system

Abstract

The system according to this embodiment aims to efficiently digitize paper books and perform summarization and management. [Solution] The system according to the embodiment comprises a scanning unit, a summarizing unit, and a management unit. The scanning unit scans a book and converts it into text data. The summarizing unit summarizes the text data scanned by the scanning unit. The management unit manages the data summarized by the summarizing unit and converts it into an e-book format.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to efficiently digitize a paper book and perform summarization and management.

[0005] The system according to the embodiment aims to efficiently digitize a paper book and perform summarization and management.

Means for Solving the Problems

[0006] The system according to the embodiment includes a scanning unit, a summarizing unit, and a management unit. The scanning unit scans a book to convert it into text data. The summarizing unit summarizes the text data scanned by the scanning unit. The management unit manages the data summarized by the summarizing unit and converts it into the format of an e-book.

Effects of the Invention

[0007] The system according to this embodiment can efficiently digitize paper books and perform summarization and management. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The eBook conversion system according to an embodiment of the present invention is a system for digitizing paper books. This system is realized by multiple AI agents, each performing a specific role. Specifically, first, a "reading agent" scans the book and converts it into text data. Next, a "summarizing agent" summarizes the text data, and a "controlling agent" manages the entire process and converts it into an eBook format. Furthermore, by utilizing OCR technology and natural language processing technology, the system also provides multilingual support and summarization functions specialized for specific fields. For example, the eBook conversion system scans a paper book and achieves high-precision text data conversion using OCR technology. Next, the eBook conversion system analyzes the text data using natural language processing technology, extracts important information, and creates a summary. Furthermore, the eBook conversion system combines the text data and the summary and converts it into an eBook format. For example, the eBook conversion system creates summaries that support multiple languages ​​such as English and Chinese. The eBook conversion system also creates summaries specialized for specific fields such as medicine and law. This solves the problems of effort and cost faced by targets such as authors, publishers, educational institutions, government agencies, and libraries in digitizing physical books, and enables efficient digitization. Furthermore, advancements in generative AI technology enable more accurate and efficient digitization than ever before, promoting the democratization and accessibility of knowledge. This allows e-book systems to efficiently digitize, summarize, and manage printed books.

[0029] The e-book conversion system according to this embodiment comprises a scanning unit, a summarization unit, and a management unit. The scanning unit scans books and converts them into text data. For example, the scanning unit reads a paper book with a scanner and converts it into text data using OCR technology. For example, the scanning unit reads a handwritten book with a scanner and saves it as image data. Then, it converts the image data into text data using OCR technology. The scanning unit can also take a picture of a book using a smartphone camera and convert the image data into text data using a dedicated app. For example, the app automatically corrects the image and performs character recognition. The scanning unit can also scan a book with a dedicated digital pen and convert the digital pen into digital data in real time. For example, it detects the movement of the pen with a sensor and saves it as character data. The summarization unit summarizes the text data. For example, the summarization unit analyzes the text data using natural language processing technology, extracts important information, and creates a summary. For example, the summarization unit analyzes the text data, extracts important information, and creates a summary. The summarization unit can also analyze the text data, extract important information, and create a summary using generative AI. For example, the generation AI analyzes text data using a text generation AI (e.g., LLM), extracts important information, and creates a summary. The management unit manages the summarized data and converts it into an ebook format. The management unit, for example, combines the text data and the summary and converts it into an ebook format. The management unit, for example, combines the text data and the summary and converts it into an ebook format. The management unit can also use the generation AI to combine the text data and the summary and convert it into an ebook format. For example, the generation AI uses a text generation AI (e.g., LLM) to combine the text data and the summary and convert it into an ebook format. As a result, the ebook conversion system according to this embodiment can efficiently digitize, summarize, and manage paper books.

[0030] The scanning unit scans books and converts them into text data. For example, the scanning unit reads paper books with a scanner and converts them into text data using OCR technology. Specifically, the scanner reads the pages of the book at high resolution and saves them as image data. Then, OCR technology recognizes the characters in the image data and converts them into text data. In this process, OCR technology analyzes the font, size, and layout of the characters to generate accurate text data. The scanning unit can also scan handwritten books with a scanner and save them as image data. Then, OCR technology is used to convert the image data into text data. A specific handwriting recognition algorithm is used to recognize handwritten characters, analyzing the characteristics of the handwriting and converting them into text data. Furthermore, the scanning unit can also take pictures of books using a smartphone camera and convert the image data into text data using a dedicated app. The dedicated app automatically corrects the captured image, removing distortion and shadows to produce a clear image. Then, OCR technology is used to convert the image data into text data. In addition, the scanning unit can scan books with a dedicated digital pen, and the digital pen itself can convert the data into digital data in real time. The digital pen uses sensors to detect pen movement and saves the written characters as digital data in real time. This allows the scanning unit to scan books in various ways and efficiently convert them into text data.

[0031] The summarization unit summarizes text data. For example, it might use natural language processing (NLP) to analyze the text data, extract important information, and create a summary. Specifically, NLP analyzes the grammatical structure and meaning of the text data, identifying important keywords and phrases. This allows it to extract important information from the text data and create a summary. Alternatively, the summarization unit can use generative AI to analyze text data, extract important information, and create a summary. Generative AI uses text generation AI (e.g., LLM) to analyze text data, extract important information, and create a summary. Because generative AI has the ability to learn from large amounts of text data and understand context and meaning, it can create more accurate summaries. For example, generative AI can extract important paragraphs and sentences from the text data and combine them to create a summary. Furthermore, generative AI can understand the content of the text data and highlight important points within the summary. This allows the summarization unit to efficiently and accurately summarize text data and provide important information to the user. Additionally, the summarization unit can adjust the length and level of detail of the summary according to the user's requirements. For example, if a short summary is needed, only the key points are extracted to create a concise summary. On the other hand, if a detailed summary is needed, a summary containing more information is created. This allows the summarization function to provide flexible summaries tailored to the user's needs.

[0032] The management department manages the summarized data and converts it into ebook format. For example, the management department combines text data and summaries and converts them into ebook format. Specifically, the management department uses templates to combine text data and summaries in an appropriate layout and convert them into ebook format. The templates are customized according to the format and design of the ebook, allowing for the beautiful arrangement of text data and summaries. The management department can also use generative AI to combine text data and summaries and convert them into ebook format. The generative AI uses text generation AI (e.g., LLM) to analyze the text data and summaries and propose the optimal layout and design. This allows the management department to create ebooks efficiently and beautifully. Furthermore, the management department centrally manages the summarized data and can collaborate with other systems and departments as needed. For example, the management department stores the summarized data on a cloud server, making it accessible to other systems. The management department can also convert ebook formats to different formats to support multiple devices and platforms. This allows the management department to enable users to view ebooks on various devices. In addition, the management department can manage the metadata of ebooks, making searching and classification easier. For example, metadata such as author name, title, and genre can be managed to allow users to easily search for ebooks. This enables the management department to efficiently manage and distribute ebooks and provide a user-friendly system.

[0033] The scanning unit can convert the data into text data using OCR technology. Using OCR technology enables highly accurate text data conversion. Some or all of the above-described processes in the scanning unit may be performed using, for example, a generation AI, or without a generation AI. For example, the scanning unit can input image data acquired using OCR technology into a generation AI, causing the generation AI to generate text data from the image data.

[0034] The summarization unit can analyze text data using natural language processing techniques and create a summary. For example, the summarization unit can analyze text data using morphological analysis and create a summary. For example, the summarization unit can also analyze text data using grammatical analysis and create a summary. For example, the summarization unit can also analyze text data using semantic analysis and create a summary. This makes highly accurate summarization possible by using natural language processing techniques. Some or all of the above-described processes in the summarization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the summarization unit can input text data acquired using natural language processing techniques into a generative AI and have the generative AI perform the analysis of the text data and the generation of a summary.

[0035] The management unit can convert the data into an ebook format. For example, it can convert it to EPUB format. The management unit can also convert it to PDF format. The management unit can also convert it to MOBI format. By converting it to an ebook format, it becomes available for use as an ebook. Some or all of the above processing in the management unit may be performed using, for example, a generation AI, or without a generation AI. For example, the management unit can input text data and a summary into a generation AI and have the generation AI perform the conversion to an ebook format.

[0036] The summarization unit can create multilingual summaries. For example, the summarization unit can create an English summary. For example, the summarization unit can also create a Chinese summary. For example, the summarization unit can also create a Spanish summary. This makes it possible to support various languages ​​by creating multilingual summaries. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the summarization unit can input text data into a generative AI and have the generative AI create a multilingual summary.

[0037] The summarization unit can create summaries specialized in specific fields. For example, it can create summaries specialized in the medical field. For example, it can also create summaries specialized in the legal field. For example, it can also create summaries specialized in the technical field. This allows for specialized summaries by creating summaries specialized in specific fields. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the summarization unit can input text data into a generative AI and have the generative AI create a summary specialized in a specific field.

[0038] The scanning unit can detect the condition of the paper media and select the optimal scanning method. For example, if the paper is deteriorated, the scanning unit can increase the scanning resolution to capture details. For example, if the paper is dirty, the scanning unit can apply a filter to remove the dirt. For example, if the paper is torn, the scanning unit can use an algorithm to fill in the torn parts. This improves the accuracy of the scan by selecting the optimal scanning method according to the condition of the paper media. Some or all of the above processing in the scanning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the scanning unit can input paper media condition data into a generative AI and have the generative AI select the optimal scanning method.

[0039] The scanning unit can apply different scanning algorithms depending on the type of book. For example, in the case of a novel, the scanning unit may use an algorithm that prioritizes the accuracy of reading characters. For example, in the case of a textbook, the scanning unit may also use an algorithm that prioritizes the accuracy of reading diagrams and mathematical formulas. For example, in the case of a magazine, the scanning unit may also use an algorithm that prioritizes the accuracy of reading images. By applying a scanning algorithm appropriate to the type of book, the scanning accuracy is improved. Some or all of the above processing in the scanning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the scanning unit can input book type data into a generative AI and have the generative AI select the optimal scanning algorithm.

[0040] The scanning unit can apply highly relevant scanning settings by considering the geographical publication information of the book. For example, if the book is published in a geographically nearby region, the scanning unit will apply scanning settings that match the language and format of that region. For example, if the book is published in a geographically distant region, the scanning unit can also apply general scanning settings. For example, if the book is popular in a particular region, the scanning unit can also apply scanning settings that match the users in that region. This allows for more appropriate scanning settings by considering the geographical publication information of the book. Some or all of the above processing in the scanning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the scanning unit can input the geographical publication information of the book into a generative AI and have the generative AI select the optimal scanning settings.

[0041] The scanning unit can analyze the book's social media ratings and prioritize scanning important pages. For example, the scanning unit can prioritize scanning pages that have received high ratings on social media. It can also prioritize scanning pages that have received many comments on social media. It can also prioritize scanning pages that have been shared on social media. This allows for the priority scanning of important pages by considering social media ratings. Some or all of the above processing in the scanning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the scanning unit can input social media rating data of the book into a generative AI and have the generative AI select important pages.

[0042] The summarization unit can adjust the level of detail in the summary based on the importance of the text data. For example, if there is a lot of important information, the summarization unit will provide a detailed summary. For example, if there is little important information, the summarization unit can also provide a concise summary. For example, the summarization unit can also provide a summary that highlights the most important parts. In this way, a more appropriate summary is provided by adjusting the level of detail in the summary based on the importance of the text data. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the summarization unit can input importance data of the text data into a generative AI and have the generative AI adjust the level of detail in the summary.

[0043] The summarization unit can apply different summarization algorithms depending on the category of the text data. For example, in the case of a technical book, the summarization unit can provide a summary that emphasizes technical details. In the case of literature, for example, the summarization unit can provide a summary that emphasizes the flow of the story. In the case of a business book, for example, the summarization unit can provide a summary that emphasizes practical information. By applying a summarization algorithm appropriate to the category of the text data, the accuracy of the summary is improved. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the summarization unit can input the category data of the text data into a generative AI and have the generative AI select the optimal summarization algorithm.

[0044] The summarization unit can determine the priority of summarization based on when the text data was created. For example, the summarization unit may prioritize summarizing the most recent text data. For example, the summarization unit may also postpone summarizing older text data. For example, the summarization unit may prioritize summarizing text data created during a specific period. This provides a more appropriate summary by prioritizing summarization based on when the text data was created. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the summarization unit can input text data creation date data into a generative AI and have the generative AI determine the priority of summarization.

[0045] The summarization unit can adjust the order of summaries based on the relevance of the text data. For example, the summarization unit may prioritize summarizing highly relevant text data. For example, the summarization unit may postpone summarizing less relevant text data. For example, the summarization unit may prioritize summarizing text data related to a specific theme. This provides a more appropriate summary by adjusting the order of summaries based on the relevance of the text data. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the summarization unit can input relevance data of the text data into a generative AI and have the generative AI adjust the order of summaries.

[0046] The management department can select the optimal management method by referring to the past management history of the text data. For example, the management department can select the optimal management method based on management methods used in the past. For example, the management department can also select an efficient management method from the past management history. For example, the management department can analyze the past management history and select the most suitable management method. In this way, the optimal management method is selected by referring to the past management history of the text data. Some or all of the above processes in the management department may be performed using, for example, a generation AI, or not using a generation AI. For example, the management department can input the past management history data of the text data into a generation AI and have the generation AI select the optimal management method.

[0047] The management unit can customize the management methods based on the current state of the text data. For example, if the text data is new, the management unit applies the normal management method. For example, if the text data is old, the management unit can also apply a special management method. For example, the management unit can select the optimal management method depending on the state of the text data. This allows for more appropriate management by customizing the management methods based on the current state of the text data. Some or all of the above processing in the management unit may be performed using, for example, a generation AI, or without a generation AI. For example, the management unit can input the current state data of the text data into a generation AI and have the generation AI select the optimal management method.

[0048] The management department can select the optimal management method for text data by considering its geographical publication information. For example, if the text data is published in a geographically close region, the management department will apply the management method for that region. For example, if the text data is published in a geographically distant region, the management department may also apply a general management method. For example, if the text data is popular in a particular region, the management department may also apply the management method for that region. This ensures that a more appropriate management method is selected by considering the geographical publication information of the text data. Some or all of the above processing in the management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the management department can input the geographical publication information of the text data into a generative AI and have the generative AI select the optimal management method.

[0049] The management department can analyze the social media ratings of text data and propose management methods. For example, the management department can prioritize the management of text data that has received high ratings on social media. The management department can also prioritize the management of text data that has received many comments on social media. The management department can also prioritize the management of text data that has been shared on social media. By taking social media ratings into consideration, more appropriate management methods can be proposed. Some or all of the above processing by the management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the management department can input social media rating data of text data into a generative AI and have the generative AI propose the optimal management method.

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

[0051] The e-book conversion system can further analyze the user's reading history and provide summaries optimized for each individual user. For example, the summarization function can analyze the genres and content of books the user has read in the past and provide more detailed summaries of similar books. The summarization function can also create summaries that highlight information related to themes the user is particularly interested in. Furthermore, the summarization function can adjust the length and level of detail of the summary based on the user's reading speed and comprehension. This results in more personalized summaries based on the user's reading history.

[0052] The e-book conversion system can further collect user feedback and improve the quality of the summaries. For example, the summarization section can analyze user feedback and improve the content and format of the summaries. The summarization section can also provide a function for users to rate summaries and adjust the summarization algorithm based on that rating. Furthermore, the summarization section can provide a function for users to leave comments on summaries and use those comments to improve the quality of the summaries. This allows for the provision of higher-quality summaries by leveraging user feedback.

[0053] The e-book conversion system can further detect the user's reading environment and provide the optimal summary format. For example, the summary unit may provide a short summary if the user is on the move. The summary unit may also provide a detailed summary if the user is in a quiet environment. The summary unit may also provide a summary that includes diagrams and graphs if the user prefers visual information. This ensures that the optimal summary format is provided according to the user's reading environment. Some or all of the above processing in the summary unit may be performed using, for example, a generative AI, or without a generative AI. For example, the summary unit may input user environment data into a generative AI and have the generative AI select the optimal summary format.

[0054] The e-book conversion system can further analyze the user's reading history and provide optimal scanning settings for each individual user. For example, the scanning unit can analyze the genre and content of books the user has read in the past and apply the optimal scanning settings to similar books. The scanning unit can also perform detailed scans on books related to themes that the user is particularly interested in. Furthermore, the scanning unit can adjust the scanning resolution and speed based on the user's reading speed and comprehension. This provides more personalized scanning settings based on the user's reading history.

[0055] The e-book conversion system can further detect the user's reading environment and provide the optimal scanning method. For example, the scanning unit can provide a simple scanning method if the user is on the move. The scanning unit can also provide a detailed scanning method if the user is in a quiet environment. The scanning unit can also provide an image-focused scanning method if the user prefers visual information. This ensures that the optimal scanning method is provided according to the user's reading environment. Some or all of the above processing in the scanning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the scanning unit can input user environment data into the generative AI and have the generative AI select the optimal scanning method.

[0056] The e-book conversion system can further analyze users' reading history and provide personalized management methods. For example, the management unit can analyze the genres and content of books a user has read in the past and apply the most suitable management method to similar books. The management unit can also provide detailed management for books related to themes that the user is particularly interested in. Furthermore, the management unit can adjust the management method based on the user's reading speed and comprehension level. This provides a more personalized management method based on the user's reading history.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The scanning unit scans books and converts them into text data. The scanning unit reads paper books with a scanner and converts them into text data using OCR technology. It can also scan handwritten books, save them as image data, and then convert the image data into text data using OCR technology. Furthermore, it is possible to take a picture of a book using a smartphone camera and convert the image data into text data using a dedicated app. It is also possible to scan books using a dedicated digital pen, and the digital pen will convert the data into digital data in real time. Step 2: The summarization unit summarizes the text data scanned by the scanning unit. The summarization unit analyzes the text data using natural language processing technology, extracts important information, and creates a summary. It is also possible to analyze the text data using generative AI, extract important information, and create a summary. Step 3: The management unit manages the data summarized by the summarization unit and converts it into an ebook format. The management unit combines the text data and summaries and converts them into an ebook format. It is also possible to use a generation AI to combine the text data and summaries and convert them into an ebook format.

[0059] (Example of form 2) The eBook conversion system according to an embodiment of the present invention is a system for digitizing paper books. This system is realized by multiple AI agents, each performing a specific role. Specifically, first, a "reading agent" scans the book and converts it into text data. Next, a "summarizing agent" summarizes the text data, and a "controlling agent" manages the entire process and converts it into an eBook format. Furthermore, by utilizing OCR technology and natural language processing technology, the system also provides multilingual support and summarization functions specialized for specific fields. For example, the eBook conversion system scans a paper book and achieves high-precision text data conversion using OCR technology. Next, the eBook conversion system analyzes the text data using natural language processing technology, extracts important information, and creates a summary. Furthermore, the eBook conversion system combines the text data and the summary and converts it into an eBook format. For example, the eBook conversion system creates summaries that support multiple languages ​​such as English and Chinese. The eBook conversion system also creates summaries specialized for specific fields such as medicine and law. This solves the problems of effort and cost faced by targets such as authors, publishers, educational institutions, government agencies, and libraries in digitizing physical books, and enables efficient digitization. Furthermore, advancements in generative AI technology enable more accurate and efficient digitization than ever before, promoting the democratization and accessibility of knowledge. This allows e-book systems to efficiently digitize, summarize, and manage printed books.

[0060] The e-book conversion system according to this embodiment comprises a scanning unit, a summarization unit, and a management unit. The scanning unit scans books and converts them into text data. For example, the scanning unit reads a paper book with a scanner and converts it into text data using OCR technology. For example, the scanning unit reads a handwritten book with a scanner and saves it as image data. Then, it converts the image data into text data using OCR technology. The scanning unit can also take a picture of a book using a smartphone camera and convert the image data into text data using a dedicated app. For example, the app automatically corrects the image and performs character recognition. The scanning unit can also scan a book with a dedicated digital pen and convert the digital pen into digital data in real time. For example, it detects the movement of the pen with a sensor and saves it as character data. The summarization unit summarizes the text data. For example, the summarization unit analyzes the text data using natural language processing technology, extracts important information, and creates a summary. For example, the summarization unit analyzes the text data, extracts important information, and creates a summary. The summarization unit can also analyze the text data, extract important information, and create a summary using generative AI. For example, the generation AI analyzes text data using a text generation AI (e.g., LLM), extracts important information, and creates a summary. The management unit manages the summarized data and converts it into an ebook format. The management unit, for example, combines the text data and the summary and converts it into an ebook format. The management unit, for example, combines the text data and the summary and converts it into an ebook format. The management unit can also use the generation AI to combine the text data and the summary and convert it into an ebook format. For example, the generation AI uses a text generation AI (e.g., LLM) to combine the text data and the summary and convert it into an ebook format. As a result, the ebook conversion system according to this embodiment can efficiently digitize, summarize, and manage paper books.

[0061] The scanning unit scans books and converts them into text data. For example, the scanning unit reads paper books with a scanner and converts them into text data using OCR technology. Specifically, the scanner reads the pages of the book at high resolution and saves them as image data. Then, OCR technology recognizes the characters in the image data and converts them into text data. In this process, OCR technology analyzes the font, size, and layout of the characters to generate accurate text data. The scanning unit can also scan handwritten books with a scanner and save them as image data. Then, OCR technology is used to convert the image data into text data. A specific handwriting recognition algorithm is used to recognize handwritten characters, analyzing the characteristics of the handwriting and converting them into text data. Furthermore, the scanning unit can also take pictures of books using a smartphone camera and convert the image data into text data using a dedicated app. The dedicated app automatically corrects the captured image, removing distortion and shadows to produce a clear image. Then, OCR technology is used to convert the image data into text data. In addition, the scanning unit can scan books with a dedicated digital pen, and the digital pen itself can convert the data into digital data in real time. The digital pen uses sensors to detect pen movement and saves the written characters as digital data in real time. This allows the scanning unit to scan books in various ways and efficiently convert them into text data.

[0062] The summarization unit summarizes text data. For example, it might use natural language processing (NLP) to analyze the text data, extract important information, and create a summary. Specifically, NLP analyzes the grammatical structure and meaning of the text data, identifying important keywords and phrases. This allows it to extract important information from the text data and create a summary. Alternatively, the summarization unit can use generative AI to analyze text data, extract important information, and create a summary. Generative AI uses text generation AI (e.g., LLM) to analyze text data, extract important information, and create a summary. Because generative AI has the ability to learn from large amounts of text data and understand context and meaning, it can create more accurate summaries. For example, generative AI can extract important paragraphs and sentences from the text data and combine them to create a summary. Furthermore, generative AI can understand the content of the text data and highlight important points within the summary. This allows the summarization unit to efficiently and accurately summarize text data and provide important information to the user. Additionally, the summarization unit can adjust the length and level of detail of the summary according to the user's requirements. For example, if a short summary is needed, only the key points are extracted to create a concise summary. On the other hand, if a detailed summary is needed, a summary containing more information is created. This allows the summarization function to provide flexible summaries tailored to the user's needs.

[0063] The management department manages the summarized data and converts it into ebook format. For example, the management department combines text data and summaries and converts them into ebook format. Specifically, the management department uses templates to combine text data and summaries in an appropriate layout and convert them into ebook format. The templates are customized according to the format and design of the ebook, allowing for the beautiful arrangement of text data and summaries. The management department can also use generative AI to combine text data and summaries and convert them into ebook format. The generative AI uses text generation AI (e.g., LLM) to analyze the text data and summaries and propose the optimal layout and design. This allows the management department to create ebooks efficiently and beautifully. Furthermore, the management department centrally manages the summarized data and can collaborate with other systems and departments as needed. For example, the management department stores the summarized data on a cloud server, making it accessible to other systems. The management department can also convert ebook formats to different formats to support multiple devices and platforms. This allows the management department to enable users to view ebooks on various devices. In addition, the management department can manage the metadata of ebooks, making searching and classification easier. For example, metadata such as author name, title, and genre can be managed to allow users to easily search for ebooks. This enables the management department to efficiently manage and distribute ebooks and provide a user-friendly system.

[0064] The scanning unit can convert the data into text data using OCR technology. Using OCR technology enables highly accurate text data conversion. Some or all of the above-described processes in the scanning unit may be performed using, for example, a generation AI, or without a generation AI. For example, the scanning unit can input image data acquired using OCR technology into a generation AI, causing the generation AI to generate text data from the image data.

[0065] The summarization unit can analyze text data using natural language processing techniques and create a summary. For example, the summarization unit can analyze text data using morphological analysis and create a summary. For example, the summarization unit can also analyze text data using grammatical analysis and create a summary. For example, the summarization unit can also analyze text data using semantic analysis and create a summary. This makes highly accurate summarization possible by using natural language processing techniques. Some or all of the above-described processes in the summarization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the summarization unit can input text data acquired using natural language processing techniques into a generative AI and have the generative AI perform the analysis of the text data and the generation of a summary.

[0066] The management unit can convert the data into an ebook format. For example, it can convert it to EPUB format. The management unit can also convert it to PDF format. The management unit can also convert it to MOBI format. By converting it to an ebook format, it becomes available for use as an ebook. Some or all of the above processing in the management unit may be performed using, for example, a generation AI, or without a generation AI. For example, the management unit can input text data and a summary into a generation AI and have the generation AI perform the conversion to an ebook format.

[0067] The summarization unit can create multilingual summaries. For example, the summarization unit can create an English summary. For example, the summarization unit can also create a Chinese summary. For example, the summarization unit can also create a Spanish summary. This makes it possible to support various languages ​​by creating multilingual summaries. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the summarization unit can input text data into a generative AI and have the generative AI create a multilingual summary.

[0068] The summarization unit can create summaries specialized in specific fields. For example, it can create summaries specialized in the medical field. For example, it can also create summaries specialized in the legal field. For example, it can also create summaries specialized in the technical field. This allows for specialized summaries by creating summaries specialized in specific fields. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the summarization unit can input text data into a generative AI and have the generative AI create a summary specialized in a specific field.

[0069] The scanning unit can estimate the user's emotions and adjust the scanning timing based on the estimated emotions. For example, if the user is relaxed, the scanning unit will set the scanning speed to normal. For example, if the user is in a hurry, the scanning unit can also speed up the scanning speed. For example, if the user is stressed, the scanning unit can slow down the scanning speed and wait until the user calms down. This allows for more appropriate scanning by adjusting the scanning timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, 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 scanning unit may be performed using a generative AI, or not using a generative AI. For example, the scanning unit can input the user's facial expression data into a generative AI and have the generative AI estimate the user's emotions.

[0070] The scanning unit can detect the condition of the paper media and select the optimal scanning method. For example, if the paper is deteriorated, the scanning unit can increase the scanning resolution to capture details. For example, if the paper is dirty, the scanning unit can apply a filter to remove the dirt. For example, if the paper is torn, the scanning unit can use an algorithm to fill in the torn parts. This improves the accuracy of the scan by selecting the optimal scanning method according to the condition of the paper media. Some or all of the above processing in the scanning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the scanning unit can input paper media condition data into a generative AI and have the generative AI select the optimal scanning method.

[0071] The scanning unit can apply different scanning algorithms depending on the type of book. For example, in the case of a novel, the scanning unit may use an algorithm that prioritizes the accuracy of reading characters. For example, in the case of a textbook, the scanning unit may also use an algorithm that prioritizes the accuracy of reading diagrams and mathematical formulas. For example, in the case of a magazine, the scanning unit may also use an algorithm that prioritizes the accuracy of reading images. By applying a scanning algorithm appropriate to the type of book, the scanning accuracy is improved. Some or all of the above processing in the scanning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the scanning unit can input book type data into a generative AI and have the generative AI select the optimal scanning algorithm.

[0072] The scanning unit can estimate the user's emotions and determine the priority of pages to scan based on the estimated emotions. For example, if the user is excited, the scanning unit may prioritize scanning important pages. If the user is relaxed, the scanning unit may proceed with scanning in order. If the user is in a hurry, the scanning unit may prioritize scanning pages that contain the main points. This allows for a more appropriate scan by determining the priority of pages to scan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, 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 scanning unit may be performed using a generative AI, or not using a generative AI. For example, the scanning unit can input the user's facial expression data into a generative AI and have the generative AI estimate the user's emotions.

[0073] The scanning unit can apply highly relevant scanning settings by considering the geographical publication information of the book. For example, if the book is published in a geographically nearby region, the scanning unit will apply scanning settings that match the language and format of that region. For example, if the book is published in a geographically distant region, the scanning unit can also apply general scanning settings. For example, if the book is popular in a particular region, the scanning unit can also apply scanning settings that match the users in that region. This allows for more appropriate scanning settings by considering the geographical publication information of the book. Some or all of the above processing in the scanning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the scanning unit can input the geographical publication information of the book into a generative AI and have the generative AI select the optimal scanning settings.

[0074] The scanning unit can analyze the book's social media ratings and prioritize scanning important pages. For example, the scanning unit can prioritize scanning pages that have received high ratings on social media. It can also prioritize scanning pages that have received many comments on social media. It can also prioritize scanning pages that have been shared on social media. This allows for the priority scanning of important pages by considering social media ratings. Some or all of the above processing in the scanning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the scanning unit can input social media rating data of the book into a generative AI and have the generative AI select important pages.

[0075] The summarization unit can estimate the user's emotions and adjust the way the summary is presented based on the estimated emotions. For example, if the user is relaxed, the summarization unit can provide a detailed summary. If the user is in a hurry, the summarization unit can also provide a concise summary. If the user is excited, the summarization unit can also provide a visually appealing summary. By adjusting the way the summary is presented according to the user's emotions, a more appropriate summary can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the summarization unit may be performed using a generative AI, or not using a generative AI. For example, the summarization unit can input user facial expression data into a generative AI and have the generative AI estimate the user's emotions.

[0076] The summarization unit can adjust the level of detail in the summary based on the importance of the text data. For example, if there is a lot of important information, the summarization unit will provide a detailed summary. For example, if there is little important information, the summarization unit can also provide a concise summary. For example, the summarization unit can also provide a summary that highlights the most important parts. In this way, a more appropriate summary is provided by adjusting the level of detail in the summary based on the importance of the text data. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the summarization unit can input importance data of the text data into a generative AI and have the generative AI adjust the level of detail in the summary.

[0077] The summarization unit can apply different summarization algorithms depending on the category of the text data. For example, in the case of a technical book, the summarization unit can provide a summary that emphasizes technical details. In the case of literature, for example, the summarization unit can provide a summary that emphasizes the flow of the story. In the case of a business book, for example, the summarization unit can provide a summary that emphasizes practical information. By applying a summarization algorithm appropriate to the category of the text data, the accuracy of the summary is improved. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the summarization unit can input the category data of the text data into a generative AI and have the generative AI select the optimal summarization algorithm.

[0078] The summarization unit can estimate the user's emotions and adjust the length of the summary based on the estimated emotions. For example, if the user is relaxed, the summarization unit can provide a longer summary. For example, if the user is in a hurry, the summarization unit can also provide a shorter summary. For example, if the user is excited, the summarization unit can also provide a visually appealing summary. By adjusting the length of the summary according to the user's emotions, a more appropriate summary can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the summarization unit may be performed using a generative AI, or not using a generative AI. For example, the summarization unit can input user facial expression data into a generative AI and have the generative AI estimate the user's emotions.

[0079] The summarization unit can determine the priority of summarization based on when the text data was created. For example, the summarization unit may prioritize summarizing the most recent text data. For example, the summarization unit may also postpone summarizing older text data. For example, the summarization unit may prioritize summarizing text data created during a specific period. This provides a more appropriate summary by prioritizing summarization based on when the text data was created. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the summarization unit can input text data creation date data into a generative AI and have the generative AI determine the priority of summarization.

[0080] The summarization unit can adjust the order of summaries based on the relevance of the text data. For example, the summarization unit may prioritize summarizing highly relevant text data. For example, the summarization unit may postpone summarizing less relevant text data. For example, the summarization unit may prioritize summarizing text data related to a specific theme. This provides a more appropriate summary by adjusting the order of summaries based on the relevance of the text data. Some or all of the above processing in the summarization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the summarization unit can input relevance data of the text data into a generative AI and have the generative AI adjust the order of summaries.

[0081] The management unit can estimate the user's emotions and adjust the management method based on the estimated emotions. For example, if the user is relaxed, the management unit can apply a normal management method. For example, if the user is in a hurry, the management unit can also apply a rapid management method. For example, if the user is stressed, the management unit can also apply a simplified management method. This allows for more appropriate management by adjusting the management method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the management unit may be performed using a generative AI, for example, or without a generative AI. For example, the management unit can input user facial expression data into a generative AI and have the generative AI estimate the user's emotions.

[0082] The management department can select the optimal management method by referring to the past management history of the text data. For example, the management department can select the optimal management method based on management methods used in the past. For example, the management department can also select an efficient management method from the past management history. For example, the management department can analyze the past management history and select the most suitable management method. In this way, the optimal management method is selected by referring to the past management history of the text data. Some or all of the above processes in the management department may be performed using, for example, a generation AI, or not using a generation AI. For example, the management department can input the past management history data of the text data into a generation AI and have the generation AI select the optimal management method.

[0083] The management unit can customize the management methods based on the current state of the text data. For example, if the text data is new, the management unit applies the normal management method. For example, if the text data is old, the management unit can also apply a special management method. For example, the management unit can select the optimal management method depending on the state of the text data. This allows for more appropriate management by customizing the management methods based on the current state of the text data. Some or all of the above processing in the management unit may be performed using, for example, a generation AI, or without a generation AI. For example, the management unit can input the current state data of the text data into a generation AI and have the generation AI select the optimal management method.

[0084] The management unit can estimate the user's emotions and determine management priorities based on the estimated emotions. For example, if the user is relaxed, the management unit will manage with normal priorities. If the user is in a hurry, the management unit may prioritize managing important data. If the user is stressed, the management unit may manage with simplified priorities. This allows for more appropriate management by determining management priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the management unit may be performed using a generative AI, or not using a generative AI. For example, the management unit can input user facial expression data into a generative AI and have the generative AI estimate the user's emotions.

[0085] The management department can select the optimal management method for text data by considering its geographical publication information. For example, if the text data is published in a geographically close region, the management department will apply the management method for that region. For example, if the text data is published in a geographically distant region, the management department may also apply a general management method. For example, if the text data is popular in a particular region, the management department may also apply the management method for that region. This ensures that a more appropriate management method is selected by considering the geographical publication information of the text data. Some or all of the above processing in the management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the management department can input the geographical publication information of the text data into a generative AI and have the generative AI select the optimal management method.

[0086] The management department can analyze the social media ratings of text data and propose management methods. For example, the management department can prioritize the management of text data that has received high ratings on social media. The management department can also prioritize the management of text data that has received many comments on social media. The management department can also prioritize the management of text data that has been shared on social media. By taking social media ratings into consideration, more appropriate management methods can be proposed. Some or all of the above processing by the management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the management department can input social media rating data of text data into a generative AI and have the generative AI propose the optimal management method.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The e-book conversion system can further analyze the user's reading history and provide summaries optimized for each individual user. For example, the summarization function can analyze the genres and content of books the user has read in the past and provide more detailed summaries of similar books. The summarization function can also create summaries that highlight information related to themes the user is particularly interested in. Furthermore, the summarization function can adjust the length and level of detail of the summary based on the user's reading speed and comprehension. This results in more personalized summaries based on the user's reading history.

[0089] The e-book conversion system can further collect user feedback and improve the quality of the summaries. For example, the summarization section can analyze user feedback and improve the content and format of the summaries. The summarization section can also provide a function for users to rate summaries and adjust the summarization algorithm based on that rating. Furthermore, the summarization section can provide a function for users to leave comments on summaries and use those comments to improve the quality of the summaries. This allows for the provision of higher-quality summaries by leveraging user feedback.

[0090] The e-book conversion system can further estimate the user's emotions and adjust the content of the summary based on the estimated emotions. For example, the summary unit can provide a detailed summary if the user is relaxed. The summary unit can also provide a concise summary if the user is in a hurry. The summary unit can also provide a visually appealing summary if the user is excited. By adjusting the content of the summary according to the user's emotions, a more appropriate summary can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the summary unit may be performed using a generative AI, or not using a generative AI. For example, the summary unit can input user facial expression data into a generative AI and have the generative AI estimate the user's emotions.

[0091] The e-book conversion system can further detect the user's reading environment and provide the optimal summary format. For example, the summary unit may provide a short summary if the user is on the move. The summary unit may also provide a detailed summary if the user is in a quiet environment. The summary unit may also provide a summary that includes diagrams and graphs if the user prefers visual information. This ensures that the optimal summary format is provided according to the user's reading environment. Some or all of the above processing in the summary unit may be performed using, for example, a generative AI, or without a generative AI. For example, the summary unit may input user environment data into a generative AI and have the generative AI select the optimal summary format.

[0092] The e-book conversion system can further estimate the user's emotions and adjust the visual representation of the summary based on the estimated emotions. For example, the summary unit may provide a summary with calm colors if the user is relaxed. The summary unit may also provide a summary with vivid colors if the user is excited. The summary unit may also provide a simple and easy-to-read summary if the user is stressed. This allows for the provision of a more appropriate summary by adjusting the visual representation of the summary according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 summary unit may be performed using a generative AI, or not. For example, the summary unit can input user facial expression data into a generative AI and have the generative AI estimate the user's emotions.

[0093] The e-book conversion system can further analyze the user's reading history and provide optimal scanning settings for each individual user. For example, the scanning unit can analyze the genre and content of books the user has read in the past and apply the optimal scanning settings to similar books. The scanning unit can also perform detailed scans on books related to themes that the user is particularly interested in. Furthermore, the scanning unit can adjust the scanning resolution and speed based on the user's reading speed and comprehension. This provides more personalized scanning settings based on the user's reading history.

[0094] The e-book conversion system can further estimate the user's emotions and adjust the scan resolution based on the estimated emotions. For example, the scanning unit can scan at normal resolution when the user is relaxed. The scanning unit can also scan at a lower resolution when the user is in a hurry. The scanning unit can also scan at a higher resolution to provide more detailed information when the user is stressed. By adjusting the scan resolution according to the user's emotions, a more appropriate scan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the scanning unit may be performed using a generative AI, or not using a generative AI. For example, the scanning unit can input the user's facial expression data into a generative AI and have the generative AI estimate the user's emotions.

[0095] The e-book conversion system can further detect the user's reading environment and provide the optimal scanning method. For example, the scanning unit can provide a simple scanning method if the user is on the move. The scanning unit can also provide a detailed scanning method if the user is in a quiet environment. The scanning unit can also provide an image-focused scanning method if the user prefers visual information. This ensures that the optimal scanning method is provided according to the user's reading environment. Some or all of the above processing in the scanning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the scanning unit can input user environment data into the generative AI and have the generative AI select the optimal scanning method.

[0096] The e-book conversion system can further estimate the user's emotions and adjust the scanning order based on the estimated emotions. For example, if the user is relaxed, the scanning unit will proceed with scanning in order. If the user is in a hurry, the scanning unit can also prioritize scanning important pages. If the user is stressed, the scanning unit can also pause scanning until the user calms down. This allows for more appropriate scanning by adjusting the scanning order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the scanning unit may be performed using a generative AI, or not using a generative AI. For example, the scanning unit can input the user's facial expression data into a generative AI and have the generative AI estimate the user's emotions.

[0097] The e-book conversion system can further analyze users' reading history and provide personalized management methods. For example, the management unit can analyze the genres and content of books a user has read in the past and apply the most suitable management method to similar books. The management unit can also provide detailed management for books related to themes that the user is particularly interested in. Furthermore, the management unit can adjust the management method based on the user's reading speed and comprehension level. This provides a more personalized management method based on the user's reading history.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The scanning unit scans books and converts them into text data. The scanning unit reads paper books with a scanner and converts them into text data using OCR technology. It can also scan handwritten books, save them as image data, and then convert the image data into text data using OCR technology. Furthermore, it is possible to take a picture of a book using a smartphone camera and convert the image data into text data using a dedicated app. It is also possible to scan books using a dedicated digital pen, and the digital pen will convert the data into digital data in real time. Step 2: The summarization unit summarizes the text data scanned by the scanning unit. The summarization unit analyzes the text data using natural language processing technology, extracts important information, and creates a summary. It is also possible to analyze the text data using generative AI, extract important information, and create a summary. Step 3: The management unit manages the data summarized by the summarization unit and converts it into an ebook format. The management unit combines the text data and summaries and converts them into an ebook format. It is also possible to use a generation AI to combine the text data and summaries and convert them into an ebook format.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] Each of the multiple elements described above, including the scanning unit, summarizing unit, and management unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the scanning unit uses the camera 42 of the smart device 14 to photograph a book and converts it into text data using OCR technology. The summarizing unit is implemented in the specific processing unit 290 of the data processing unit 12, which analyzes the text data using natural language processing technology, extracts important information, and creates a summary. The management unit is implemented in the specific processing unit 290 of the data processing unit 12, which combines the text data and summary and converts them into an ebook format. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] Each of the multiple elements described above, including the scanning unit, summarizing unit, and management unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the scanning unit uses the camera 42 of the smart glasses 214 to photograph a book and converts it into text data using OCR technology. The summarizing unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, which analyzes the text data using natural language processing technology, extracts important information, and creates a summary. The management unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, which combines the text data and summary and converts them into an ebook format. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] Each of the multiple elements described above, including the scanning unit, summarizing unit, and management unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the scanning unit uses the camera 42 of the headset terminal 314 to photograph a book and converts it into text data using OCR technology. The summarizing unit is implemented in the specific processing unit 290 of the data processing unit 12, which analyzes the text data using natural language processing technology, extracts important information, and creates a summary. The management unit is implemented in the specific processing unit 290 of the data processing unit 12, which combines the text data and summary and converts them into an ebook format. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the scanning unit, summarizing unit, and management unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the scanning unit uses the camera 42 of the robot 414 to photograph a book and converts it into text data using OCR technology. The summarizing unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the text data using natural language processing technology, extracts important information, and creates a summary. The management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which combines the text data and summary and converts them into an e-book format. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] (Note 1) A scanning unit that scans books and converts them into text data, A summarization unit that summarizes the text data scanned by the scanning unit, The system includes a management unit that manages the data summarized by the summarization unit and converts it into an e-book format. A system characterized by the following features. (Note 2) The scanning unit is Convert to text data using OCR technology. The system described in Appendix 1, characterized by the features described herein. (Note 3) The summary section above is, Analyze text data using NLP techniques and create summaries. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned management department, Convert to ebook format The system described in Appendix 1, characterized by the features described herein. (Note 5) The summary section above is, Create a multilingual summary. The system described in Appendix 1, characterized by the features described herein. (Note 6) The summary section above is, Create a summary that is specialized for a particular field. The system described in Appendix 1, characterized by the features described herein. (Note 7) The scanning unit is It estimates the user's emotions and adjusts the timing of scans based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The scanning unit is The system detects the condition of the paper document and selects the optimal scanning method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The scanning unit is Apply different scanning algorithms depending on the type of book. The system described in Appendix 1, characterized by the features described herein. (Note 10) The scanning unit is It estimates the user's emotions and prioritizes the pages to scan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The scanning unit is Apply relevant scan settings considering the book's geographical publication information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The scanning unit is Analyze the book's social media ratings and prioritize scanning important pages. The system described in Appendix 1, characterized by the features described herein. (Note 13) The summary section above is, It estimates the user's emotions and adjusts the way the summary is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The summary section above is, Adjust the level of detail in the summary based on the importance of the text data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The summary section above is, Apply different summarization algorithms depending on the category of the text data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The summary section above is, It estimates the user's sentiment and adjusts the length of the summary based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The summary section above is, Prioritize summaries based on when the text data was created. The system described in Appendix 1, characterized by the features described herein. (Note 18) The summary section above is, Adjust the order of summaries based on the relevance of the text data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned management department, It estimates user sentiment and adjusts management methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned management department, Select the optimal management method by referring to the past management history of the text data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned management department, Customize management methods based on the current state of text data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned management department, It estimates user sentiment and determines management priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned management department, Select the optimal management method for text data, taking into account its geographical publication information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned management department, Analyzing the social media evaluation of text data and proposing management methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0172] 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 scanning unit that scans books and converts them into text data, A summarization unit that summarizes the text data scanned by the scanning unit, The system includes a management unit that manages the data summarized by the summarization unit and converts it into an e-book format. A system characterized by the following features.

2. The scanning unit is Convert to text data using OCR technology. The system according to feature 1.

3. The summary section above is, Analyze text data using NLP techniques and create summaries. The system according to feature 1.

4. The aforementioned management department, Convert to ebook format The system according to feature 1.

5. The summary section above is, Create a multilingual summary. The system according to feature 1.

6. The summary section above is, Create a summary that is specialized for a particular field. The system according to feature 1.

7. The scanning unit is It estimates the user's emotions and adjusts the timing of scans based on the estimated emotions. The system according to feature 1.

8. The scanning unit is The system detects the condition of the paper document and selects the optimal scanning method. The system according to feature 1.

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