmark locations can be suggested. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input reading purpose data into a generating AI and have the generating AI perform the adjustment of the bookmark location suggestion method.

[0045] The suggestion unit can analyze the reader's device usage history and improve the bookmark location suggestion method based on device usage patterns. For example, if the reader has frequently used a smartphone in the past, the suggestion unit can suggest bookmark locations suitable for a smartphone. Similarly, if the reader has frequently used a tablet in the past, the suggestion unit can suggest bookmark locations suitable for a tablet. Furthermore, if the reader has frequently used a PC in the past, the suggestion unit can suggest bookmark locations suitable for a PC. This optimizes the suggestion method based on device usage patterns, thereby suggesting the most suitable bookmark locations for the reader. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input device usage history data into a generating AI and have the generating AI perform improvements to the bookmark location suggestion method.

[0046] The suggestion unit can sense the reader's reading environment and adjust the method of suggesting bookmark locations based on that environment. For example, if the lighting is dim, the suggestion unit may suggest a suitable location as a bookmark to reduce eye strain. Furthermore, if the surroundings are noisy, the suggestion unit may suggest an important location as a bookmark to maintain concentration. Additionally, if the lighting is bright, the suggestion unit may suggest an optimal location as a bookmark to support efficient reading. By adjusting the suggestion method according to the reading environment, the optimal bookmark locations can be suggested to the reader. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input reading environment data into a generating AI and have the generating AI adjust the method of suggesting bookmark locations.

[0047] The highlighting function can analyze the reader's reading history and improve the highlighting method based on past highlighting patterns. For example, the highlighting function can suggest the optimal highlighting method based on sections the reader has highlighted in the past. It can also suggest how to highlight important sections based on the reader's past reading history. Furthermore, the highlighting function can analyze the reader's past reading history and suggest an efficient highlighting method. This allows the highlighting function to provide the reader with the best possible highlighting method by optimizing the highlighting method based on past highlighting patterns. Some or all of the above processing in the highlighting function may be performed using AI, for example, or without AI. For example, the highlighting function can input reading history data into a generating AI and have the generating AI perform improvements to the highlighting method.

[0048] The highlighting function can sense the reader's reading purpose and adjust the highlighting method based on that purpose. For example, if the reader is reading for educational purposes, the highlighting function can emphasize important keywords and phrases. If the reader is reading for entertainment purposes, the highlighting function can also emphasize interesting sections. Furthermore, if the reader is reading for research purposes, the highlighting function can emphasize relevant sections. By adjusting the highlighting method according to the reading purpose, more appropriate highlighting becomes possible. Some or all of the above processing in the highlighting function may be performed using AI, for example, or not. For example, the highlighting function can input reading purpose data into a generating AI and have the generating AI perform the adjustment of the highlighting method.

[0049] The highlighting function can analyze the reader's device usage history and improve the highlighting method based on the device usage pattern. For example, if the reader has frequently used a smartphone in the past, the highlighting function can suggest a highlighting method suitable for smartphones. Similarly, if the reader has frequently used a tablet in the past, the highlighting function can suggest a highlighting method suitable for tablets. Furthermore, if the reader has frequently used a PC in the past, the highlighting function can suggest a highlighting method suitable for PCs. This allows the highlighting function to provide the reader with the most suitable highlighting method by optimizing the highlighting method based on the device usage pattern. Some or all of the above processing in the highlighting function may be performed using AI, for example, or without AI. For example, the highlighting function can input device usage history data into a generating AI and have the generating AI perform improvements to the highlighting method.

[0050] The highlighting function can sense the reader's reading environment and adjust the highlighting method based on that environment. For example, if the lighting is dim, the highlighting function can use an appropriate color to reduce eye strain. It can also use a more emphasized color to maintain concentration if the surroundings are noisy. Furthermore, if the lighting is bright, the highlighting function can use the optimal color to support efficient reading. This allows for optimal highlighting for the reader by adjusting the highlighting method according to the reading environment. Some or all of the above processing in the highlighting function may be performed using AI, for example, or without AI. For example, the highlighting function can input reading environment data into a generating AI and have the generating AI perform the adjustment of the highlighting method.

[0051] The display unit can analyze the reader's reading history and improve the display method based on past display patterns. For example, the display unit can suggest the optimal display method based on the display method the reader has preferred in the past. The display unit can also suggest a display method that highlights important sections based on the reader's past reading history. Furthermore, the display unit can analyze the reader's past reading history and suggest an efficient display method. In this way, by optimizing the display method based on past display patterns, the display unit can provide the reader with the most suitable display method. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input reading history data into a generating AI and have the generating AI perform improvements to the display method.

[0052] The display unit can sense the reader's reading purpose and adjust the display method based on that purpose. For example, if the reader is reading for educational purposes, the display unit may adopt a display method that highlights important keywords and phrases. If the reader is reading for entertainment purposes, the display unit may also adopt a display method that highlights interesting sections. Furthermore, if the reader is reading for research purposes, the display unit may also adopt a display method that highlights relevant sections. By adjusting the display method according to the reading purpose, a more appropriate display becomes possible. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input reading purpose data into a generating AI and have the generating AI perform the adjustment of the display method.

[0053] The display unit can analyze the reader's device usage history and improve the display method based on the device usage pattern. For example, if the reader has frequently used a smartphone in the past, the display unit can suggest a display method suitable for a smartphone. Similarly, if the reader has frequently used a tablet in the past, the display unit can suggest a display method suitable for a tablet. Furthermore, if the reader has frequently used a PC in the past, the display unit can suggest a display method suitable for a PC. This allows the display unit to provide the reader with the most suitable display method by optimizing the display method based on the device usage pattern. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input device usage history data into a generating AI and have the generating AI perform improvements to the display method.

[0054] The display unit can sense the reader's reading environment and adjust the display method based on that environment. For example, if the lighting is dim, the display unit can adopt an appropriate display method to reduce eye strain. Furthermore, if the surroundings are noisy, the display unit can adopt an emphasized display method to maintain concentration. In addition, if the lighting is bright, the display unit can adopt an optimal display method to support efficient reading. This allows for optimal display for the reader by adjusting the display method according to the reading environment. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input reading environment data into a generating AI and have the generating AI perform the adjustment of the display method.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The bookmark support system can also analyze the reader's reading history and suggest bookmark locations based on past reading patterns. For example, if a reader has previously enjoyed reading books of a particular genre, it can suggest important passages related to that genre as bookmark locations. Similarly, if a reader has frequently read books by a particular author, it can suggest important passages in that author's works as bookmark locations. Furthermore, if a reader has previously been interested in a particular theme, it can suggest passages related to that theme as bookmark locations. This allows for more personalized bookmark suggestions based on the reader's past reading history.

[0057] The bookmark support system can also sense the reader's reading environment and suggest bookmark locations based on that environment. For example, if the lighting is dim while reading, it can suggest a suitable location as a bookmark to reduce eye strain. If the surroundings are noisy, it can suggest important locations as bookmark locations to maintain concentration. Furthermore, if the lighting is bright, it can suggest the optimal location as a bookmark to support efficient reading. In this way, by suggesting bookmark locations according to the reading environment, it can provide readers with the best possible reading experience.

[0058] The bookmark support system can also analyze the reader's device usage history and suggest bookmark locations based on their device usage patterns. For example, if a reader has frequently used a smartphone in the past, it can suggest bookmark locations suitable for a smartphone. Similarly, if a reader has frequently used a tablet in the past, it can suggest bookmark locations suitable for a tablet. Furthermore, if a reader has frequently used a PC in the past, it can suggest bookmark locations suitable for a PC. By suggesting bookmark locations based on device usage patterns, the system can provide readers with the most optimal bookmark locations.

[0059] The bookmark support system can also sense the reader's reading purpose and suggest bookmark locations based on that purpose. For example, if a reader is reading for educational purposes, it can suggest important sections as bookmark locations. If a reader is reading for entertainment purposes, it can suggest interesting sections as bookmark locations. Furthermore, if a reader is reading for research purposes, it can suggest relevant sections as bookmark locations. In this way, by suggesting bookmark locations according to the reader's purpose, it can provide more appropriate bookmark locations.

[0060] The bookmark support system can also sense the reader's physical state and suggest bookmark locations based on that state. For example, if the reader is tired, it can suggest a suitable bookmark location to reduce the reader's burden. If the reader is relaxed, it can suggest an optimal bookmark location to support efficient reading. Furthermore, if the reader has poor posture, it can suggest an appropriate bookmark location to encourage them to correct their posture. In this way, by suggesting bookmark locations according to the reader's physical state, it can provide the reader with an optimal reading experience.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The sensing unit senses the reader's reading pace and level of attention. For example, it senses the speed at which the reader turns pages and the time spent at specific points. The sensing unit may be equipped with a camera to track the reader's gaze and a sensor to measure heart rate. For example, it may use an infrared camera to track gaze and an optical sensor to measure heart rate. Step 2: The suggestion unit automatically suggests bookmark locations based on the information sensed by the sensing unit. For example, it suggests bookmark locations where the reader has spent a long time or frequently returned to. The suggestion unit can also estimate the reader's emotions and adjust the bookmark location suggestion method based on the estimated emotions. Step 3: The highlighting section analyzes the text and automatically highlights important keywords and phrases. For example, it uses natural language processing technology to analyze the text and extract important keywords and phrases. The highlighting section can also estimate the reader's emotions and adjust the way the highlighting is presented based on those emotions. Step 4: The display unit records the playback position of the audiobook and visually displays paused and highlighted sections. For example, it records the playback time and chapters of the audiobook and visually displays paused and highlighted sections. The display unit can also estimate the reader's emotions and adjust the display method based on the estimated emotions.

[0063] (Example of form 2) The bookmark support system according to an embodiment of the present invention is a system that senses the reader's reading pace and level of attention and automatically suggests bookmark locations. This bookmark support system senses the reader's reading pace and level of attention, and the AI ​​automatically suggests bookmark locations based on the sensed information. It also has a function to highlight important keywords and phrases so that they can be easily referenced later. Furthermore, in the case of audiobooks, it also has a function to visually display pause locations and highlighted sections. For example, the bookmark support system senses the speed at which the reader turns pages and the time spent at specific locations. This information is input to the AI. Next, the AI ​​automatically suggests bookmark locations based on the sensed information. For example, it suggests locations where the reader has spent a long time or frequently returns to as bookmark locations. This allows the reader to read efficiently without missing important parts. Furthermore, it also has a function to highlight important keywords and phrases so that they can be easily referenced later. For example, the AI ​​analyzes the text and automatically highlights important keywords and phrases. This allows the reader to easily find important information later. In the case of audiobooks, it also has a function to visually display pause locations and highlighted sections. For example, AI can record the playback position of an audiobook and visually display paused or highlighted sections. This allows readers to easily identify important parts of the audiobook. This mechanism enables readers to read efficiently, avoid missing important information, and easily refer back to it later. Furthermore, the ability to visually identify important sections in audiobooks enhances the reading experience. Thus, a bookmark support system can sense the reader's reading pace and attention span and automatically suggest bookmark locations to support efficient reading.

[0064] The bookmark support system according to this embodiment comprises a sensing unit, a suggestion unit, a highlighting unit, and a display unit. The sensing unit senses the reader's reading pace and level of attention. For example, the sensing unit senses the speed at which the reader turns pages and the time spent at specific locations. The sensing unit may also be equipped with a camera for tracking the reader's gaze and a sensor for measuring heart rate. For example, the sensing unit uses an infrared camera to track the reader's gaze and collects data on the movement of their eyes. The sensing unit can also use an optical sensor to measure the reader's heart rate and collect data on changes in heart rate. The suggestion unit automatically suggests bookmark locations based on the information sensed by the sensing unit. For example, the suggestion unit suggests locations where the reader has spent a long time or frequently returns as bookmark locations. The suggestion unit can also estimate the reader's emotions and adjust the method of suggesting bookmark locations based on the estimated emotions. For example, the suggestion unit may suggest bookmark locations more frequently if the reader is excited and less frequently if the reader is relaxed. The highlighting section analyzes the text and automatically highlights important keywords and phrases. For example, it might use natural language processing techniques to analyze the text and extract important keywords and phrases. The highlighting section can also estimate the reader's emotions and adjust the highlighting method based on that estimation. For instance, if the reader is excited, it might use emphasized colors and effects; if the reader is relaxed, it might use calming colors and effects. The display section records the audiobook's playback position and visually displays paused and highlighted sections. For example, it might record the audiobook's playback time and chapters and visually display paused and highlighted sections. The display section can also estimate the reader's emotions and adjust the display method based on that estimation. For instance, if the reader is excited, it might use a visually stimulating display method; if the reader is relaxed, it might use a calming display method.As a result, the bookmark support system according to this embodiment can sense the reader's reading pace and level of attention, and automatically suggest bookmark locations, thereby supporting efficient reading.

[0065] The sensing unit detects the reader's reading pace and level of attention. For example, it can sense the speed at which the reader turns pages and the time spent at specific points in the text. Specifically, the sensing unit measures the page-turning speed in milliseconds and meticulously records how much time the reader spends on each page. Furthermore, the sensing unit can be equipped with a camera to track the reader's gaze and a sensor to measure heart rate. For example, the sensing unit can use an infrared camera to track the reader's gaze and collect data on eye movements. Eye-tracking technology is used to accurately understand which parts the reader is focusing on, and the duration of eye contact and movement patterns can be analyzed. The sensing unit can also use an optical sensor to measure the reader's heart rate and collect data on changes in heart rate. Changes in heart rate are used as an indicator of the reader's level of excitement or relaxation, allowing for real-time understanding of their emotional state while reading. In this way, the sensing unit can comprehensively sense the reader's reading behavior and physiological responses and provide detailed data. Furthermore, the sensing unit can transmit this data to a cloud server and share information in real time in conjunction with other system components. This allows the sensing unit to gain a deeper understanding of the reader's reading experience and build a foundation for providing support tailored to individual needs.

[0066] The suggestion unit automatically proposes bookmark locations based on information sensed by the sensing unit. For example, the suggestion unit suggests bookmark locations such as sections where the reader has spent a long time or frequently revisited. Specifically, the suggestion unit analyzes data provided by the sensing unit to identify sections that the reader paid particular attention to or reread multiple times. This allows it to automatically suggest sections that the reader found important as bookmarks. Furthermore, the suggestion unit can estimate the reader's emotions and adjust the bookmark suggestion method based on the estimated emotions. For example, if the reader is excited, the suggestion unit will suggest bookmark locations more frequently, and if the reader is relaxed, it will suggest bookmark locations less frequently. Heart rate data and eye-tracking data provided by the sensing unit can be used for emotion estimation. This allows the suggestion unit to provide flexible bookmark suggestions according to the reader's emotional state. In addition, the suggestion unit can learn past reading history and the reader's preferences and incorporate algorithms to suggest optimal bookmark locations for individual readers. This allows the suggestion unit to improve the reader's reading experience and support efficient reading.

[0067] The highlighting function analyzes text and automatically highlights important keywords and phrases. For example, it uses natural language processing techniques to analyze text and extract key keywords and phrases. Specifically, it uses topic modeling and keyword extraction algorithms to understand the context of the text and identify keywords and phrases containing important information. This allows readers to read efficiently without missing important information. Furthermore, the highlighting function can estimate the reader's emotions and adjust the highlighting style based on the estimated emotions. For example, if the reader is excited, it uses emphasized colors and effects for highlighting; if the reader is relaxed, it uses calming colors and effects. Emotion estimation can utilize heart rate and eye-tracking data provided by the sensing unit. This allows the highlighting function to provide flexible highlighting expressions that respond to the reader's emotional state. Additionally, the highlighting function can collect reader feedback and continuously improve the accuracy and style of highlighting. This enhances the reader's reading experience and effectively conveys important information.

[0068] The display unit records the playback position of the audiobook and visually displays paused and highlighted sections. Specifically, it records the audiobook's playback time and chapters, visually displaying paused and highlighted sections. More precisely, it records the audiobook's playback time in seconds and identifies the currently playing chapter or section. This allows the reader to easily resume playback from where it left off. Furthermore, the display unit can estimate the reader's emotions and adjust the display method based on that estimation. For example, it might use a visually stimulating display method if the reader is excited, and a calming display method if the reader is relaxed. Heart rate data and eye-tracking data provided by the sensing unit can be used for emotion estimation. This allows the display unit to provide a flexible display method tailored to the reader's emotional state. Additionally, the display unit can collect reader feedback and continuously improve the accuracy and effectiveness of the display method. This allows the display unit to enhance the reader's reading experience and effectively manage the audiobook's playback position and highlighted sections.

[0069] The sensing unit can sense the speed at which pages are turned and the time spent on each page. For example, the sensing unit can measure the speed at which pages are turned in seconds and count the number of pages. The sensing unit can also measure the time spent on specific pages or paragraphs. For example, the sensing unit can record the time a reader spends on a particular page and analyze the reading pace based on that data. This allows for an accurate understanding of the reader's reading pace by sensing the speed at which pages are turned and the time spent on each page. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input data on the speed at which pages are turned and the time spent on each page into a generating AI, and have the generating AI perform the reading pace analysis.

[0070] The suggestion unit can suggest bookmark locations based on the amount of time spent and the number of times the user returns to them. For example, the suggestion unit can suggest bookmark locations based on the amount of time the user spends there or more. It can also suggest bookmark locations based on the number of times the user returns to them or more. For example, the suggestion unit can identify locations that the user frequently returns to and suggest those locations as bookmark locations. This ensures that important sections are not missed by suggesting bookmark locations based on the amount of time spent there or the number of times the user returns to them. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on the amount of time spent and the number of times the user returns to a generating AI and have the generating AI perform the bookmark location suggestion.

[0071] The highlighting function can analyze text and automatically highlight keywords and phrases. For example, it can use natural language processing techniques to analyze text and extract important keywords and phrases. The highlighting function can also highlight based on the frequency and importance of keywords and phrases. For example, it can automatically highlight frequently occurring keywords and highly important phrases. This allows for easy later reference of important keywords and phrases. Some or all of the above processing in the highlighting function may be performed using AI, or not. For example, the highlighting function can input text data into a generating AI and have the generating AI perform keyword and phrase extraction and highlighting.

[0072] The display unit can record the playback position of an audiobook and visually display the pause position and highlighted sections. For example, the display unit can record the playback time and chapter of the audiobook and visually display the paused position and highlighted sections. The display unit can also record the time and page number at which playback stopped. For example, the display unit can record the time at which playback stopped and visually display that position. This allows users to easily identify important sections by recording the audiobook's playback position and visually displaying the paused position and highlighted sections. Some or all of the above processing in the display unit may be performed using AI, or not. For example, the display unit can input playback position and highlighted section data into a generating AI and have the generating AI perform the visual display.

[0073] The sensing unit may be equipped with a camera for tracking the reader's gaze and a sensor for measuring heart rate. For example, the sensing unit may use an infrared camera to track the reader's gaze and collect data on the movement of their eyes. The sensing unit may also use an optical sensor to measure the reader's heart rate and collect data on changes in heart rate. This allows for a more accurate understanding of the reader's reading pace and level of attention by sensing their gaze and heart rate. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit may input gaze tracking data and heart rate data into a generating AI and have the generating AI perform an analysis of the reading pace and level of attention.

[0074] The sensing unit can estimate the reader's emotions and adjust the accuracy of the reading pace based on the estimated emotions. For example, if the reader is excited, the sensing unit can increase the accuracy of reading pace detection to capture subtle changes. Conversely, if the reader is relaxed, the sensing unit can also loosen the accuracy of reading pace detection and prioritize the overall pace. Furthermore, if the reader is tired, the sensing unit can set the accuracy of reading pace detection to a moderate level to reduce the reader's burden. This allows for a more appropriate reading pace detection by adjusting the accuracy of reading pace detection according to the reader's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sensing unit may be performed using AI, or not. For example, the sensing unit can input the reader's emotion data into the generative AI and have the generative AI adjust the accuracy of reading pace detection.

[0075] The sensing unit can analyze the reader's reading history and optimize the sensing method based on past reading patterns. For example, if the reader has preferred speed reading in the past, the sensing unit will adopt a sensing method suitable for speed reading. Furthermore, if the reader has preferred a particular genre in the past, the sensing unit can adopt a sensing method suitable for that genre. Additionally, if the reader has read at night in the past, the sensing unit can adopt a sensing method suitable for nighttime reading. This allows the sensing unit to provide the reader with the most optimal sensing method by optimizing it based on past reading patterns. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input reading history data into a generating AI and have the generating AI perform the optimization of the sensing method.

[0076] The sensing unit can sense the reader's reading environment and adjust the reading pace sensing method based on the environment. For example, if the lighting is dim, the sensing unit can ease the reading pace sensing method to reduce eye strain. The sensing unit can also adjust the reading pace sensing method if the surroundings are noisy to maintain concentration. Furthermore, if the lighting is bright, the sensing unit can increase the reading pace sensing method to support efficient reading. In this way, by adjusting the sensing method according to the reading environment, the optimal reading pace can be sensed for the reader. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input reading environment data into a generating AI and have the generating AI perform the adjustment of the reading pace sensing method.

[0077] The sensing unit can estimate the reader's emotions and adjust the accuracy of attention detection based on the estimated emotions. For example, if the reader is excited, the sensing unit can increase the accuracy of attention detection to capture subtle changes. Conversely, if the reader is relaxed, the sensing unit can also loosen the accuracy of attention detection and prioritize overall attention. Furthermore, if the reader is tired, the sensing unit can set the accuracy of attention detection to a moderate level to reduce the reader's burden. This allows for more appropriate attention detection by adjusting the accuracy of attention detection according to the reader's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the sensing unit may be performed using AI, or not. For example, the sensing unit can input the reader's emotion data into the generative AI and have the generative AI adjust the accuracy of attention detection.

[0078] The sensing unit can analyze the reader's device usage history and improve the sensing method based on the device usage pattern. For example, if the reader has frequently used a smartphone in the past, the sensing unit will adopt a sensing method suitable for smartphones. Similarly, if the reader has frequently used a tablet in the past, the sensing unit can adopt a sensing method suitable for tablets. Furthermore, if the reader has frequently used a PC in the past, the sensing unit can adopt a sensing method suitable for PCs. This allows the sensing unit to provide the reader with the most suitable sensing method by optimizing the sensing method based on the device usage pattern. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input device usage history data into a generating AI and have the generating AI perform improvements to the sensing method.

[0079] The sensing unit can sense the reader's physical state and adjust the reading pace sensing method based on that state. For example, if the reader is tired, the sensing unit can ease the reading pace sensing method to reduce the reader's burden. Conversely, if the reader is relaxed, the sensing unit can increase the reading pace sensing method to support efficient reading. Furthermore, if the reader's posture is poor, the sensing unit can adjust the reading pace sensing method to encourage them to correct their posture. In this way, by adjusting the sensing method according to the reader's physical state, the optimal reading pace for the reader can be sensed. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input physical state data into a generating AI and have the generating AI perform the adjustment of the reading pace sensing method.

[0080] The suggestion unit can estimate the reader's emotions and adjust the method of suggesting bookmark locations based on the estimated emotions. For example, if the reader is excited, the suggestion unit may suggest bookmark locations more frequently. Conversely, if the reader is relaxed, the suggestion unit may suggest bookmark locations less frequently. Furthermore, if the reader is tired, the suggestion unit may suggest bookmark locations moderately to reduce the reader's burden. In this way, by adjusting the method of suggesting bookmark locations according to the reader's emotions, more appropriate bookmark locations can be suggested. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the reader's emotion data into a generative AI and have the generative AI adjust the method of suggesting bookmark locations.

[0081] The suggestion unit can analyze the reader's reading history and improve its suggestion method based on past bookmark locations. For example, the suggestion unit can suggest the optimal bookmark locations based on the locations the reader has bookmarked in the past. It can also suggest frequently revisited sections as bookmark locations based on the reader's past reading history. Furthermore, the suggestion unit can analyze the reader's past reading history and suggest important sections as bookmark locations. By optimizing the suggestion method based on past bookmark locations, it can suggest the most suitable bookmark locations for the reader. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input reading history data into a generating AI and have the generating AI perform improvements to the suggestion method.

[0082] The suggestion unit can sense the reader's reading purpose and adjust the method of suggesting bookmark locations based on that purpose. For example, if the reader is reading for educational purposes, the suggestion unit will suggest important sections as bookmark locations. If the reader is reading for entertainment purposes, the suggestion unit can also suggest interesting sections as bookmark locations. Furthermore, if the reader is reading for research purposes, the suggestion unit can also suggest relevant sections as bookmark locations. By adjusting the method of suggesting bookmark locations according to the reading purpose, more appropriate bookmark locations can be suggested. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input reading purpose data into a generating AI and have the generating AI perform the adjustment of the bookmark location suggestion method.

[0083] The suggestion function can estimate the reader's emotions and determine the priority of bookmark locations based on the estimated emotions. For example, if the reader is excited, the suggestion function may prioritize suggesting important sections as bookmark locations. If the reader is relaxed, the suggestion function may also suggest bookmark locations that emphasize the overall flow. Furthermore, if the reader is tired, the suggestion function may suggest moderate sections as bookmark locations to reduce the reader's burden. This allows for prioritizing bookmark locations according to the reader's emotions, thereby prioritizing bookmarks of more important sections. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion function may be performed using AI or not. For example, the suggestion function can input the reader's emotion data into a generative AI and have the generative AI determine the priority of bookmark locations.

[0084] The suggestion unit can analyze the reader's device usage history and improve the bookmark location suggestion method based on device usage patterns. For example, if the reader has frequently used a smartphone in the past, the suggestion unit can suggest bookmark locations suitable for a smartphone. Similarly, if the reader has frequently used a tablet in the past, the suggestion unit can suggest bookmark locations suitable for a tablet. Furthermore, if the reader has frequently used a PC in the past, the suggestion unit can suggest bookmark locations suitable for a PC. This optimizes the suggestion method based on device usage patterns, thereby suggesting the most suitable bookmark locations for the reader. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input device usage history data into a generating AI and have the generating AI perform improvements to the bookmark location suggestion method.

[0085] The suggestion unit can sense the reader's reading environment and adjust the method of suggesting bookmark locations based on that environment. For example, if the lighting is dim, the suggestion unit may suggest a suitable location as a bookmark to reduce eye strain. Furthermore, if the surroundings are noisy, the suggestion unit may suggest an important location as a bookmark to maintain concentration. Additionally, if the lighting is bright, the suggestion unit may suggest an optimal location as a bookmark to support efficient reading. By adjusting the suggestion method according to the reading environment, the optimal bookmark locations can be suggested to the reader. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input reading environment data into a generating AI and have the generating AI adjust the method of suggesting bookmark locations.

[0086] The highlighting section can estimate the reader's emotions and adjust the way it highlights based on those emotions. For example, if the reader is excited, the highlighting section may use emphasized colors or effects. If the reader is relaxed, it may use calming colors or effects. Furthermore, if the reader is tired, it may use highly visible colors or effects. This allows for more appropriate highlighting by adjusting the way it highlights according to the reader's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the highlighting section may be performed using AI or not. For example, the highlighting section can input reader emotion data into a generative AI and have the generative AI adjust the way it highlights.

[0087] The highlighting function can analyze the reader's reading history and improve the highlighting method based on past highlighting patterns. For example, the highlighting function can suggest the optimal highlighting method based on sections the reader has highlighted in the past. It can also suggest how to highlight important sections based on the reader's past reading history. Furthermore, the highlighting function can analyze the reader's past reading history and suggest an efficient highlighting method. This allows the highlighting function to provide the reader with the best possible highlighting method by optimizing the highlighting method based on past highlighting patterns. Some or all of the above processing in the highlighting function may be performed using AI, for example, or without AI. For example, the highlighting function can input reading history data into a generating AI and have the generating AI perform improvements to the highlighting method.

[0088] The highlighting function can sense the reader's reading purpose and adjust the highlighting method based on that purpose. For example, if the reader is reading for educational purposes, the highlighting function can emphasize important keywords and phrases. If the reader is reading for entertainment purposes, the highlighting function can also emphasize interesting sections. Furthermore, if the reader is reading for research purposes, the highlighting function can emphasize relevant sections. By adjusting the highlighting method according to the reading purpose, more appropriate highlighting becomes possible. Some or all of the above processing in the highlighting function may be performed using AI, for example, or not. For example, the highlighting function can input reading purpose data into a generating AI and have the generating AI perform the adjustment of the highlighting method.

[0089] The highlighting section can estimate the reader's emotions and determine the priority of highlights based on those emotions. For example, if the reader is excited, the highlighting section will prioritize highlighting important parts. If the reader is relaxed, the highlighting section may also prioritize highlighting the overall flow. Furthermore, if the reader is tired, the highlighting section may highlight moderate parts to reduce the reader's burden. This allows for prioritizing highlights according to the reader's emotions, thereby highlighting more important parts. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the highlighting section may be performed using AI or not. For example, the highlighting section can input reader emotion data into a generative AI and have the generative AI determine the priority of highlights.

[0090] The highlighting function can analyze the reader's device usage history and improve the highlighting method based on the device usage pattern. For example, if the reader has frequently used a smartphone in the past, the highlighting function can suggest a highlighting method suitable for smartphones. Similarly, if the reader has frequently used a tablet in the past, the highlighting function can suggest a highlighting method suitable for tablets. Furthermore, if the reader has frequently used a PC in the past, the highlighting function can suggest a highlighting method suitable for PCs. This allows the highlighting function to provide the reader with the most suitable highlighting method by optimizing the highlighting method based on the device usage pattern. Some or all of the above processing in the highlighting function may be performed using AI, for example, or without AI. For example, the highlighting function can input device usage history data into a generating AI and have the generating AI perform improvements to the highlighting method.

[0091] The highlighting function can sense the reader's reading environment and adjust the highlighting method based on that environment. For example, if the lighting is dim, the highlighting function can use an appropriate color to reduce eye strain. It can also use a more emphasized color to maintain concentration if the surroundings are noisy. Furthermore, if the lighting is bright, the highlighting function can use the optimal color to support efficient reading. This allows for optimal highlighting for the reader by adjusting the highlighting method according to the reading environment. Some or all of the above processing in the highlighting function may be performed using AI, for example, or without AI. For example, the highlighting function can input reading environment data into a generating AI and have the generating AI perform the adjustment of the highlighting method.

[0092] The display unit can estimate the reader's emotions and adjust the display method based on the estimated emotions. For example, if the reader is excited, the display unit may adopt a visually stimulating display method. If the reader is relaxed, the display unit may adopt a calming display method. Furthermore, if the reader is tired, the display unit may adopt a highly visible display method. By adjusting the display method according to the reader's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the reader's emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0093] The display unit can analyze the reader's reading history and improve the display method based on past display patterns. For example, the display unit can suggest the optimal display method based on the display method the reader has preferred in the past. The display unit can also suggest a display method that highlights important sections based on the reader's past reading history. Furthermore, the display unit can analyze the reader's past reading history and suggest an efficient display method. In this way, by optimizing the display method based on past display patterns, the display unit can provide the reader with the most suitable display method. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input reading history data into a generating AI and have the generating AI perform improvements to the display method.

[0094] The display unit can sense the reader's reading purpose and adjust the display method based on that purpose. For example, if the reader is reading for educational purposes, the display unit may adopt a display method that highlights important keywords and phrases. If the reader is reading for entertainment purposes, the display unit may also adopt a display method that highlights interesting sections. Furthermore, if the reader is reading for research purposes, the display unit may also adopt a display method that highlights relevant sections. By adjusting the display method according to the reading purpose, a more appropriate display becomes possible. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input reading purpose data into a generating AI and have the generating AI perform the adjustment of the display method.

[0095] The display unit can estimate the reader's emotions and determine the priority of the displayed content based on the estimated emotions. For example, if the reader is excited, the display unit may prioritize displaying important sections. If the reader is relaxed, the display unit may also prioritize displaying the overall flow. Furthermore, if the reader is tired, the display unit may prioritize displaying sections of moderate importance to reduce the reader's burden. In this way, by determining the priority of the displayed content according to the reader's emotions, more important sections can be displayed preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, or not using AI. For example, the display unit can input the reader's emotion data into the generative AI and have the generative AI determine the priority of the displayed content.

[0096] The display unit can analyze the reader's device usage history and improve the display method based on the device usage pattern. For example, if the reader has frequently used a smartphone in the past, the display unit can suggest a display method suitable for a smartphone. Similarly, if the reader has frequently used a tablet in the past, the display unit can suggest a display method suitable for a tablet. Furthermore, if the reader has frequently used a PC in the past, the display unit can suggest a display method suitable for a PC. This allows the display unit to provide the reader with the most suitable display method by optimizing the display method based on the device usage pattern. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input device usage history data into a generating AI and have the generating AI perform improvements to the display method.

[0097] The display unit can sense the reader's reading environment and adjust the display method based on that environment. For example, if the lighting is dim, the display unit can adopt an appropriate display method to reduce eye strain. Furthermore, if the surroundings are noisy, the display unit can adopt an emphasized display method to maintain concentration. In addition, if the lighting is bright, the display unit can adopt an optimal display method to support efficient reading. This allows for optimal display for the reader by adjusting the display method according to the reading environment. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input reading environment data into a generating AI and have the generating AI perform the adjustment of the display method.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The bookmark support system can also analyze the reader's reading history and suggest bookmark locations based on past reading patterns. For example, if a reader has previously enjoyed reading books of a particular genre, it can suggest important passages related to that genre as bookmark locations. Similarly, if a reader has frequently read books by a particular author, it can suggest important passages in that author's works as bookmark locations. Furthermore, if a reader has previously been interested in a particular theme, it can suggest passages related to that theme as bookmark locations. This allows for more personalized bookmark suggestions based on the reader's past reading history.

[0100] The bookmark support system can also sense the reader's reading environment and suggest bookmark locations based on that environment. For example, if the lighting is dim while reading, it can suggest a suitable location as a bookmark to reduce eye strain. If the surroundings are noisy, it can suggest important locations as bookmark locations to maintain concentration. Furthermore, if the lighting is bright, it can suggest the optimal location as a bookmark to support efficient reading. In this way, by suggesting bookmark locations according to the reading environment, it can provide readers with the best possible reading experience.

[0101] The bookmark support system can also analyze the reader's device usage history and suggest bookmark locations based on their device usage patterns. For example, if a reader has frequently used a smartphone in the past, it can suggest bookmark locations suitable for a smartphone. Similarly, if a reader has frequently used a tablet in the past, it can suggest bookmark locations suitable for a tablet. Furthermore, if a reader has frequently used a PC in the past, it can suggest bookmark locations suitable for a PC. By suggesting bookmark locations based on device usage patterns, the system can provide readers with the most optimal bookmark locations.

[0102] The bookmark support system can also sense the reader's reading purpose and suggest bookmark locations based on that purpose. For example, if a reader is reading for educational purposes, it can suggest important sections as bookmark locations. If a reader is reading for entertainment purposes, it can suggest interesting sections as bookmark locations. Furthermore, if a reader is reading for research purposes, it can suggest relevant sections as bookmark locations. In this way, by suggesting bookmark locations according to the reader's purpose, it can provide more appropriate bookmark locations.

[0103] The bookmark support system can also sense the reader's physical state and suggest bookmark locations based on that state. For example, if the reader is tired, it can suggest a suitable bookmark location to reduce the reader's burden. If the reader is relaxed, it can suggest an optimal bookmark location to support efficient reading. Furthermore, if the reader has poor posture, it can suggest an appropriate bookmark location to encourage them to correct their posture. In this way, by suggesting bookmark locations according to the reader's physical state, it can provide the reader with an optimal reading experience.

[0104] The bookmark support system can further estimate the reader's emotions and adjust how bookmark locations are suggested based on those emotions. For example, if the reader is excited, bookmark locations may be suggested more frequently. Conversely, if the reader is relaxed, bookmark locations may be suggested less frequently. Furthermore, if the reader is tired, bookmark locations may be suggested moderately to reduce the reader's burden. In this way, by adjusting how bookmark locations are suggested according to the reader's emotions, more appropriate bookmark locations can be suggested.

[0105] The bookmark support system can further estimate the reader's emotions and prioritize bookmark locations based on those emotions. For example, if the reader is excited, it will prioritize suggesting important sections as bookmark locations. If the reader is relaxed, it can suggest bookmark locations that emphasize the overall flow. Furthermore, if the reader is tired, it can suggest bookmark locations that are at a moderate level to reduce the reader's burden. In this way, by prioritizing bookmark locations according to the reader's emotions, it is possible to bookmark more important sections first.

[0106] The bookmark support system can further estimate the reader's emotions and adjust the way highlights are displayed based on those emotions. For example, if the reader is excited, it can use emphasized colors and effects for highlighting. If the reader is relaxed, it can use calming colors and effects for highlighting. Furthermore, if the reader is tired, it can use highly visible colors and effects for highlighting. This allows for more appropriate highlighting by adjusting the way highlights are displayed according to the reader's emotions.

[0107] The bookmark support system can further estimate the reader's emotions and adjust the display method based on those emotions. For example, if the reader is excited, a visually stimulating display method can be adopted. If the reader is relaxed, a calming display method can be adopted. Furthermore, if the reader is tired, a highly visible display method can be adopted. In this way, by adjusting the display method according to the reader's emotions, a more appropriate display becomes possible.

[0108] The bookmark support system can further estimate the reader's emotions and prioritize the displayed content based on those emotions. For example, if the reader is excited, it will prioritize displaying important sections. If the reader is relaxed, it can prioritize displaying the overall flow. Furthermore, if the reader is tired, it can prioritize displaying sections of moderate importance to reduce the reader's burden. In this way, by prioritizing the displayed content according to the reader's emotions, it can prioritize displaying more important sections.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The sensing unit senses the reader's reading pace and level of attention. For example, it senses the speed at which the reader turns pages and the time spent at specific points. The sensing unit may be equipped with a camera to track the reader's gaze and a sensor to measure heart rate. For example, it may use an infrared camera to track gaze and an optical sensor to measure heart rate. Step 2: The suggestion unit automatically suggests bookmark locations based on the information sensed by the sensing unit. For example, it suggests bookmark locations where the reader has spent a long time or frequently returned to. The suggestion unit can also estimate the reader's emotions and adjust the bookmark location suggestion method based on the estimated emotions. Step 3: The highlighting section analyzes the text and automatically highlights important keywords and phrases. For example, it uses natural language processing technology to analyze the text and extract important keywords and phrases. The highlighting section can also estimate the reader's emotions and adjust the way the highlighting is presented based on those emotions. Step 4: The display unit records the playback position of the audiobook and visually displays paused and highlighted sections. For example, it records the playback time and chapters of the audiobook and visually displays paused and highlighted sections. The display unit can also estimate the reader's emotions and adjust the display method based on the estimated emotions.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] For example, the sensing unit can sense the reader's reading pace and level of attention using the camera 42 and sensors of the smart device 14. The suggestion unit is implemented by the identification processing unit 290 of the data processing device 12 and automatically suggests bookmark locations based on information from the sensing unit. The highlighting unit is implemented by the control unit 46A of the smart device 14 and analyzes the text to highlight important keywords and phrases. The display unit uses the display 40A of the smart device 14 to visually display the playback position and highlighted parts of the audiobook. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] For example, the sensing unit can sense the reader's reading pace and level of attention using the camera 42 and sensors of the smart glasses 214. The suggestion unit is implemented by the identification processing unit 290 of the data processing device 12 and automatically suggests bookmark locations based on information from the sensing unit. The highlighting unit is implemented by the control unit 46A of the smart glasses 214 and analyzes the text to highlight important keywords and phrases. The display unit visually displays the playback position and highlighted parts of the audiobook using the display of the smart glasses 214. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] For example, the sensing unit can sense the reader's reading pace and level of attention using the camera 42 and sensors of the headset terminal 314. The suggestion unit is implemented by the identification processing unit 290 of the data processing device 12 and automatically suggests bookmark locations based on information from the sensing unit. The highlighting unit is implemented by the control unit 46A of the headset terminal 314 and analyzes the text to highlight important keywords and phrases. The display unit uses the display 343 of the headset terminal 314 to visually display the playback position and highlighted parts of the audiobook. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] For example, the sensing unit can sense the reader's reading pace and level of attention using the camera 42 and sensors of the robot 414. The suggestion unit is implemented by the identification processing unit 290 of the data processing device 12 and automatically suggests bookmark locations based on information from the sensing unit. The highlighting unit is implemented by the control unit 46A of the robot 414 and analyzes the text to highlight important keywords and phrases. The display unit uses the display of the robot 414 to visually display the playback position and highlighted parts of the audiobook. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) A system characterized by comprising: a sensing unit that senses the reader's reading pace and level of attention; a suggestion unit that automatically suggests bookmark locations based on the information sensed by the sensing unit; a highlighting unit that highlights keywords and phrases; and a display unit that visually displays the paused positions and highlighted portions of the audiobook. (Note 2) The system according to Appendix 1, characterized in that the sensing unit senses the speed at which pages are turned and the time spent on the page. (Note 3) The system according to Appendix 1, characterized in that the suggestion unit suggests locations where the user spends a long time and locations where the user frequently returns as bookmark locations. (Note 4) The system described in Appendix 1 is characterized in that the highlighting section analyzes text and automatically highlights keywords and phrases. (Note 5) The system according to Appendix 1, characterized in that the display unit records the playback position of the audiobook and visually displays the stop position and highlighted portion. (Note 6) The system according to Appendix 1, characterized in that the sensing unit includes a camera for tracking the reader's gaze and a sensor for measuring heart rate. (Note 7) The system according to Appendix 1, characterized in that the sensing unit estimates the reader's emotions and adjusts the accuracy of the reading pace based on the estimated reader's emotions. (Note 8) The system according to Appendix 1, characterized in that the sensing unit analyzes the reader's reading history and improves the sensing method based on past reading patterns. (Note 9) The system according to Appendix 1, characterized in that the sensing unit senses the reader's reading environment and adjusts the method of sensing the reading pace based on the environment. (Note 10) The system according to Appendix 1, characterized in that the sensing unit estimates the reader's emotions and adjusts the accuracy of the attention level based on the estimated reader's emotions. (Note 11) The system according to Appendix 1, characterized in that the sensing unit analyzes the reader's device usage history and improves the sensing method based on the device usage pattern. (Note 12) The system according to Appendix 1, characterized in that the sensing unit senses the reader's physical condition and adjusts the method of sensing the reading pace based on that condition. (Note 13) The proposed system, as described in Appendix 1, is characterized by estimating the reader's emotions and adjusting the method of bookmark placement based on the estimated reader's emotions. (Note 14) The proposed system, as described in Appendix 1, is characterized by analyzing the reader's reading history and improving the method based on past bookmark locations. (Note 15) The proposed unit is the system described in Appendix 1, characterized in that it senses the reader's reading purpose and adjusts the method of bookmark placement based on that purpose. (Note 16) The proposed system, as described in Appendix 1, is characterized by estimating the reader's emotions and determining the ranking of bookmark positions based on the estimated reader emotions. (Note 17) The proposed system, as described in Appendix 1, is characterized by analyzing the reader's device usage history and improving the method of bookmark placement based on the device usage pattern. (Note 18) The proposed unit is the system described in Appendix 1, characterized in that it senses the reader's reading environment and adjusts the method of bookmark placement based on the environment. (Note 19) The system according to Appendix 1, characterized in that the highlighting section estimates the reader's emotions and adjusts the highlighting method based on the estimated reader's emotions. (Note 20) The system described in Appendix 1 is characterized in that the highlighting section analyzes the reader's reading history and improves the method based on past highlighting patterns. (Note 21) The system according to Appendix 1, characterized in that the highlighting section senses the reader's reading purpose and adjusts the highlighting method based on that purpose. (Note 22) The system according to Appendix 1, characterized in that the highlighting section estimates the reader's emotions and determines the ranking of the highlights based on the estimated reader's emotions. (Note 23) The system described in Appendix 1 is characterized in that the highlighting section analyzes the reader's device usage history and improves the highlighting method based on the device usage pattern. (Note 24) The system according to Appendix 1, characterized in that the highlighting section senses the reader's reading environment and adjusts the highlighting method based on the environment. (Note 25) The system according to Appendix 1, characterized in that the display unit estimates the reader's emotions and adjusts the method based on the estimated reader's emotions. (Note 26) The system according to Appendix 1, characterized in that the display unit analyzes the reader's reading history and improves the method based on past display patterns. (Note 27) The system according to Appendix 1, characterized in that the display unit senses the reader's reading purpose and adjusts the display method based on that purpose. (Note 28) The system according to Appendix 1, characterized in that the display unit estimates the reader's emotions and determines the order of the displayed content based on the estimated reader's emotions. (Note 29) The system according to Appendix 1, characterized in that the display unit analyzes the reader's device usage history and improves the display method based on the device usage pattern. (Note 30) The system according to Appendix 1, characterized in that the display unit senses the reader's reading environment and adjusts the display method based on the environment. [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A sensing unit that detects the reader's reading pace and level of attention, A suggestion unit that automatically suggests a bookmark location based on the information detected by the sensing unit, The highlighting section highlights keywords and phrases, It includes a display unit that visually displays the pause position and highlighted sections of the audiobook. A system characterized by the following features.

2. The sensing unit is It senses the speed at which pages are turned and the time spent on each page. The system according to feature 1.

3. The aforementioned proposal section is, Suggest bookmark locations based on the amount of time spent there and the number of times you return. The system according to feature 1.

4. The aforementioned highlight section is, Analyzes text and automatically highlights keywords and phrases. The system according to feature 1.

5. The aforementioned display unit is Records the playback position of an audiobook and visually displays the stop position and highlighted sections. The system according to feature 1.

6. The sensing unit is It features a camera to track the reader's gaze and a sensor to measure heart rate. The system according to feature 1.

7. The sensing unit is It estimates the reader's emotions and adjusts the accuracy of the reading pace based on those estimated emotions. The system according to feature 1.

8. The sensing unit is We analyze readers' reading history and improve our sensing methods based on past reading patterns. The system according to feature 1.

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