System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting a reading goal; means for uploading electronic data of a book; means for analyzing contents of the uploaded book and dividing the contents into chapters; means for generating a personalized reading plan based on a user's profile and book information; means for providing the generated reading plan to the user; means for periodically generating questions for evaluating the user's reading progress and comprehension; and means for evaluating the user's answers to the generated questions and providing feedback.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In today's information-saturated age, many people recognize the usefulness of reading, but often are unable to finish a book due to lack of time or difficulty in understanding. Furthermore, it is becoming increasingly difficult to maintain the concentration required for long periods of time. As a result, the decline in reading volume leads to a shrinking market size and a decline in writers' motivation. Therefore, a system is needed that reduces the pain felt when reading, such as redundancy and a lack of prior knowledge, thereby shortening the time spent reading and increasing the amount of reading, while maintaining the amount of information gained. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for inputting reading goals, a means for uploading electronic book data, a means for analyzing the content of the uploaded book and dividing it into chapters, a means for generating an individual reading plan based on a user's profile and book information, a means for providing the generated reading plan to the user, a means for periodically generating questions to assess the user's reading progress and comprehension, and a means for evaluating the user's answers to the generated questions and providing feedback. This allows the user to read efficiently and is expected to increase the amount of reading they do. Furthermore, by further including a means for the user to send a request for a summary or a simple explanation of a specific chapter, and a means for supporting the user's progress in progressing to the next chapter based on the user's progress, more effective reading support can be achieved.

[0006] "Reading goals" refer to the purpose or goal that a user wants to achieve through reading.

[0007] "Electronic book data" refers to the data format in which the contents of a book are stored electronically (such as PDF or ePub).

[0008] "Upload" refers to the act of a user sending files or data stored on their own device to a server.

[0009] "Analysis" refers to the process of analyzing the content of an uploaded book and dividing it into specific chapters or sections.

[0010] A "user profile" refers to data that compiles information about a user (such as name, email address, reading goals, etc.).

[0011] "Reading plan" refers to a plan created based on a user's profile and book information to support efficient reading progress.

[0012] "Reading progress" refers to how far the user has progressed in reading a book.

[0013] "Comprehension" refers to an index that indicates how well a user understands what they have read.

[0014] "Question" refers to a question created to assess a user's understanding.

[0015] An "answer" refers to a response given by a user to a question.

[0016] "Feedback" refers to evaluations and advice given based on the user's answers. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11]FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] This invention relates to a custom-made reading support system that utilizes generative AI, enabling users to efficiently progress through reading according to their reading goals. Specific embodiments of this system are described below.

[0039] User Registration and Profiling

[0040] The server provides a new user registration form.

[0041] The user inputs his / her name, email address, and reading goal, and submits the information. For example, the name is "Taro Tanaka," the email address is "taro@example.com," and the reading goal is "improving business knowledge."

[0042] The server receives this information and creates a user profile, which is stored in a database.

[0043] Book information input and analysis

[0044] The server provides a form for uploading book titles, authors and electronic data files.

[0045] Users enter information about the book they want to read and upload the electronic data file. For example, the title might be "Theory and Practice of Business Strategy," the author might be "Ichiro Suzuki," and the electronic data file might be "business_strategy.pdf."

[0046] The server receives this data and begins analyzing it, which involves splitting the uploaded book content into chapters. The results of this analysis are also stored in the database.

[0047] Providing customized reading support

[0048] The server generates a personalized reading plan based on the user's profile and book information. This reading plan includes a plan to help the user efficiently progress through the book, including chapter summaries and simple descriptions.

[0049] The server provides this reading plan to the user.

[0050] The user begins reading based on the plan provided, and while reading a particular chapter, can request a summary or simple explanation.

[0051] Reading progress and comprehension assessment

[0052] The server periodically generates questions to assess the user's reading progress, which are used to verify the user's level of comprehension.

[0053] The user answers questions from the server and submits them.

[0054] The server evaluates these answers and provides feedback, for example assessing whether the user has understood the key concepts of a particular chapter and helping them progress to the next chapter based on the results.

[0055] Specific examples

[0056] Consider a scenario in which a user, Taro Tanaka, reads a book called "Theory and Practice of Business Strategy" to improve his business knowledge. Taro Tanaka logs in to the system, sets his reading goals, and then uploads the book's title, author, and electronic data. The server analyzes the data and generates and provides a reading plan that includes summaries and explanations of each chapter. Taro Tanaka reads efficiently according to this plan and periodically answers questions from the server to check his understanding. The server evaluates his answers and provides feedback to support Taro Tanaka's reading.

[0057] This system allows Taro Tanaka to read efficiently and acquire the necessary knowledge in a short time. In addition, by lowering the barrier to reading, it is expected that the amount of reading will increase and the market will expand.

[0058] The processing flow will be explained below.

[0059] Step 1:

[0060] Users enter their name, email address, and reading goal into a registration form and submit it.

[0061] The server receives this registration information, creates a user profile, and stores it in a database.

[0062] Step 2:

[0063] The server provides a form that accepts book titles, authors, and uploads of electronic data files.

[0064] The user enters information about the book they want to read and uploads the electronic data file.

[0065] Step 3:

[0066] The server receives the uploaded electronic data and begins analyzing the book contents.

[0067] The server divides the book into chapters and stores the data of the divided chapters in a database.

[0068] Step 4:

[0069] The server generates a personalized reading plan based on the user's profile and book data.

[0070] This reading plan includes summaries and simple explanations for each chapter.

[0071] Step 5:

[0072] The server provides the generated reading plan to the user.

[0073] The user begins reading according to the provided reading plan.

[0074] Step 6:

[0075] A user submits a request for a summary or simple description of a particular chapter.

[0076] The server receives the request and generates the necessary summary or explanation to provide to the user.

[0077] Step 7:

[0078] The server periodically generates questions to assess the user's reading progress and comprehension.

[0079] The server sends the generated question to the user.

[0080] Step 8:

[0081] The user answers questions posed by the server.

[0082] The server receives the user's answers and evaluates their understanding.

[0083] Step 9:

[0084] The server generates and provides feedback to the user based on the user's answers.

[0085] This feedback allows the user to get advice on how to proceed to the next chapter or improve their understanding.

[0086] Example 1

[0087] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0088] Conventional reading support systems have difficulty providing efficient reading plans tailored to individual users' reading goals, and are inadequate in properly evaluating and providing feedback on users' reading progress and comprehension. They also lack the functionality to request summaries of specific chapters or simple explanations, hindering effective learning. Furthermore, it is difficult to provide personalized support based on user profiles, and the process of analyzing large amounts of book information is time-consuming.

[0089] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0090] In this invention, the server includes: a means for creating a user profile; a means for inputting reading goals; a means for uploading electronic book data; a means for analyzing the uploaded book content and dividing it into chapters; a means for generating an individual reading plan based on the user profile and book information; a means for providing the generated reading plan to the user; a means for periodically generating questions to assess the user's reading progress and comprehension; a means for evaluating the user's answers to the generated questions and providing feedback; and a means for generating reading plans and questions using a generative AI model. This allows for the provision of an efficient reading plan tailored to the user's individual reading goals, and for appropriate evaluation and feedback of the user's reading progress and comprehension. Furthermore, the server also provides a function for requesting summaries and simplified explanations of specific chapters, thereby supporting effective learning for the user.

[0091] "User Profile" refers to data containing a user's personal information and reading goals.

[0092] "Reading goal" refers to the purpose or goal a user wishes to achieve in reading.

[0093] "Electronic Book Data" refers to a file containing the contents of a Book stored in digital form.

[0094] "Upload" refers to the act of a user sending data from their own device to a server.

[0095] "Analysis" refers to the process of dividing the received electronic data of a book into content and extracting information.

[0096] A "chapter" refers to a section into which the contents of a book are divided.

[0097] "Reading plan" refers to data that includes a plan for efficiently progressing through reading in accordance with the user's reading goals.

[0098] "Question" refers to a question generated to assess a user's reading progress and comprehension.

[0099] "Feedback" refers to information about improvements and next steps provided based on the user's responses.

[0100] A "generative AI model" refers to an algorithm that uses artificial intelligence to automatically handle specific tasks.

[0101] A "summary" is a brief summary of the contents of each chapter of a book.

[0102] "Simple expression" refers to an explanation that simplifies complex content to make it easy to understand.

[0103] "Request" means a request sent by a User to a Server for particular information or functionality.

[0104] "Progress" refers to the process and progress of the user's reading.

[0105] "Support" refers to assistance provided to a user in taking the next step.

[0106] This invention relates to a custom-made reading support system that utilizes generative AI models. This system can efficiently guide users through reading according to their reading goals, and can appropriately evaluate the user's reading progress and comprehension and provide feedback.

[0107] Hardware and Software Configuration

[0108] The server acts as a web server, receiving requests from users and processing them accordingly. The server is built using, for example, the Python Flask framework. The database uses SQLAlchemy to manage user profiles and book information.

[0109] Users access the server from a browser using a device connected to the Internet (e.g., a PC, smartphone, or tablet).

[0110] Processing Details

[0111] First, the server presents a new user registration form, which contains fields where the user can enter their name, email address, and reading goal. After the user enters the information in the form and clicks the submit button, the server receives the data and creates a user profile, which is then stored in a database.

[0112] The server then provides a form for uploading the book title, author, and electronic data file. The user enters the book information in this form and uploads the electronic data, such as a PDF file. The server receives the uploaded data and uses the Python PyMuPDF library to parse the book and split it into chapters. The results of this analysis are also stored in the database.

[0113] The server then generates a personalized reading plan based on the user profile and book information. This plan uses OpenAI's GPT-3 API to prompt a generative AI model to generate summaries and simplified explanations. The generated reading plan is then provided to the user, for example, in PDF format or as a web page.

[0114] As the user reads, the server periodically generates questions to assess the user's progress. These questions are also generated using a generative AI model. The user answers the questions provided by the server, and the answers are sent to the server. The server evaluates the received answers using a natural language processing algorithm and provides feedback to the user. Based on this feedback, the server supports the user in progressing to the next chapter.

[0115] Specific examples

[0116] For example, consider a user who has a goal to "improve his business knowledge." He uploads a book called "Theory and Practice of Business Strategy" to the system. The server analyzes the book and generates summaries for each chapter. An example prompt for the generated code might look like this:

[0117] "Generate a summary of Chapter 1 of Business Strategy Theory and Practice."

[0118] "Generate questions for users to assess their reading progress."

[0119] "Please explain how the new reading plan will help Taro Tanaka improve his business knowledge."

[0120] In this way, the system provides efficient reading assistance tailored to the user's specific reading goals.

[0121] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0122] Step 1: Provide a user registration form

[0123] The server provides a web form for user registration using HTML and CSS.

[0124] The user enters profile information such as their name, email address, and desired genre of reading into the form and clicks the submit button. For example, the user enters their name "Yamada Taro," their email address "yamada@example.com," and their reading goal "skill acquisition."

[0125] (Input) Name, email address, and reading goal entered by the user

[0126] (Output) User registration data received by the server

[0127] The server stores the received data in a database using SQLAlchemy.

[0128] Step 2: Provide a book information input form

[0129] The server provides a form for uploading the book title, author, and PDF file.

[0130] The user enters the title of the book they want to read, the author's name, and the PDF file into the form and clicks the upload button. For example, enter the title "Technology Basics," the author "Ichiro Sato," and the PDF file "tech_basics.pdf."

[0131] (Input) User-entered book title, author name, and uploaded PDF file

[0132] (Output) Book information and electronic data files received by the server

[0133] The server analyzes the received PDF file using Python's PyMuPDF library and divides the content into chapters. The analysis results are also stored in a database.

[0134] Step 3: Generate a Reading Plan

[0135] The server generates a personalized reading plan based on the user profile and the analyzed book information.

[0136] Specifically, it uses OpenAI's GPT-3 API to send prompt sentences summarizing the uploaded book to a generative model to obtain a summary.

[0137] (Input) User profile, book information, prompt for GPT-3

[0138] (Output) Generated summary and reading plan

[0139] For example, generate a summary of "Technology Fundamentals" and create a plan such as "Read one chapter per week."

[0140] Step 4: Provide a reading plan

[0141] The server provides the generated reading plan to the user.

[0142] Specifically, the results are published in PDF format or as a web page, and users are notified by email.

[0143] (Input) Generated reading plan

[0144] (Output) Links to the reading plans that the user can access

[0145] Users can view and download the reading plan from the provided link.

[0146] Step 5: Generate questions to assess reading progress

[0147] The server periodically generates questions to assess the user's reading progress.

[0148] These questions are also generated using generative AI models, for example, to create questions that assess key points based on what you've read.

[0149] (Input) User's reading progress data, prompt text for GPT-3

[0150] (Output) A list of generated questions

[0151] Users periodically answer provided questions and send their answers to the server.

[0152] Step 6: Rate answers and provide feedback

[0153] The server evaluates the received user responses using natural language processing algorithms.

[0154] For example, it analyzes whether the answers are accurate and how much they understand.

[0155] (Input) User response data

[0156] (Output) Evaluation results and feedback

[0157] The server provides the user with customized feedback based on the evaluation results, supporting their progress to the next chapter.

[0158] These are the specific processing steps of this system's program. This flow allows users to study efficiently according to their reading goals. In addition, by receiving appropriate feedback based on their progress, they can acquire knowledge effectively.

[0159] (Application example 1)

[0160] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0161] While support systems for efficient reading existed in the past, they were difficult to customize to meet the user's reading goals or to provide detailed evaluations of reading progress and comprehension. Furthermore, efficiently analyzing large volumes of book data and generating summaries required significant time and effort. The present invention aims to solve these problems by providing an optimal reading plan tailored to the user's reading goals and supporting efficient reading.

[0162] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0163] In this invention, the server includes: means for inputting reading goals; means for uploading electronic book data; means for analyzing the content of the uploaded book and dividing it into chapters; means for generating summaries of the uploaded book using a generative AI model; means for incorporating the generated summaries into a reading plan; means for generating an individual reading plan based on a user's profile and book information; means for providing the generated reading plan to the user; means for periodically generating questions to evaluate the user's reading progress and comprehension; and means for evaluating the user's answers to the generated questions and providing feedback. This enables the provision of an optimal reading plan based on the user's reading goals, as well as evaluation and feedback of the user's progress and comprehension.

[0164] A "reading goal" is a specific purpose or task related to reading that a user wants to achieve.

[0165] "Electronic data of a book" refers to content data of a book stored in electronic form.

[0166] "Analysis" refers to the process of dividing the uploaded book into chapters and analyzing the content to make it easier to understand.

[0167] A "generative AI model" is an artificial intelligence model that summarizes the contents of a book or generates information tailored to the user's requests.

[0168] A "reading plan" is a plan for efficient reading based on the user's reading goals and profile.

[0169] A "summary" is information that condenses the contents of a book and extracts only the main points.

[0170] A "profile" is a collection of data such as a user's attribute information and reading goals.

[0171] A "question" is a server-generated question used to assess a user's reading progress and comprehension.

[0172] "Feedback" is evaluation and advice provided based on the user's answers.

[0173] This invention relates to a custom-made reading support system that utilizes generative AI, and enables users to efficiently progress through reading according to their reading goals. A specific embodiment of this system will be described.

[0174] User Registration and Profiling

[0175] The server provides a new user registration form. The user enters their name, email address, and reading goal, and submits it. For example, the name might be "Yamada Taro," the email address might be "taro@example.com," and the reading goal might be "improving business knowledge." The server receives this information and creates a user profile. This profile is stored in a database.

[0176] Book information input and analysis

[0177] The server provides a form for uploading the book title, author, and electronic data file. The user enters information about the book they want to read and uploads the electronic data file. For example, the title is "Theory and Practice of Business Strategy," the author is "Ichiro Suzuki," and the electronic data file is "business_strategy.pdf." The server receives this data and begins analysis. The analysis includes dividing the contents of the uploaded book into chapters. It also uses a generative AI model to generate a summary of the uploaded book. The analysis results and summary information are also stored in the database.

[0178] Providing customized reading support

[0179] The server generates an individual reading plan based on the user's profile and book information. This reading plan includes a plan for the user to efficiently progress through the book. For example, it includes summaries and simple explanations of each chapter. The user begins reading based on this reading plan. While reading a specific chapter, the user can request summaries and simple explanations.

[0180] Reading progress and comprehension assessment

[0181] The server periodically generates questions to assess the user's reading progress. These questions are designed to confirm the user's level of understanding. The user answers the questions from the server and submits them. The server evaluates these answers and provides feedback. For example, the server may evaluate whether the user has understood the key concepts of a particular chapter and help the user progress to the next chapter based on the results.

[0182] Hardware and software used

[0183] Hardware: Server (SQLite for database and server-side processing)

[0184] software:

[0185] SQLite: Used as a local database to manage user information, book information, and reading plans.

[0186] Transformers: Generate book summaries using the Hugging Face pipeline.

[0187] Examples and prompts

[0188] As a concrete example, let us consider a scenario in which a user named "Yamada Taro" uploads the book "Theory and Practice of Business Strategy" to the app with the goal of "improving his business knowledge." The app generates a summary of the book and provides it as a reading plan. Yamada Taro reads efficiently according to this plan, and periodically answers questions from the server to check his level of understanding.

[0189] An example of a prompt for the generative AI model is as follows:

[0190] Summarize the book's contents:

[0191] Book Title: Business Strategy Theory and Practice

[0192] Content: <Book content text uploaded here>

[0193] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0194] Step 1:

[0195] The server presents a new user registration form. The user enters their name, email address, and reading goal, then submits it. Based on the information entered, the server creates a user profile and stores it in a SQLite database.

[0196] Input: User's name, email address, reading goal

[0197] Output: User profile stored in the database

[0198] Step 2:

[0199] The server provides a form for uploading the book title, author, and electronic data file. The user enters the information about the book they want to read and uploads the electronic data file. The server receives this data and begins analyzing it.

[0200] Input: Book title, author, electronic data file

[0201] Output: Book data for analysis

[0202] Step 3:

[0203] The server then processes the uploaded book by dividing it into chapters. In this step, it analyzes the text in the book's electronic data file and performs data calculations to divide it into chapters. The analysis results are stored in a database.

[0204] Input: Contents of electronic data file

[0205] Output: Book data split by chapters

[0206] Step 4:

[0207] The server uses a generative AI model (such as the Hugging Face pipeline) to generate a summary for each chapter. This process takes the book's contents as input and performs data calculations to generate a summary. The generated summary is also stored in a database.

[0208] Input: Book data divided into chapters

[0209] Output: Summary of each chapter

[0210] Step 5:

[0211] The server generates a personalized reading plan based on the user's profile and book information, including chapter summaries and simple descriptions, and stores the plan in a database and provides it to the user.

[0212] Input: User profile, chapter summaries

[0213] Output: Individual Reading Plans

[0214] Step 6:

[0215] The user begins reading according to the personalized reading plan provided by the server. If a summary or brief explanation is needed during the reading, the user sends a request to the server, and the server responds by providing additional information.

[0216] Input: Reading plan, user request

[0217] Output: Additional information needed

[0218] Step 7:

[0219] The server periodically generates questions to assess the user's reading progress, and delivers the questions to the user, who then answers them.

[0220] Input: User's reading progress

[0221] Output: Assessment questions

[0222] Step 8:

[0223] The server receives and evaluates the user's answers, provides feedback based on the evaluation results, and supports the user in progressing to the next chapter.

[0224] Input: User's answer

[0225] Output: Evaluation results, feedback

[0226] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0227] This invention relates to a custom-made reading support system that utilizes generative AI and an emotion engine, and enables users to efficiently progress through reading according to their reading goals. Specific embodiments of this system are described below.

[0228] User Registration and Profiling

[0229] The server provides a new user registration form.

[0230] The user inputs his / her name, email address, and reading goal, and submits the information. For example, the name is "Taro Tanaka," the email address is "taro@example.com," and the reading goal is "improving business knowledge."

[0231] The server receives this information and creates a user profile, which is stored in a database.

[0232] Book information input and analysis

[0233] The server provides a form for uploading book titles, authors and electronic data files.

[0234] Users enter information about the book they want to read and upload the electronic data file. For example, the title might be "Theory and Practice of Business Strategy," the author might be "Ichiro Suzuki," and the electronic data file might be "business_strategy.pdf."

[0235] The server receives this data and begins analyzing it, which involves splitting the uploaded book content into chapters. The results of this analysis are also stored in the database.

[0236] Providing customized reading support

[0237] The server generates a personalized reading plan based on the user's profile and book information. This reading plan includes a plan to help the user efficiently progress through the book, including chapter summaries and simple descriptions.

[0238] The server provides this reading plan to the user.

[0239] The user begins reading based on the plan provided, and while reading a particular chapter, can request a summary or simple explanation.

[0240] Use of emotion engine

[0241] The server uses an emotion engine to recognize the user's emotions when they submit a request for a summary or simple explanation of a particular chapter. This emotion data is used to improve the user's reading experience. For example, if the user is struggling to understand, the server can provide a more concise explanation or additional examples.

[0242] Reading progress and comprehension assessment

[0243] The server periodically generates questions to assess the user's reading progress, which are used to verify the user's level of comprehension.

[0244] The user answers questions from the server and submits them.

[0245] The server receives these responses and also evaluates the user's emotions using an emotion engine, for example encouraging the user to proceed to the next chapter if they are excited.

[0246] Providing Feedback

[0247] The server generates feedback based on the user's answers and sentiment data to improve the user's reading experience, including advice on next steps and improving comprehension.

[0248] The user receives this feedback and decides to proceed to the next chapter.

[0249] Specific examples

[0250] Consider a scenario in which user Tanaka Taro is reading a book called "Theory and Practice of Business Strategy" to improve his business knowledge. Tanaka Taro logs in to the system, sets his reading goals, and then uploads the book's title, author, and electronic data. The server analyzes the data and generates and provides a reading plan that includes summaries and explanations for each chapter. Tanaka Taro reads efficiently according to this plan, and if he has difficulty understanding a particular chapter, he requests a simpler explanation through the emotion engine. The server responds by providing additional explanation. Periodically, the server checks Tanaka Taro's progress and comprehension in the form of questions and evaluates his emotion data using the emotion engine. Based on the results, the server provides feedback to optimize Tanaka Taro's reading experience.

[0251] This system allows Taro Tanaka to read efficiently and acquire the necessary knowledge in a short time. In addition, by utilizing the emotion engine, it is possible to provide optimal support according to individual needs and emotions.

[0252] The processing flow will be explained below.

[0253] This invention relates to a custom-made reading support system that utilizes generative AI and an emotion engine, allowing users to efficiently progress through reading according to their reading goals. A specific embodiment of this system will be described below, divided into processing steps.

[0254] Step 1:

[0255] The user enters his / her name, email address, and reading goal in the registration form and submits it. For example, the name is "Taro Tanaka," the email address is "taro@example.com," and the reading goal is "improving business knowledge."

[0256] The server receives this registration information, creates a user profile, and stores it in a database.

[0257] Step 2:

[0258] The server provides a form that accepts book titles, authors, and uploads of electronic data files.

[0259] Users enter information about the book they want to read and upload the electronic data file. For example, the title might be "Theory and Practice of Business Strategy," the author might be "Ichiro Suzuki," and the electronic data file might be "business_strategy.pdf."

[0260] Step 3:

[0261] The server receives the uploaded electronic data and begins analyzing the book contents.

[0262] The server divides the book into chapters and stores the data of the divided chapters in a database.

[0263] Step 4:

[0264] The server generates a personalized reading plan based on the user's profile and book data.

[0265] This reading plan includes summaries and simple explanations for each chapter.

[0266] Step 5:

[0267] The server provides the generated reading plan to the user.

[0268] The user begins reading according to the provided reading plan.

[0269] Step 6:

[0270] A user submits a request for a summary or simple description of a particular chapter, for example, "Please give me a summary of Chapter 1."

[0271] The server receives the request, generates the necessary summary and explanation, and provides it to the user, including using an emotion engine to analyze the user's emotion at the time of the request and providing it in the most appropriate format based on that emotion.

[0272] Step 7:

[0273] The server periodically generates questions to assess the user's reading progress and comprehension, such as "What are the key strategies discussed in Chapter 1?"

[0274] The server sends the generated question to the user.

[0275] Step 8:

[0276] The user answers questions from the server, for example, "The primary strategy is cost leadership."

[0277] The server receives the user's answers and evaluates their understanding.

[0278] Step 9:

[0279] The server generates and provides feedback to the user based on the user's answers.

[0280] This feedback allows the user to get advice on how to proceed to the next chapter or improve their understanding.

[0281] Step 10:

[0282] The emotion engine constantly monitors the user's emotions while reading, and if it detects that the user is feeling stressed, it will provide simple expressions or suggest taking a break, thereby improving the user's reading experience.

[0283] Specific examples

[0284] Consider a scenario in which user Tanaka Taro is reading a book called "Theory and Practice of Business Strategy" to improve his business knowledge. Tanaka Taro logs in to the system, sets his reading goals, and then uploads the book's title, author, and electronic data. The server analyzes the data and generates and provides a reading plan that includes summaries and explanations for each chapter. Tanaka Taro reads efficiently according to this plan, and if he has difficulty understanding a particular chapter, he requests a simpler explanation through the emotion engine. The server responds by providing additional explanation. The server periodically checks Tanaka Taro's progress and level of understanding in the form of questions and evaluates his emotion data at that time using the emotion engine. Based on the results, the server provides feedback to optimize Tanaka Taro's reading experience.

[0285] Example 2

[0286] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0287] Conventional reading support systems do not consider how to efficiently progress through reading, and in particular lack individualized support based on the user's reading goals and emotional state. They also lacked measures to address difficulties when users were having difficulty understanding the content of a book, and did not provide effective feedback to optimize the user's reading experience. Furthermore, they lacked a means to regularly evaluate the user's reading progress and comprehension and provide appropriate feedback.

[0288] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0289] In this invention, the server includes means for inputting reading goals, means for uploading electronic data of books, means for analyzing the content of the uploaded books and dividing them into chapters, means for generating an individual reading plan based on the user's profile and book information, means for providing the generated reading plan to the user, means for accepting requests from the user for summaries and brief explanations, means for generating responses to the accepted requests using a sentiment analysis engine, means for periodically generating questions to evaluate the user's reading progress and comprehension, and means for evaluating the user's answers to the generated questions and providing feedback, thereby optimizing the user's reading experience and enabling efficient reading progress, support for comprehension, and feedback tailored to individual needs.

[0290] The "means for inputting reading goals" is an interface that allows users to input their own reading purposes and goals into the system.

[0291] The "means for uploading electronic book data" is an interface that allows users to upload electronic files of books they wish to read to the system.

[0292] The "means for analyzing the contents of the uploaded book and dividing it into chapters" refers to an algorithm or program that analyzes the contents of the e-book file and divides it into chapters.

[0293] The "means for generating an individual reading plan based on a user's profile and book information" is a program for automatically creating an efficient reading plan based on user information and book contents.

[0294] The "means for providing the generated reading plan to the user" refers to an interface or notification function for presenting the generated reading plan to the user.

[0295] The "means for accepting a request for a summary or a brief explanation from a user" is an interface that allows a user to request additional information or a brief explanation of the content from the system.

[0296] The "means for generating a response using an emotion analysis engine" refers to an algorithm or program for analyzing a user's emotion and level of understanding and generating an appropriate response.

[0297] "Means for generating questions to periodically assess a user's reading progress and level of comprehension" refers to a program that enables the system to automatically generate questions to check a user's reading progress and level of comprehension.

[0298] The "means for evaluating the user's answers to the generated questions and providing feedback" is a program that analyzes the user's answers and provides appropriate advice and recommendations for the next learning step.

[0299] The present invention relates to a custom-made reading support system that uses generative AI and a sentiment analysis engine, and enables users to efficiently progress through reading according to their reading goals. Specific embodiments of this system are described below.

[0300] This system operates according to the following steps: Each process is executed by either the server, the terminal, or the user.

[0301] User Registration and Profiling

[0302] The server provides a new user registration form, which includes fields for name, email address, and reading goal.

[0303] The user inputs and submits this information. For example, the user's name is "Yamada Taro," the user's email address is "taro@example.com," and the user's reading goal is "improving business knowledge."

[0304] The server creates a user profile based on the received information and stores it in a database.

[0305] Book information input and analysis

[0306] The server provides a form for uploading book titles, authors and electronic data files.

[0307] The user enters information about the book they want to read and uploads the electronic data file. For example, the title is "Theory and Practice of Business Strategy," the author is "Ichiro Sato," and the electronic data file is "business_strategy.pdf."

[0308] The server receives this data, analyzes the contents of the book using AI, and divides it into chapters. The results of this analysis are also stored in a database.

[0309] Providing customized reading support

[0310] The server generates a personalized reading plan based on the user's profile and book information, including chapter summaries and simple descriptions.

[0311] The server provides this reading plan to the user.

[0312] The user begins reading based on the plan provided, and while reading a particular chapter, can request a summary or simple explanation.

[0313] Use of emotion engine

[0314] The server uses a sentiment analysis engine to recognize the user's emotions when they submit a request for a summary or simple explanation of a particular chapter. This emotional data is used to improve the user's reading experience. For example, if the user is struggling to understand, the server can provide a more concise explanation or additional examples.

[0315] Reading progress and comprehension assessment

[0316] The server periodically generates questions to assess the user's reading progress, which are used to verify the user's level of comprehension.

[0317] The user answers questions from the server and submits them.

[0318] The server receives these responses and uses a sentiment analysis engine to evaluate the user's emotions as well, for example encouraging them to move on to the next chapter if they are excited.

[0319] Providing Feedback

[0320] The server generates feedback based on the user's answers and sentiment data, including advice on next steps to take or improve understanding.

[0321] The user receives this feedback and decides to proceed to the next chapter.

[0322] Specific examples

[0323] As an example, consider a scenario in which user Yamada Taro is reading a book called "Theory and Practice of Business Strategy" to improve his business knowledge. Yamada Taro logs in to the system, sets his reading goals, and then uploads the book's title, author, and electronic data. The server analyzes the data and generates and provides a reading plan that includes summaries and explanations for each chapter. Yamada Taro reads efficiently according to this plan, and if he has difficulty understanding a particular chapter, he requests a simpler explanation through the emotion engine. The server responds by providing additional explanation. Periodically, the server checks Yamada Taro's progress and level of understanding in the form of questions and evaluates the emotional data using the emotion analysis engine. Based on the results, it provides feedback to optimize Yamada Taro's reading experience.

[0324] Usage example (example of prompt for generative AI model)

[0325] Please provide a brief summary of Chapter 1 of "The Theory and Practice of Business Strategy."

[0326] I find this chapter difficult. Can you use the Emotion Engine to provide a simpler explanation?

[0327] Generate feedback that encourages progression to the next chapter.

[0328] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0329] Step 1:

[0330] The server presents a new user registration form. The user enters their name, email address, and reading goals, and submits it. The server receives this information, generates a user profile, and stores it in a database.

[0331] Input: User's name, email address, reading goal

[0332] Output: User profile (e.g. user ID, name, email address, reading goal)

[0333] What happens: The server generates a user profile and stores it in a database.

[0334] Step 2:

[0335] The server provides a form for uploading the book title, author, and electronic data file. The user enters this information and uploads the electronic data file. The server receives this data and begins analyzing it.

[0336] Input: Book title, author, electronic data file

[0337] Output: Contents of each chapter of the book (analysis results)

[0338] Specific operation: The server analyzes the electronic data file, divides the book contents into chapters, and stores the analysis results in a database.

[0339] Step 3:

[0340] The server generates an individual reading plan based on the user profile and book information, provides the generated reading plan to the user, and the user begins reading based on this plan.

[0341] Input: User profile, book information (analysis results)

[0342] Output: Individual reading plan (e.g., chapter summaries, brief descriptions)

[0343] Specific behavior: The server creates a reading plan and provides it through the user interface.

[0344] Step 4:

[0345] When a user is reading a particular chapter, they can request a summary or simple explanation. The server receives this request and uses a sentiment analysis engine to recognize the user's sentiment.

[0346] Input: User request (request for summary or brief explanation)

[0347] Output: User's emotional state and response (e.g., a more concise explanation, additional examples)

[0348] Specific operation: The server analyzes the user's emotions using an emotion analysis engine and generates an appropriate response.

[0349] Step 5:

[0350] The server periodically generates questions to assess the user's reading progress. The user answers the questions and submits them. The server receives these answers and evaluates the user's emotions using a sentiment analysis engine.

[0351] Input: User progress, answers to comprehension questions

[0352] Output: User comprehension assessment and sentiment data

[0353] What it does: The server generates questions and presents them to the user. The user submits answers, which the server then evaluates.

[0354] Step 6:

[0355] The server generates feedback based on the user's answers and emotional data, including advice on next steps or improving comprehension. The user receives this feedback and decides to proceed to the next chapter.

[0356] Input: User responses, emotion data

[0357] Output: Feedback (e.g., suggestions for next chapter, advice for improving comprehension)

[0358] What happens: The server generates feedback and provides it to the user.

[0359] (Application example 2)

[0360] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0361] Existing reading support systems face challenges in providing optimal support that takes into account the individual needs and emotional state of each user. It is particularly difficult to provide effective feedback when a user is struggling to understand a particular chapter or is behind in their progress. Another issue is the lack of real-time feedback that utilizes emotional data when assessing reading progress and comprehension.

[0362] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for inputting reading goals, a means for uploading electronic book data, a means for analyzing the content of the uploaded book and dividing it into chapters, a means for generating an individual reading plan based on the user's profile and book information, a means for providing the generated reading plan to the user, a means for periodically generating questions to assess the user's reading progress and comprehension, a means for evaluating the user's answers to the generated questions and providing feedback, a means for analyzing the user's emotional state using an emotion engine, and a means for optimizing feedback based on the analyzed emotion data. This enables optimal support based on the user's individual needs and emotional state, allowing for efficient reading. Furthermore, utilizing real-time emotion data enables accurate assessment of reading progress and comprehension, enabling effective feedback.

[0363] "Means for inputting reading goals" refers to an interface that allows users to input their reading goals and the objectives they wish to achieve into the system.

[0364] "Means for uploading electronic book data" is a function that allows users to upload electronic book data files to the system.

[0365] "Means for analyzing the contents of uploaded books and dividing them into chapters" refers to a function in which the system analyzes the electronic data of uploaded books and divides and organizes the contents into chapters.

[0366] "Means for generating an individual reading plan based on the user's profile and book information" refers to the function by which the system automatically generates a reading plan optimized for each user based on the user's profile information and the content of the book.

[0367] "Means for providing the user with the generated reading plan" refers to the function by which the system presents the user with the generated individual reading plan and allows the user to proceed with their reading based on it.

[0368] "Means for generating questions to periodically assess a user's reading progress and comprehension" refers to a function that allows the system to generate questions to check a user's comprehension and progress according to a certain period of time or reading progress.

[0369] "Means for evaluating the user's answers to the generated questions and providing feedback" refers to a function that analyzes the answers given by the user to the questions, evaluates their reading progress and level of understanding based on that, and provides appropriate feedback.

[0370] "Means for analyzing the user's emotional state using an emotion engine" refers to the function by which the system recognizes and analyzes emotions from the user's input and actions, and acquires that data.

[0371] The "means for optimizing feedback based on analyzed emotional data" is a function that provides feedback and support according to the emotional state of the user based on the analyzed emotional data.

[0372] This invention relates to a custom-made reading support system that utilizes generative AI and an emotion engine, allowing users to efficiently progress through reading according to their reading goals. This system is mainly composed of a server and a terminal, and operates as follows:

[0373] User Registration and Profiling

[0374] The server provides a new user registration form. The user enters their name, email address, and reading goal through their terminal and submits it. For example, the name is "Reader A," the email address is "reader@example.com," and the reading goal is "Improvement of Expertise." The server receives this information and creates a user profile. This profile is stored in a database.

[0375] Book information input and analysis

[0376] The server provides a form for uploading the book title, author, and electronic data file. The user enters the information of the book they want to read through their terminal and uploads the electronic data file. For example, the title is "Theory and Practice of Specialized Knowledge," the author is "Author B," and the electronic data file is "specialized_knowledge.pdf." The server receives this data and begins analysis. The analysis includes dividing the content of the uploaded book into chapters. The analysis results are also stored in the database.

[0377] Providing customized reading support

[0378] The server generates an individual reading plan based on the user's profile and book information. This reading plan includes a plan for the user to efficiently progress through the book. For example, it may include summaries and simple explanations of each chapter. The server provides this reading plan to the user. The user can start reading based on the plan provided and request summaries and simple explanations while reading a specific chapter.

[0379] Use of emotion engine

[0380] The server uses an emotion engine to recognize the user's emotions when they submit a request for a summary or brief explanation of a particular chapter. This emotion data is used to improve the user's reading experience. For example, if the user is having difficulty understanding, the server can provide a more concise explanation or additional examples.

[0381] Reading progress and comprehension assessment

[0382] The server periodically generates questions to evaluate the user's reading progress. These questions are intended to confirm the user's level of understanding. The user answers the questions from the server and submits them via their device. The server receives these answers and evaluates the user's emotions using an emotion engine. For example, if the user is excited, it encourages them to move on to the next chapter.

[0383] Providing Feedback

[0384] The server generates feedback based on the user's answers and sentiment data. This feedback is intended to improve the user's reading experience and includes advice on next steps and improving comprehension. The user receives this feedback and decides to proceed to the next chapter.

[0385] Specific examples

[0386] For example, consider a scenario in which a user, "Reader A," is reading a book called "Theory and Practice of Specialized Knowledge" to improve their expertise. "Reader A" logs in to the system, sets their reading goals, and then uploads the book's title, author, and electronic data. The server analyzes the data and generates and provides a reading plan that includes summaries and explanations for each chapter. "Reader A" reads efficiently according to this plan, and if they have difficulty understanding a particular chapter, they request a simpler explanation through the emotion engine. The server responds by providing additional explanation. The server periodically checks "Reader A's" progress and comprehension in the form of questions and evaluates their emotional data using the emotion engine. By providing feedback based on the results, it is possible to optimize "Reader A's" reading experience.

[0387] Specific examples of input prompts for the generative AI model used

[0388] For example, if "Reader A" finishes reading the first chapter and writes that he or she "doesn't understand," the following prompt can be used to connect with the emotion engine:

[0389] "User: Reader A. Goal: Improve expertise. Book: Theory and Practice of Expertise.

[0390] Reader A finished reading Chapter 1 and commented, "I don't understand it." Please analyze this sentiment with the emotion engine and generate feedback for the next step."

[0391] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0392] Step 1:

[0393] A user accesses the system through a terminal and displays a new user registration form. The user enters their name, email address, and reading goals and submits it. This information is sent to the server. The server creates a user profile based on the received information and stores it in a database. The input of this step is the user's personal information and reading goals, and the output is the user profile stored in the database.

[0394] Step 2:

[0395] The server provides a form for uploading the book title, author, and electronic data file. The user enters the book information through the terminal and uploads the electronic data file. This information is sent to the server. The server analyzes the received data and divides the book content into chapters. The analysis results are also stored in the database. The input of this step is the book information and electronic data file uploaded by the user, and the output is the analyzed book content for each chapter.

[0396] Step 3:

[0397] The server generates a personalized reading plan based on the user profile and book information, including chapter summaries and simple descriptions. The server then sends this reading plan to the user's device and displays it. The input for this step is the user profile and the parsed book content, and the output is the generated reading plan.

[0398] Step 4:

[0399] The user reads according to the reading plan provided through the terminal. While reading a particular chapter, the user sends a request to the server for a summary or simple explanation. The server receives this request and uses an emotion engine to analyze the user's emotion. The input of this step is the user's request, and the output is the analyzed emotion data.

[0400] Step 5:

[0401] The server provides the user with a more concise explanation or additional examples based on the emotion data. This feedback is sent to the user's device and displayed. The input of this step is the analyzed emotion data, and the output is the optimized feedback.

[0402] Step 6:

[0403] The server periodically generates questions to assess the user's reading progress, including questions to check comprehension. The questions are sent to the user's device and displayed. The input of this step is the user's reading progress, and the output is the generated questions.

[0404] Step 7:

[0405] The user answers questions through the terminal and sends the answers to the server. The server receives the answers and evaluates the user's emotions using the emotion engine. The input of this step is the user's answers, and the output is the evaluation result.

[0406] Step 8:

[0407] The server generates feedback based on the user's answers and emotional data. The feedback includes advice on how to proceed to the next step or improve understanding. The feedback is sent to the user's device and displayed. The inputs of this step are the evaluation results and the analyzed emotional data, and the output is the feedback.

[0408] Examples of use

[0409] The scenario involves "Reader A" starting to read a book called "Theory and Practice of Specialized Knowledge." "Reader A" logs into the system, sets a reading goal, and uploads the book's title, author, and electronic data. The server analyzes the data and generates and provides summaries and explanations for each chapter. If "Reader A" has difficulty understanding, the server responds to requests through the emotion engine and provides additional explanations. The server also periodically checks the progress and level of comprehension, evaluates the emotion data, and provides feedback.

[0410] Specific examples of input prompts for generative AI models

[0411] "User: Reader A. Goal: Improve expertise. Book: Theory and Practice of Expertise.

[0412] Reader A finished reading Chapter 1 and commented, "I don't understand it." Please analyze this sentiment with the emotion engine and generate feedback for the next step."

[0413] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0414] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0415] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0416] [Second embodiment]

[0417] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0418] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0419] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0420] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0421] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0422] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0423] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0424] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0425] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0426] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0427] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0428] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0429] This invention relates to a custom-made reading support system that utilizes generative AI, enabling users to efficiently progress through reading according to their reading goals. Specific embodiments of this system are described below.

[0430] User Registration and Profiling

[0431] The server provides a new user registration form.

[0432] The user inputs his / her name, email address, and reading goal, and submits the information. For example, the name is "Taro Tanaka," the email address is "taro@example.com," and the reading goal is "improving business knowledge."

[0433] The server receives this information and creates a user profile, which is stored in a database.

[0434] Book information input and analysis

[0435] The server provides a form for uploading book titles, authors and electronic data files.

[0436] Users enter information about the book they want to read and upload the electronic data file. For example, the title might be "Theory and Practice of Business Strategy," the author might be "Ichiro Suzuki," and the electronic data file might be "business_strategy.pdf."

[0437] The server receives this data and begins analyzing it, which involves splitting the uploaded book content into chapters. The results of this analysis are also stored in the database.

[0438] Providing customized reading support

[0439] The server generates a personalized reading plan based on the user's profile and book information. This reading plan includes a plan to help the user efficiently progress through the book, including chapter summaries and simple descriptions.

[0440] The server provides this reading plan to the user.

[0441] The user begins reading based on the plan provided, and while reading a particular chapter, can request a summary or simple explanation.

[0442] Reading progress and comprehension assessment

[0443] The server periodically generates questions to assess the user's reading progress, which are used to verify the user's level of comprehension.

[0444] The user answers questions from the server and submits them.

[0445] The server evaluates these answers and provides feedback, for example assessing whether the user has understood the key concepts of a particular chapter and helping them progress to the next chapter based on the results.

[0446] Specific examples

[0447] Consider a scenario in which a user, Taro Tanaka, reads a book called "Theory and Practice of Business Strategy" to improve his business knowledge. Taro Tanaka logs in to the system, sets his reading goals, and then uploads the book's title, author, and electronic data. The server analyzes the data and generates and provides a reading plan that includes summaries and explanations of each chapter. Taro Tanaka reads efficiently according to this plan and periodically answers questions from the server to check his understanding. The server evaluates his answers and provides feedback to support Taro Tanaka's reading.

[0448] This system allows Taro Tanaka to read efficiently and acquire the necessary knowledge in a short time. In addition, by lowering the barrier to reading, it is expected that the amount of reading will increase and the market will expand.

[0449] The processing flow will be explained below.

[0450] Step 1:

[0451] Users enter their name, email address, and reading goal into a registration form and submit it.

[0452] The server receives this registration information, creates a user profile, and stores it in a database.

[0453] Step 2:

[0454] The server provides a form that accepts book titles, authors, and uploads of electronic data files.

[0455] The user enters information about the book they want to read and uploads the electronic data file.

[0456] Step 3:

[0457] The server receives the uploaded electronic data and begins analyzing the book contents.

[0458] The server divides the book into chapters and stores the data of the divided chapters in a database.

[0459] Step 4:

[0460] The server generates a personalized reading plan based on the user's profile and book data.

[0461] This reading plan includes summaries and simple explanations for each chapter.

[0462] Step 5:

[0463] The server provides the generated reading plan to the user.

[0464] The user begins reading according to the provided reading plan.

[0465] Step 6:

[0466] A user submits a request for a summary or simple description of a particular chapter.

[0467] The server receives the request and generates the necessary summary or explanation to provide to the user.

[0468] Step 7:

[0469] The server periodically generates questions to assess the user's reading progress and comprehension.

[0470] The server sends the generated question to the user.

[0471] Step 8:

[0472] The user answers questions posed by the server.

[0473] The server receives the user's answers and evaluates their understanding.

[0474] Step 9:

[0475] The server generates and provides feedback to the user based on the user's answers.

[0476] This feedback allows the user to get advice on how to proceed to the next chapter or improve their understanding.

[0477] Example 1

[0478] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0479] Conventional reading support systems have difficulty providing efficient reading plans tailored to individual users' reading goals, and are inadequate in properly evaluating and providing feedback on users' reading progress and comprehension. They also lack the functionality to request summaries of specific chapters or simple explanations, hindering effective learning. Furthermore, it is difficult to provide personalized support based on user profiles, and the process of analyzing large amounts of book information is time-consuming.

[0480] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0481] In this invention, the server includes: a means for creating a user profile; a means for inputting reading goals; a means for uploading electronic book data; a means for analyzing the uploaded book content and dividing it into chapters; a means for generating an individual reading plan based on the user profile and book information; a means for providing the generated reading plan to the user; a means for periodically generating questions to assess the user's reading progress and comprehension; a means for evaluating the user's answers to the generated questions and providing feedback; and a means for generating reading plans and questions using a generative AI model. This allows for the provision of an efficient reading plan tailored to the user's individual reading goals, and for appropriate evaluation and feedback of the user's reading progress and comprehension. Furthermore, the server also provides a function for requesting summaries and simplified explanations of specific chapters, thereby supporting effective learning for the user.

[0482] "User Profile" refers to data containing a user's personal information and reading goals.

[0483] "Reading goal" refers to the purpose or goal a user wishes to achieve in reading.

[0484] "Electronic Book Data" refers to a file containing the contents of a Book stored in digital form.

[0485] "Upload" refers to the act of a user sending data from their own device to a server.

[0486] "Analysis" refers to the process of dividing the received electronic data of a book into content and extracting information.

[0487] A "chapter" refers to a section into which the contents of a book are divided.

[0488] "Reading plan" refers to data that includes a plan for efficiently progressing through reading in accordance with the user's reading goals.

[0489] "Question" refers to a question generated to assess a user's reading progress and comprehension.

[0490] "Feedback" refers to information about improvements and next steps provided based on the user's responses.

[0491] A "generative AI model" refers to an algorithm that uses artificial intelligence to automatically handle specific tasks.

[0492] A "summary" is a brief summary of the contents of each chapter of a book.

[0493] "Simple expression" refers to an explanation that simplifies complex content to make it easy to understand.

[0494] "Request" means a request sent by a User to a Server for particular information or functionality.

[0495] "Progress" refers to the process and progress of the user's reading.

[0496] "Support" refers to assistance provided to a user in taking the next step.

[0497] This invention relates to a custom-made reading support system that utilizes generative AI models. This system can efficiently guide users through reading according to their reading goals, and can appropriately evaluate the user's reading progress and comprehension and provide feedback.

[0498] Hardware and Software Configuration

[0499] The server acts as a web server, receiving requests from users and processing them accordingly. The server is built using, for example, the Python Flask framework. The database uses SQLAlchemy to manage user profiles and book information.

[0500] Users access the server from a browser using a device connected to the Internet (e.g., a PC, smartphone, or tablet).

[0501] Processing Details

[0502] First, the server presents a new user registration form, which contains fields where the user can enter their name, email address, and reading goal. After the user enters the information in the form and clicks the submit button, the server receives the data and creates a user profile, which is then stored in a database.

[0503] The server then provides a form for uploading the book title, author, and electronic data file. The user enters the book information in this form and uploads the electronic data, such as a PDF file. The server receives the uploaded data and uses the Python PyMuPDF library to parse the book and split it into chapters. The results of this analysis are also stored in the database.

[0504] The server then generates a personalized reading plan based on the user profile and book information. This plan uses OpenAI's GPT-3 API to prompt a generative AI model to generate summaries and simplified explanations. The generated reading plan is then provided to the user, for example, in PDF format or as a web page.

[0505] As the user reads, the server periodically generates questions to assess the user's progress. These questions are also generated using a generative AI model. The user answers the questions provided by the server, and the answers are sent to the server. The server evaluates the received answers using a natural language processing algorithm and provides feedback to the user. Based on this feedback, the server supports the user in progressing to the next chapter.

[0506] Specific examples

[0507] For example, consider a user who has a goal to "improve his business knowledge." He uploads a book called "Theory and Practice of Business Strategy" to the system. The server analyzes the book and generates summaries for each chapter. An example prompt for the generated code might look like this:

[0508] "Generate a summary of Chapter 1 of Business Strategy Theory and Practice."

[0509] "Generate questions for users to assess their reading progress."

[0510] "Please explain how the new reading plan will help Taro Tanaka improve his business knowledge."

[0511] In this way, the system provides efficient reading assistance tailored to the user's specific reading goals.

[0512] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0513] Step 1: Provide a user registration form

[0514] The server provides a web form for user registration using HTML and CSS.

[0515] The user enters profile information such as their name, email address, and desired genre of reading into the form and clicks the submit button. For example, the user enters their name "Yamada Taro," their email address "yamada@example.com," and their reading goal "skill acquisition."

[0516] (Input) Name, email address, and reading goal entered by the user

[0517] (Output) User registration data received by the server

[0518] The server stores the received data in a database using SQLAlchemy.

[0519] Step 2: Provide a book information input form

[0520] The server provides a form for uploading the book title, author, and PDF file.

[0521] The user enters the title of the book they want to read, the author's name, and the PDF file into the form and clicks the upload button. For example, enter the title "Technology Basics," the author "Ichiro Sato," and the PDF file "tech_basics.pdf."

[0522] (Input) User-entered book title, author name, and uploaded PDF file

[0523] (Output) Book information and electronic data files received by the server

[0524] The server analyzes the received PDF file using Python's PyMuPDF library and divides the content into chapters. The analysis results are also stored in a database.

[0525] Step 3: Generate a Reading Plan

[0526] The server generates a personalized reading plan based on the user profile and the analyzed book information.

[0527] Specifically, it uses OpenAI's GPT-3 API to send prompt sentences summarizing the uploaded book to a generative model to obtain a summary.

[0528] (Input) User profile, book information, prompt for GPT-3

[0529] (Output) Generated summary and reading plan

[0530] For example, generate a summary of "Technology Fundamentals" and create a plan such as "Read one chapter per week."

[0531] Step 4: Provide a reading plan

[0532] The server provides the generated reading plan to the user.

[0533] Specifically, the results are published in PDF format or as a web page, and users are notified by email.

[0534] (Input) Generated reading plan

[0535] (Output) Links to the reading plans that the user can access

[0536] Users can view and download the reading plan from the provided link.

[0537] Step 5: Generate questions to assess reading progress

[0538] The server periodically generates questions to assess the user's reading progress.

[0539] These questions are also generated using generative AI models, for example, to create questions that assess key points based on what you've read.

[0540] (Input) User's reading progress data, prompt text for GPT-3

[0541] (Output) A list of generated questions

[0542] Users periodically answer provided questions and send their answers to the server.

[0543] Step 6: Rate answers and provide feedback

[0544] The server evaluates the received user responses using natural language processing algorithms.

[0545] For example, it analyzes whether the answers are accurate and how much they understand.

[0546] (Input) User response data

[0547] (Output) Evaluation results and feedback

[0548] The server provides the user with customized feedback based on the evaluation results, supporting their progress to the next chapter.

[0549] These are the specific processing steps of this system's program. This flow allows users to study efficiently according to their reading goals. In addition, by receiving appropriate feedback based on their progress, they can acquire knowledge effectively.

[0550] (Application example 1)

[0551] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0552] While support systems for efficient reading existed in the past, they were difficult to customize to meet the user's reading goals or to provide detailed evaluations of reading progress and comprehension. Furthermore, efficiently analyzing large volumes of book data and generating summaries required significant time and effort. The present invention aims to solve these problems by providing an optimal reading plan tailored to the user's reading goals and supporting efficient reading.

[0553] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0554] In this invention, the server includes: means for inputting reading goals; means for uploading electronic book data; means for analyzing the content of the uploaded book and dividing it into chapters; means for generating summaries of the uploaded book using a generative AI model; means for incorporating the generated summaries into a reading plan; means for generating an individual reading plan based on a user's profile and book information; means for providing the generated reading plan to the user; means for periodically generating questions to evaluate the user's reading progress and comprehension; and means for evaluating the user's answers to the generated questions and providing feedback. This enables the provision of an optimal reading plan based on the user's reading goals, as well as evaluation and feedback of the user's progress and comprehension.

[0555] A "reading goal" is a specific purpose or task related to reading that a user wants to achieve.

[0556] "Electronic data of a book" refers to content data of a book stored in electronic form.

[0557] "Analysis" refers to the process of dividing the uploaded book into chapters and analyzing the content to make it easier to understand.

[0558] A "generative AI model" is an artificial intelligence model that summarizes the contents of a book or generates information tailored to the user's requests.

[0559] A "reading plan" is a plan for efficient reading based on the user's reading goals and profile.

[0560] A "summary" is information that condenses the contents of a book and extracts only the main points.

[0561] A "profile" is a collection of data such as a user's attribute information and reading goals.

[0562] A "question" is a server-generated question used to assess a user's reading progress and comprehension.

[0563] "Feedback" is evaluation and advice provided based on the user's answers.

[0564] This invention relates to a custom-made reading support system that utilizes generative AI, and enables users to efficiently progress through reading according to their reading goals. A specific embodiment of this system will be described.

[0565] User Registration and Profiling

[0566] The server provides a new user registration form. The user enters their name, email address, and reading goal, and submits it. For example, the name might be "Yamada Taro," the email address might be "taro@example.com," and the reading goal might be "improving business knowledge." The server receives this information and creates a user profile. This profile is stored in a database.

[0567] Book information input and analysis

[0568] The server provides a form for uploading the book title, author, and electronic data file. The user enters information about the book they want to read and uploads the electronic data file. For example, the title is "Theory and Practice of Business Strategy," the author is "Ichiro Suzuki," and the electronic data file is "business_strategy.pdf." The server receives this data and begins analysis. The analysis includes dividing the contents of the uploaded book into chapters. It also uses a generative AI model to generate a summary of the uploaded book. The analysis results and summary information are also stored in the database.

[0569] Providing customized reading support

[0570] The server generates an individual reading plan based on the user's profile and book information. This reading plan includes a plan for the user to efficiently progress through the book. For example, it includes summaries and simple explanations of each chapter. The user begins reading based on this reading plan. While reading a specific chapter, the user can request summaries and simple explanations.

[0571] Reading progress and comprehension assessment

[0572] The server periodically generates questions to assess the user's reading progress. These questions are designed to confirm the user's level of understanding. The user answers the questions from the server and submits them. The server evaluates these answers and provides feedback. For example, the server may evaluate whether the user has understood the key concepts of a particular chapter and help the user progress to the next chapter based on the results.

[0573] Hardware and software used

[0574] Hardware: Server (SQLite for database and server-side processing)

[0575] software:

[0576] SQLite: Used as a local database to manage user information, book information, and reading plans.

[0577] Transformers: Generate book summaries using the Hugging Face pipeline.

[0578] Examples and prompts

[0579] As a concrete example, let us consider a scenario in which a user named "Yamada Taro" uploads the book "Theory and Practice of Business Strategy" to the app with the goal of "improving his business knowledge." The app generates a summary of the book and provides it as a reading plan. Yamada Taro reads efficiently according to this plan, and periodically answers questions from the server to check his level of understanding.

[0580] An example of a prompt for the generative AI model is as follows:

[0581] Summarize the book's contents:

[0582] Book Title: Business Strategy Theory and Practice

[0583] Content: <Book content text uploaded here>

[0584] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0585] Step 1:

[0586] The server presents a new user registration form. The user enters their name, email address, and reading goal, then submits it. Based on the information entered, the server creates a user profile and stores it in a SQLite database.

[0587] Input: User's name, email address, reading goal

[0588] Output: User profile stored in the database

[0589] Step 2:

[0590] The server provides a form for uploading the book title, author, and electronic data file. The user enters the information about the book they want to read and uploads the electronic data file. The server receives this data and begins analyzing it.

[0591] Input: Book title, author, electronic data file

[0592] Output: Book data for analysis

[0593] Step 3:

[0594] The server then processes the uploaded book by dividing it into chapters. In this step, it analyzes the text in the book's electronic data file and performs data calculations to divide it into chapters. The analysis results are stored in a database.

[0595] Input: Contents of electronic data file

[0596] Output: Book data split by chapters

[0597] Step 4:

[0598] The server uses a generative AI model (such as the Hugging Face pipeline) to generate a summary for each chapter. This process takes the book's contents as input and performs data calculations to generate a summary. The generated summary is also stored in a database.

[0599] Input: Book data divided into chapters

[0600] Output: Summary of each chapter

[0601] Step 5:

[0602] The server generates a personalized reading plan based on the user's profile and book information, including chapter summaries and simple descriptions, and stores the plan in a database and provides it to the user.

[0603] Input: User profile, chapter summaries

[0604] Output: Individual Reading Plans

[0605] Step 6:

[0606] The user begins reading according to the personalized reading plan provided by the server. If a summary or brief explanation is needed during the reading, the user sends a request to the server, and the server responds by providing additional information.

[0607] Input: Reading plan, user request

[0608] Output: Additional information needed

[0609] Step 7:

[0610] The server periodically generates questions to assess the user's reading progress, and delivers the questions to the user, who then answers them.

[0611] Input: User's reading progress

[0612] Output: Assessment questions

[0613] Step 8:

[0614] The server receives and evaluates the user's answers, provides feedback based on the evaluation results, and supports the user in progressing to the next chapter.

[0615] Input: User's answer

[0616] Output: Evaluation results, feedback

[0617] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0618] This invention relates to a custom-made reading support system that utilizes generative AI and an emotion engine, and enables users to efficiently progress through reading according to their reading goals. Specific embodiments of this system are described below.

[0619] User Registration and Profiling

[0620] The server provides a new user registration form.

[0621] The user inputs his / her name, email address, and reading goal, and submits the information. For example, the name is "Taro Tanaka," the email address is "taro@example.com," and the reading goal is "improving business knowledge."

[0622] The server receives this information and creates a user profile, which is stored in a database.

[0623] Book information input and analysis

[0624] The server provides a form for uploading book titles, authors and electronic data files.

[0625] Users enter information about the book they want to read and upload the electronic data file. For example, the title might be "Theory and Practice of Business Strategy," the author might be "Ichiro Suzuki," and the electronic data file might be "business_strategy.pdf."

[0626] The server receives this data and begins analyzing it, which involves splitting the uploaded book content into chapters. The results of this analysis are also stored in the database.

[0627] Providing customized reading support

[0628] The server generates a personalized reading plan based on the user's profile and book information. This reading plan includes a plan to help the user efficiently progress through the book, including chapter summaries and simple descriptions.

[0629] The server provides this reading plan to the user.

[0630] The user begins reading based on the plan provided, and while reading a particular chapter, can request a summary or simple explanation.

[0631] Use of emotion engine

[0632] The server uses an emotion engine to recognize the user's emotions when they submit a request for a summary or simple explanation of a particular chapter. This emotion data is used to improve the user's reading experience. For example, if the user is struggling to understand, the server can provide a more concise explanation or additional examples.

[0633] Reading progress and comprehension assessment

[0634] The server periodically generates questions to assess the user's reading progress, which are used to verify the user's level of comprehension.

[0635] The user answers questions from the server and submits them.

[0636] The server receives these responses and also evaluates the user's emotions using an emotion engine, for example encouraging the user to proceed to the next chapter if they are excited.

[0637] Providing Feedback

[0638] The server generates feedback based on the user's answers and sentiment data to improve the user's reading experience, including advice on next steps and improving comprehension.

[0639] The user receives this feedback and decides to proceed to the next chapter.

[0640] Specific examples

[0641] Consider a scenario in which user Tanaka Taro is reading a book called "Theory and Practice of Business Strategy" to improve his business knowledge. Tanaka Taro logs in to the system, sets his reading goals, and then uploads the book's title, author, and electronic data. The server analyzes the data and generates and provides a reading plan that includes summaries and explanations for each chapter. Tanaka Taro reads efficiently according to this plan, and if he has difficulty understanding a particular chapter, he requests a simpler explanation through the emotion engine. The server responds by providing additional explanation. Periodically, the server checks Tanaka Taro's progress and comprehension in the form of questions and evaluates his emotion data using the emotion engine. Based on the results, the server provides feedback to optimize Tanaka Taro's reading experience.

[0642] This system allows Taro Tanaka to read efficiently and acquire the necessary knowledge in a short time. In addition, by utilizing the emotion engine, it is possible to provide optimal support according to individual needs and emotions.

[0643] The processing flow will be explained below.

[0644] This invention relates to a custom-made reading support system that utilizes generative AI and an emotion engine, allowing users to efficiently progress through reading according to their reading goals. A specific embodiment of this system will be described below, divided into processing steps.

[0645] Step 1:

[0646] The user enters his / her name, email address, and reading goal in the registration form and submits it. For example, the name is "Taro Tanaka," the email address is "taro@example.com," and the reading goal is "improving business knowledge."

[0647] The server receives this registration information, creates a user profile, and stores it in a database.

[0648] Step 2:

[0649] The server provides a form that accepts book titles, authors, and uploads of electronic data files.

[0650] Users enter information about the book they want to read and upload the electronic data file. For example, the title might be "Theory and Practice of Business Strategy," the author might be "Ichiro Suzuki," and the electronic data file might be "business_strategy.pdf."

[0651] Step 3:

[0652] The server receives the uploaded electronic data and begins analyzing the book contents.

[0653] The server divides the book into chapters and stores the data of the divided chapters in a database.

[0654] Step 4:

[0655] The server generates a personalized reading plan based on the user's profile and book data.

[0656] This reading plan includes summaries and simple explanations for each chapter.

[0657] Step 5:

[0658] The server provides the generated reading plan to the user.

[0659] The user begins reading according to the provided reading plan.

[0660] Step 6:

[0661] A user submits a request for a summary or simple description of a particular chapter, for example, "Please give me a summary of Chapter 1."

[0662] The server receives the request, generates the necessary summary and explanation, and provides it to the user, including using an emotion engine to analyze the user's emotion at the time of the request and providing it in the most appropriate format based on that emotion.

[0663] Step 7:

[0664] The server periodically generates questions to assess the user's reading progress and comprehension, such as "What are the key strategies discussed in Chapter 1?"

[0665] The server sends the generated question to the user.

[0666] Step 8:

[0667] The user answers questions from the server, for example, "The primary strategy is cost leadership."

[0668] The server receives the user's answers and evaluates their understanding.

[0669] Step 9:

[0670] The server generates and provides feedback to the user based on the user's answers.

[0671] This feedback allows the user to get advice on how to proceed to the next chapter or improve their understanding.

[0672] Step 10:

[0673] The emotion engine constantly monitors the user's emotions while reading, and if it detects that the user is feeling stressed, it will provide simple expressions or suggest taking a break, thereby improving the user's reading experience.

[0674] Specific examples

[0675] Consider a scenario in which user Tanaka Taro is reading a book called "Theory and Practice of Business Strategy" to improve his business knowledge. Tanaka Taro logs in to the system, sets his reading goals, and then uploads the book's title, author, and electronic data. The server analyzes the data and generates and provides a reading plan that includes summaries and explanations for each chapter. Tanaka Taro reads efficiently according to this plan, and if he has difficulty understanding a particular chapter, he requests a simpler explanation through the emotion engine. The server responds by providing additional explanation. The server periodically checks Tanaka Taro's progress and level of understanding in the form of questions and evaluates his emotion data at that time using the emotion engine. Based on the results, the server provides feedback to optimize Tanaka Taro's reading experience.

[0676] Example 2

[0677] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0678] Conventional reading support systems do not consider how to efficiently progress through reading, and in particular lack individualized support based on the user's reading goals and emotional state. They also lacked measures to address difficulties when users were having difficulty understanding the content of a book, and did not provide effective feedback to optimize the user's reading experience. Furthermore, they lacked a means to regularly evaluate the user's reading progress and comprehension and provide appropriate feedback.

[0679] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0680] In this invention, the server includes means for inputting reading goals, means for uploading electronic data of books, means for analyzing the content of the uploaded books and dividing them into chapters, means for generating an individual reading plan based on the user's profile and book information, means for providing the generated reading plan to the user, means for accepting requests from the user for summaries and brief explanations, means for generating responses to the accepted requests using a sentiment analysis engine, means for periodically generating questions to evaluate the user's reading progress and comprehension, and means for evaluating the user's answers to the generated questions and providing feedback, thereby optimizing the user's reading experience and enabling efficient reading progress, support for comprehension, and feedback tailored to individual needs.

[0681] The "means for inputting reading goals" is an interface that allows users to input their own reading purposes and goals into the system.

[0682] The "means for uploading electronic book data" is an interface that allows users to upload electronic files of books they wish to read to the system.

[0683] The "means for analyzing the contents of the uploaded book and dividing it into chapters" refers to an algorithm or program that analyzes the contents of the e-book file and divides it into chapters.

[0684] The "means for generating an individual reading plan based on a user's profile and book information" is a program for automatically creating an efficient reading plan based on user information and book contents.

[0685] The "means for providing the generated reading plan to the user" refers to an interface or notification function for presenting the generated reading plan to the user.

[0686] The "means for accepting a request for a summary or a brief explanation from a user" is an interface that allows a user to request additional information or a brief explanation of the content from the system.

[0687] The "means for generating a response using an emotion analysis engine" refers to an algorithm or program for analyzing a user's emotion and level of understanding and generating an appropriate response.

[0688] "Means for generating questions to periodically assess a user's reading progress and level of comprehension" refers to a program that enables the system to automatically generate questions to check a user's reading progress and level of comprehension.

[0689] The "means for evaluating the user's answers to the generated questions and providing feedback" is a program that analyzes the user's answers and provides appropriate advice and recommendations for the next learning step.

[0690] The present invention relates to a custom-made reading support system that uses generative AI and a sentiment analysis engine, and enables users to efficiently progress through reading according to their reading goals. Specific embodiments of this system are described below.

[0691] This system operates according to the following steps: Each process is executed by either the server, the terminal, or the user.

[0692] User Registration and Profiling

[0693] The server provides a new user registration form, which includes fields for name, email address, and reading goal.

[0694] The user inputs and submits this information. For example, the user's name is "Yamada Taro," the user's email address is "taro@example.com," and the user's reading goal is "improving business knowledge."

[0695] The server creates a user profile based on the received information and stores it in a database.

[0696] Book information input and analysis

[0697] The server provides a form for uploading book titles, authors and electronic data files.

[0698] The user enters information about the book they want to read and uploads the electronic data file. For example, the title is "Theory and Practice of Business Strategy," the author is "Ichiro Sato," and the electronic data file is "business_strategy.pdf."

[0699] The server receives this data, analyzes the contents of the book using AI, and divides it into chapters. The results of this analysis are also stored in a database.

[0700] Providing customized reading support

[0701] The server generates a personalized reading plan based on the user's profile and book information, including chapter summaries and simple descriptions.

[0702] The server provides this reading plan to the user.

[0703] The user begins reading based on the plan provided, and while reading a particular chapter, can request a summary or simple explanation.

[0704] Use of emotion engine

[0705] The server uses a sentiment analysis engine to recognize the user's emotions when they submit a request for a summary or simple explanation of a particular chapter. This emotional data is used to improve the user's reading experience. For example, if the user is struggling to understand, the server can provide a more concise explanation or additional examples.

[0706] Reading progress and comprehension assessment

[0707] The server periodically generates questions to assess the user's reading progress, which are used to verify the user's level of comprehension.

[0708] The user answers questions from the server and submits them.

[0709] The server receives these responses and uses a sentiment analysis engine to evaluate the user's emotions as well, for example encouraging them to move on to the next chapter if they are excited.

[0710] Providing Feedback

[0711] The server generates feedback based on the user's answers and sentiment data, including advice on next steps to take or improve understanding.

[0712] The user receives this feedback and decides to proceed to the next chapter.

[0713] Specific examples

[0714] As an example, consider a scenario in which user Yamada Taro is reading a book called "Theory and Practice of Business Strategy" to improve his business knowledge. Yamada Taro logs in to the system, sets his reading goals, and then uploads the book's title, author, and electronic data. The server analyzes the data and generates and provides a reading plan that includes summaries and explanations for each chapter. Yamada Taro reads efficiently according to this plan, and if he has difficulty understanding a particular chapter, he requests a simpler explanation through the emotion engine. The server responds by providing additional explanation. Periodically, the server checks Yamada Taro's progress and level of understanding in the form of questions and evaluates the emotional data using the emotion analysis engine. Based on the results, it provides feedback to optimize Yamada Taro's reading experience.

[0715] Usage example (example of prompt for generative AI model)

[0716] Please provide a brief summary of Chapter 1 of "The Theory and Practice of Business Strategy."

[0717] I find this chapter difficult. Can you use the Emotion Engine to provide a simpler explanation?

[0718] Generate feedback that encourages progression to the next chapter.

[0719] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0720] Step 1:

[0721] The server presents a new user registration form. The user enters their name, email address, and reading goals, and submits it. The server receives this information, generates a user profile, and stores it in a database.

[0722] Input: User's name, email address, reading goal

[0723] Output: User profile (e.g. user ID, name, email address, reading goal)

[0724] What happens: The server generates a user profile and stores it in a database.

[0725] Step 2:

[0726] The server provides a form for uploading the book title, author, and electronic data file. The user enters this information and uploads the electronic data file. The server receives this data and begins analyzing it.

[0727] Input: Book title, author, electronic data file

[0728] Output: Contents of each chapter of the book (analysis results)

[0729] Specific operation: The server analyzes the electronic data file, divides the book contents into chapters, and stores the analysis results in a database.

[0730] Step 3:

[0731] The server generates an individual reading plan based on the user profile and book information, provides the generated reading plan to the user, and the user begins reading based on this plan.

[0732] Input: User profile, book information (analysis results)

[0733] Output: Individual reading plan (e.g., chapter summaries, brief descriptions)

[0734] Specific behavior: The server creates a reading plan and provides it through the user interface.

[0735] Step 4:

[0736] When a user is reading a particular chapter, they can request a summary or simple explanation. The server receives this request and uses a sentiment analysis engine to recognize the user's sentiment.

[0737] Input: User request (request for summary or brief explanation)

[0738] Output: User's emotional state and response (e.g., a more concise explanation, additional examples)

[0739] Specific operation: The server analyzes the user's emotions using an emotion analysis engine and generates an appropriate response.

[0740] Step 5:

[0741] The server periodically generates questions to assess the user's reading progress. The user answers the questions and submits them. The server receives these answers and evaluates the user's emotions using a sentiment analysis engine.

[0742] Input: User progress, answers to comprehension questions

[0743] Output: User comprehension assessment and sentiment data

[0744] What it does: The server generates questions and presents them to the user. The user submits answers, which the server then evaluates.

[0745] Step 6:

[0746] The server generates feedback based on the user's answers and emotional data, including advice on next steps or improving comprehension. The user receives this feedback and decides to proceed to the next chapter.

[0747] Input: User responses, emotion data

[0748] Output: Feedback (e.g., suggestions for next chapter, advice for improving comprehension)

[0749] What happens: The server generates feedback and provides it to the user.

[0750] (Application example 2)

[0751] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0752] Existing reading support systems face challenges in providing optimal support that takes into account the individual needs and emotional state of each user. It is particularly difficult to provide effective feedback when a user is struggling to understand a particular chapter or is behind in their progress. Another issue is the lack of real-time feedback that utilizes emotional data when assessing reading progress and comprehension.

[0753] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for inputting reading goals, a means for uploading electronic book data, a means for analyzing the content of the uploaded book and dividing it into chapters, a means for generating an individual reading plan based on the user's profile and book information, a means for providing the generated reading plan to the user, a means for periodically generating questions to assess the user's reading progress and comprehension, a means for evaluating the user's answers to the generated questions and providing feedback, a means for analyzing the user's emotional state using an emotion engine, and a means for optimizing feedback based on the analyzed emotion data. This enables optimal support based on the user's individual needs and emotional state, allowing for efficient reading. Furthermore, utilizing real-time emotion data enables accurate assessment of reading progress and comprehension, enabling effective feedback.

[0754] "Means for inputting reading goals" refers to an interface that allows users to input their reading goals and the objectives they wish to achieve into the system.

[0755] "Means for uploading electronic book data" is a function that allows users to upload electronic book data files to the system.

[0756] "Means for analyzing the contents of uploaded books and dividing them into chapters" refers to a function in which the system analyzes the electronic data of uploaded books and divides and organizes the contents into chapters.

[0757] "Means for generating an individual reading plan based on the user's profile and book information" refers to the function by which the system automatically generates a reading plan optimized for each user based on the user's profile information and the content of the book.

[0758] "Means for providing the user with the generated reading plan" refers to the function by which the system presents the user with the generated individual reading plan and allows the user to proceed with their reading based on it.

[0759] "Means for generating questions to periodically assess a user's reading progress and comprehension" refers to a function that allows the system to generate questions to check a user's comprehension and progress according to a certain period of time or reading progress.

[0760] "Means for evaluating the user's answers to the generated questions and providing feedback" refers to a function that analyzes the answers given by the user to the questions, evaluates their reading progress and level of understanding based on that, and provides appropriate feedback.

[0761] "Means for analyzing the user's emotional state using an emotion engine" refers to the function by which the system recognizes and analyzes emotions from the user's input and actions, and acquires that data.

[0762] The "means for optimizing feedback based on analyzed emotional data" is a function that provides feedback and support according to the emotional state of the user based on the analyzed emotional data.

[0763] This invention relates to a custom-made reading support system that utilizes generative AI and an emotion engine, allowing users to efficiently progress through reading according to their reading goals. This system is mainly composed of a server and a terminal, and operates as follows:

[0764] User Registration and Profiling

[0765] The server provides a new user registration form. The user enters their name, email address, and reading goal through their terminal and submits it. For example, the name is "Reader A," the email address is "reader@example.com," and the reading goal is "Improvement of Expertise." The server receives this information and creates a user profile. This profile is stored in a database.

[0766] Book information input and analysis

[0767] The server provides a form for uploading the book title, author, and electronic data file. The user enters the information of the book they want to read through their terminal and uploads the electronic data file. For example, the title is "Theory and Practice of Specialized Knowledge," the author is "Author B," and the electronic data file is "specialized_knowledge.pdf." The server receives this data and begins analysis. The analysis includes dividing the content of the uploaded book into chapters. The analysis results are also stored in the database.

[0768] Providing customized reading support

[0769] The server generates an individual reading plan based on the user's profile and book information. This reading plan includes a plan for the user to efficiently progress through the book. For example, it may include summaries and simple explanations of each chapter. The server provides this reading plan to the user. The user can start reading based on the plan provided and request summaries and simple explanations while reading a specific chapter.

[0770] Use of emotion engine

[0771] The server uses an emotion engine to recognize the user's emotions when they submit a request for a summary or brief explanation of a particular chapter. This emotion data is used to improve the user's reading experience. For example, if the user is having difficulty understanding, the server can provide a more concise explanation or additional examples.

[0772] Reading progress and comprehension assessment

[0773] The server periodically generates questions to evaluate the user's reading progress. These questions are intended to confirm the user's level of understanding. The user answers the questions from the server and submits them via their device. The server receives these answers and evaluates the user's emotions using an emotion engine. For example, if the user is excited, it encourages them to move on to the next chapter.

[0774] Providing Feedback

[0775] The server generates feedback based on the user's answers and sentiment data. This feedback is intended to improve the user's reading experience and includes advice on next steps and improving comprehension. The user receives this feedback and decides to proceed to the next chapter.

[0776] Specific examples

[0777] For example, consider a scenario in which a user, "Reader A," is reading a book called "Theory and Practice of Specialized Knowledge" to improve their expertise. "Reader A" logs in to the system, sets their reading goals, and then uploads the book's title, author, and electronic data. The server analyzes the data and generates and provides a reading plan that includes summaries and explanations for each chapter. "Reader A" reads efficiently according to this plan, and if they have difficulty understanding a particular chapter, they request a simpler explanation through the emotion engine. The server responds by providing additional explanation. The server periodically checks "Reader A's" progress and comprehension in the form of questions and evaluates their emotional data using the emotion engine. By providing feedback based on the results, it is possible to optimize "Reader A's" reading experience.

[0778] Specific examples of input prompts for the generative AI model used

[0779] For example, if "Reader A" finishes reading the first chapter and writes that he or she "doesn't understand," the following prompt can be used to connect with the emotion engine:

[0780] "User: Reader A. Goal: Improve expertise. Book: Theory and Practice of Expertise.

[0781] Reader A finished reading Chapter 1 and commented, "I don't understand it." Please analyze this sentiment with the emotion engine and generate feedback for the next step."

[0782] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0783] Step 1:

[0784] A user accesses the system through a terminal and displays a new user registration form. The user enters their name, email address, and reading goals and submits it. This information is sent to the server. The server creates a user profile based on the received information and stores it in a database. The input of this step is the user's personal information and reading goals, and the output is the user profile stored in the database.

[0785] Step 2:

[0786] The server provides a form for uploading the book title, author, and electronic data file. The user enters the book information through the terminal and uploads the electronic data file. This information is sent to the server. The server analyzes the received data and divides the book content into chapters. The analysis results are also stored in the database. The input of this step is the book information and electronic data file uploaded by the user, and the output is the analyzed book content for each chapter.

[0787] Step 3:

[0788] The server generates a personalized reading plan based on the user profile and book information, including chapter summaries and simple descriptions. The server then sends this reading plan to the user's device and displays it. The input for this step is the user profile and the parsed book content, and the output is the generated reading plan.

[0789] Step 4:

[0790] The user reads according to the reading plan provided through the terminal. While reading a particular chapter, the user sends a request to the server for a summary or simple explanation. The server receives this request and uses an emotion engine to analyze the user's emotion. The input of this step is the user's request, and the output is the analyzed emotion data.

[0791] Step 5:

[0792] The server provides the user with a more concise explanation or additional examples based on the emotion data. This feedback is sent to the user's device and displayed. The input of this step is the analyzed emotion data, and the output is the optimized feedback.

[0793] Step 6:

[0794] The server periodically generates questions to assess the user's reading progress, including questions to check comprehension. The questions are sent to the user's device and displayed. The input of this step is the user's reading progress, and the output is the generated questions.

[0795] Step 7:

[0796] The user answers questions through the terminal and sends the answers to the server. The server receives the answers and evaluates the user's emotions using the emotion engine. The input of this step is the user's answers, and the output is the evaluation result.

[0797] Step 8:

[0798] The server generates feedback based on the user's answers and emotional data. The feedback includes advice on how to proceed to the next step or improve understanding. The feedback is sent to the user's device and displayed. The inputs of this step are the evaluation results and the analyzed emotional data, and the output is the feedback.

[0799] Examples of use

[0800] The scenario involves "Reader A" starting to read a book called "Theory and Practice of Specialized Knowledge." "Reader A" logs into the system, sets a reading goal, and uploads the book's title, author, and electronic data. The server analyzes the data and generates and provides summaries and explanations for each chapter. If "Reader A" has difficulty understanding, the server responds to requests through the emotion engine and provides additional explanations. The server also periodically checks the progress and level of comprehension, evaluates the emotion data, and provides feedback.

[0801] Specific examples of input prompts for generative AI models

[0802] "User: Reader A. Goal: Improve expertise. Book: Theory and Practice of Expertise.

[0803] Reader A finished reading Chapter 1 and commented, "I don't understand it." Please analyze this sentiment with the emotion engine and generate feedback for the next step."

[0804] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0805] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0806] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0807] [Third embodiment]

[0808] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0809] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0810] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0811] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0812] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0813] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0814] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0815] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0816] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0817] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0818] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0819] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0820] This invention relates to a custom-made reading support system that utilizes generative AI, enabling users to efficiently progress through reading according to their reading goals. Specific embodiments of this system are described below.

[0821] User Registration and Profiling

[0822] The server provides a new user registration form.

[0823] The user inputs his / her name, email address, and reading goal, and submits the information. For example, the name is "Taro Tanaka," the email address is "taro@example.com," and the reading goal is "improving business knowledge."

[0824] The server receives this information and creates a user profile, which is stored in a database.

[0825] Book information input and analysis

[0826] The server provides a form for uploading book titles, authors and electronic data files.

[0827] Users enter information about the book they want to read and upload the electronic data file. For example, the title might be "Theory and Practice of Business Strategy," the author might be "Ichiro Suzuki," and the electronic data file might be "business_strategy.pdf."

[0828] The server receives this data and begins analyzing it, which involves splitting the uploaded book content into chapters. The results of this analysis are also stored in the database.

[0829] Providing customized reading support

[0830] The server generates a personalized reading plan based on the user's profile and book information. This reading plan includes a plan to help the user efficiently progress through the book, including chapter summaries and simple descriptions.

[0831] The server provides this reading plan to the user.

[0832] The user begins reading based on the plan provided, and while reading a particular chapter, can request a summary or simple explanation.

[0833] Reading progress and comprehension assessment

[0834] The server periodically generates questions to assess the user's reading progress, which are used to verify the user's level of comprehension.

[0835] The user answers questions from the server and submits them.

[0836] The server evaluates these answers and provides feedback, for example assessing whether the user has understood the key concepts of a particular chapter and helping them progress to the next chapter based on the results.

[0837] Specific examples

[0838] Consider a scenario in which a user, Taro Tanaka, reads a book called "Theory and Practice of Business Strategy" to improve his business knowledge. Taro Tanaka logs in to the system, sets his reading goals, and then uploads the book's title, author, and electronic data. The server analyzes the data and generates and provides a reading plan that includes summaries and explanations of each chapter. Taro Tanaka reads efficiently according to this plan and periodically answers questions from the server to check his understanding. The server evaluates his answers and provides feedback to support Taro Tanaka's reading.

[0839] This system allows Taro Tanaka to read efficiently and acquire the necessary knowledge in a short time. In addition, by lowering the barrier to reading, it is expected that the amount of reading will increase and the market will expand.

[0840] The processing flow will be explained below.

[0841] Step 1:

[0842] Users enter their name, email address, and reading goal into a registration form and submit it.

[0843] The server receives this registration information, creates a user profile, and stores it in a database.

[0844] Step 2:

[0845] The server provides a form that accepts book titles, authors, and uploads of electronic data files.

[0846] The user enters information about the book they want to read and uploads the electronic data file.

[0847] Step 3:

[0848] The server receives the uploaded electronic data and begins analyzing the book contents.

[0849] The server divides the book into chapters and stores the data of the divided chapters in a database.

[0850] Step 4:

[0851] The server generates a personalized reading plan based on the user's profile and book data.

[0852] This reading plan includes summaries and simple explanations for each chapter.

[0853] Step 5:

[0854] The server provides the generated reading plan to the user.

[0855] The user begins reading according to the provided reading plan.

[0856] Step 6:

[0857] A user submits a request for a summary or simple description of a particular chapter.

[0858] The server receives the request and generates the necessary summary or explanation to provide to the user.

[0859] Step 7:

[0860] The server periodically generates questions to assess the user's reading progress and comprehension.

[0861] The server sends the generated question to the user.

[0862] Step 8:

[0863] The user answers questions posed by the server.

[0864] The server receives the user's answers and evaluates their understanding.

[0865] Step 9:

[0866] The server generates and provides feedback to the user based on the user's answers.

[0867] This feedback allows the user to get advice on how to proceed to the next chapter or improve their understanding.

[0868] Example 1

[0869] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0870] Conventional reading support systems have difficulty providing efficient reading plans tailored to individual users' reading goals, and are inadequate in properly evaluating and providing feedback on users' reading progress and comprehension. They also lack the functionality to request summaries of specific chapters or simple explanations, hindering effective learning. Furthermore, it is difficult to provide personalized support based on user profiles, and the process of analyzing large amounts of book information is time-consuming.

[0871] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0872] In this invention, the server includes: a means for creating a user profile; a means for inputting reading goals; a means for uploading electronic book data; a means for analyzing the uploaded book content and dividing it into chapters; a means for generating an individual reading plan based on the user profile and book information; a means for providing the generated reading plan to the user; a means for periodically generating questions to assess the user's reading progress and comprehension; a means for evaluating the user's answers to the generated questions and providing feedback; and a means for generating reading plans and questions using a generative AI model. This allows for the provision of an efficient reading plan tailored to the user's individual reading goals, and for appropriate evaluation and feedback of the user's reading progress and comprehension. Furthermore, the server also provides a function for requesting summaries and simplified explanations of specific chapters, thereby supporting effective learning for the user.

[0873] "User Profile" refers to data containing a user's personal information and reading goals.

[0874] "Reading goal" refers to the purpose or goal a user wishes to achieve in reading.

[0875] "Electronic Book Data" refers to a file containing the contents of a Book stored in digital form.

[0876] "Upload" refers to the act of a user sending data from their own device to a server.

[0877] "Analysis" refers to the process of dividing the received electronic data of a book into content and extracting information.

[0878] A "chapter" refers to a section into which the contents of a book are divided.

[0879] "Reading plan" refers to data that includes a plan for efficiently progressing through reading in accordance with the user's reading goals.

[0880] "Question" refers to a question generated to assess a user's reading progress and comprehension.

[0881] "Feedback" refers to information about improvements and next steps provided based on the user's responses.

[0882] A "generative AI model" refers to an algorithm that uses artificial intelligence to automatically handle specific tasks.

[0883] A "summary" is a brief summary of the contents of each chapter of a book.

[0884] "Simple expression" refers to an explanation that simplifies complex content to make it easy to understand.

[0885] "Request" means a request sent by a User to a Server for particular information or functionality.

[0886] "Progress" refers to the process and progress of the user's reading.

[0887] "Support" refers to assistance provided to a user in taking the next step.

[0888] This invention relates to a custom-made reading support system that utilizes generative AI models. This system can efficiently guide users through reading according to their reading goals, and can appropriately evaluate the user's reading progress and comprehension and provide feedback.

[0889] Hardware and Software Configuration

[0890] The server acts as a web server, receiving requests from users and processing them accordingly. The server is built using, for example, the Python Flask framework. The database uses SQLAlchemy to manage user profiles and book information.

[0891] Users access the server from a browser using a device connected to the Internet (e.g., a PC, smartphone, or tablet).

[0892] Processing Details

[0893] First, the server presents a new user registration form, which contains fields where the user can enter their name, email address, and reading goal. After the user enters the information in the form and clicks the submit button, the server receives the data and creates a user profile, which is then stored in a database.

[0894] The server then provides a form for uploading the book title, author, and electronic data file. The user enters the book information in this form and uploads the electronic data, such as a PDF file. The server receives the uploaded data and uses the Python PyMuPDF library to parse the book and split it into chapters. The results of this analysis are also stored in the database.

[0895] The server then generates a personalized reading plan based on the user profile and book information. This plan uses OpenAI's GPT-3 API to prompt a generative AI model to generate summaries and simplified explanations. The generated reading plan is then provided to the user, for example, in PDF format or as a web page.

[0896] As the user reads, the server periodically generates questions to assess the user's progress. These questions are also generated using a generative AI model. The user answers the questions provided by the server, and the answers are sent to the server. The server evaluates the received answers using a natural language processing algorithm and provides feedback to the user. Based on this feedback, the server supports the user in progressing to the next chapter.

[0897] Specific examples

[0898] For example, consider a user who has a goal to "improve his business knowledge." He uploads a book called "Theory and Practice of Business Strategy" to the system. The server analyzes the book and generates summaries for each chapter. An example prompt for the generated code might look like this:

[0899] "Generate a summary of Chapter 1 of Business Strategy Theory and Practice."

[0900] "Generate questions for users to assess their reading progress."

[0901] "Please explain how the new reading plan will help Taro Tanaka improve his business knowledge."

[0902] In this way, the system provides efficient reading assistance tailored to the user's specific reading goals.

[0903] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0904] Step 1: Provide a user registration form

[0905] The server provides a web form for user registration using HTML and CSS.

[0906] The user enters profile information such as their name, email address, and desired genre of reading into the form and clicks the submit button. For example, the user enters their name "Yamada Taro," their email address "yamada@example.com," and their reading goal "skill acquisition."

[0907] (Input) Name, email address, and reading goal entered by the user

[0908] (Output) User registration data received by the server

[0909] The server stores the received data in a database using SQLAlchemy.

[0910] Step 2: Provide a book information input form

[0911] The server provides a form for uploading the book title, author, and PDF file.

[0912] The user enters the title of the book they want to read, the author's name, and the PDF file into the form and clicks the upload button. For example, enter the title "Technology Basics," the author "Ichiro Sato," and the PDF file "tech_basics.pdf."

[0913] (Input) User-entered book title, author name, and uploaded PDF file

[0914] (Output) Book information and electronic data files received by the server

[0915] The server analyzes the received PDF file using Python's PyMuPDF library and divides the content into chapters. The analysis results are also stored in a database.

[0916] Step 3: Generate a Reading Plan

[0917] The server generates a personalized reading plan based on the user profile and the analyzed book information.

[0918] Specifically, it uses OpenAI's GPT-3 API to send prompt sentences summarizing the uploaded book to a generative model to obtain a summary.

[0919] (Input) User profile, book information, prompt for GPT-3

[0920] (Output) Generated summary and reading plan

[0921] For example, generate a summary of "Technology Fundamentals" and create a plan such as "Read one chapter per week."

[0922] Step 4: Provide a reading plan

[0923] The server provides the generated reading plan to the user.

[0924] Specifically, the results are published in PDF format or as a web page, and users are notified by email.

[0925] (Input) Generated reading plan

[0926] (Output) Links to the reading plans that the user can access

[0927] Users can view and download the reading plan from the provided link.

[0928] Step 5: Generate questions to assess reading progress

[0929] The server periodically generates questions to assess the user's reading progress.

[0930] These questions are also generated using generative AI models, for example, to create questions that assess key points based on what you've read.

[0931] (Input) User's reading progress data, prompt text for GPT-3

[0932] (Output) A list of generated questions

[0933] Users periodically answer provided questions and send their answers to the server.

[0934] Step 6: Rate answers and provide feedback

[0935] The server evaluates the received user responses using natural language processing algorithms.

[0936] For example, it analyzes whether the answers are accurate and how much they understand.

[0937] (Input) User response data

[0938] (Output) Evaluation results and feedback

[0939] The server provides the user with customized feedback based on the evaluation results, supporting their progress to the next chapter.

[0940] These are the specific processing steps of this system's program. This flow allows users to study efficiently according to their reading goals. In addition, by receiving appropriate feedback based on their progress, they can acquire knowledge effectively.

[0941] (Application example 1)

[0942] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0943] While support systems for efficient reading existed in the past, they were difficult to customize to meet the user's reading goals or to provide detailed evaluations of reading progress and comprehension. Furthermore, efficiently analyzing large volumes of book data and generating summaries required significant time and effort. The present invention aims to solve these problems by providing an optimal reading plan tailored to the user's reading goals and supporting efficient reading.

[0944] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0945] In this invention, the server includes: means for inputting reading goals; means for uploading electronic book data; means for analyzing the content of the uploaded book and dividing it into chapters; means for generating summaries of the uploaded book using a generative AI model; means for incorporating the generated summaries into a reading plan; means for generating an individual reading plan based on a user's profile and book information; means for providing the generated reading plan to the user; means for periodically generating questions to evaluate the user's reading progress and comprehension; and means for evaluating the user's answers to the generated questions and providing feedback. This enables the provision of an optimal reading plan based on the user's reading goals, as well as evaluation and feedback of the user's progress and comprehension.

[0946] A "reading goal" is a specific purpose or task related to reading that a user wants to achieve.

[0947] "Electronic data of a book" refers to content data of a book stored in electronic form.

[0948] "Analysis" refers to the process of dividing the uploaded book into chapters and analyzing the content to make it easier to understand.

[0949] A "generative AI model" is an artificial intelligence model that summarizes the contents of a book or generates information tailored to the user's requests.

[0950] A "reading plan" is a plan for efficient reading based on the user's reading goals and profile.

[0951] A "summary" is information that condenses the contents of a book and extracts only the main points.

[0952] A "profile" is a collection of data such as a user's attribute information and reading goals.

[0953] A "question" is a server-generated question used to assess a user's reading progress and comprehension.

[0954] "Feedback" is evaluation and advice provided based on the user's answers.

[0955] This invention relates to a custom-made reading support system that utilizes generative AI, and enables users to efficiently progress through reading according to their reading goals. A specific embodiment of this system will be described.

[0956] User Registration and Profiling

[0957] The server provides a new user registration form. The user enters their name, email address, and reading goal, and submits it. For example, the name might be "Yamada Taro," the email address might be "taro@example.com," and the reading goal might be "improving business knowledge." The server receives this information and creates a user profile. This profile is stored in a database.

[0958] Book information input and analysis

[0959] The server provides a form for uploading the book title, author, and electronic data file. The user enters information about the book they want to read and uploads the electronic data file. For example, the title is "Theory and Practice of Business Strategy," the author is "Ichiro Suzuki," and the electronic data file is "business_strategy.pdf." The server receives this data and begins analysis. The analysis includes dividing the contents of the uploaded book into chapters. It also uses a generative AI model to generate a summary of the uploaded book. The analysis results and summary information are also stored in the database.

[0960] Providing customized reading support

[0961] The server generates an individual reading plan based on the user's profile and book information. This reading plan includes a plan for the user to efficiently progress through the book. For example, it includes summaries and simple explanations of each chapter. The user begins reading based on this reading plan. While reading a specific chapter, the user can request summaries and simple explanations.

[0962] Reading progress and comprehension assessment

[0963] The server periodically generates questions to assess the user's reading progress. These questions are designed to confirm the user's level of understanding. The user answers the questions from the server and submits them. The server evaluates these answers and provides feedback. For example, the server may evaluate whether the user has understood the key concepts of a particular chapter and help the user progress to the next chapter based on the results.

[0964] Hardware and software used

[0965] Hardware: Server (SQLite for database and server-side processing)

[0966] software:

[0967] SQLite: Used as a local database to manage user information, book information, and reading plans.

[0968] Transformers: Generate book summaries using the Hugging Face pipeline.

[0969] Examples and prompts

[0970] As a concrete example, let us consider a scenario in which a user named "Yamada Taro" uploads the book "Theory and Practice of Business Strategy" to the app with the goal of "improving his business knowledge." The app generates a summary of the book and provides it as a reading plan. Yamada Taro reads efficiently according to this plan, and periodically answers questions from the server to check his level of understanding.

[0971] An example of a prompt for the generative AI model is as follows:

[0972] Summarize the book's contents:

[0973] Book Title: Business Strategy Theory and Practice

[0974] Content: <Book content text uploaded here>

[0975] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0976] Step 1:

[0977] The server presents a new user registration form. The user enters their name, email address, and reading goal, then submits it. Based on the information entered, the server creates a user profile and stores it in a SQLite database.

[0978] Input: User's name, email address, reading goal

[0979] Output: User profile stored in the database

[0980] Step 2:

[0981] The server provides a form for uploading the book title, author, and electronic data file. The user enters the information about the book they want to read and uploads the electronic data file. The server receives this data and begins analyzing it.

[0982] Input: Book title, author, electronic data file

[0983] Output: Book data for analysis

[0984] Step 3:

[0985] The server then processes the uploaded book by dividing it into chapters. In this step, it analyzes the text in the book's electronic data file and performs data calculations to divide it into chapters. The analysis results are stored in a database.

[0986] Input: Contents of electronic data file

[0987] Output: Book data split by chapters

[0988] Step 4:

[0989] The server uses a generative AI model (such as the Hugging Face pipeline) to generate a summary for each chapter. This process takes the book's contents as input and performs data calculations to generate a summary. The generated summary is also stored in a database.

[0990] Input: Book data divided into chapters

[0991] Output: Summary of each chapter

[0992] Step 5:

[0993] The server generates a personalized reading plan based on the user's profile and book information, including chapter summaries and simple descriptions, and stores the plan in a database and provides it to the user.

[0994] Input: User profile, chapter summaries

[0995] Output: Individual Reading Plans

[0996] Step 6:

[0997] The user begins reading according to the personalized reading plan provided by the server. If a summary or brief explanation is needed during the reading, the user sends a request to the server, and the server responds by providing additional information.

[0998] Input: Reading plan, user request

[0999] Output: Additional information needed

[1000] Step 7:

[1001] The server periodically generates questions to assess the user's reading progress, and delivers the questions to the user, who then answers them.

[1002] Input: User's reading progress

[1003] Output: Assessment questions

[1004] Step 8:

[1005] The server receives and evaluates the user's answers, provides feedback based on the evaluation results, and supports the user in progressing to the next chapter.

[1006] Input: User's answer

[1007] Output: Evaluation results, feedback

[1008] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1009] This invention relates to a custom-made reading support system that utilizes generative AI and an emotion engine, and enables users to efficiently progress through reading according to their reading goals. Specific embodiments of this system are described below.

[1010] User Registration and Profiling

[1011] The server provides a new user registration form.

[1012] The user inputs his / her name, email address, and reading goal, and submits the information. For example, the name is "Taro Tanaka," the email address is "taro@example.com," and the reading goal is "improving business knowledge."

[1013] The server receives this information and creates a user profile, which is stored in a database.

[1014] Book information input and analysis

[1015] The server provides a form for uploading book titles, authors and electronic data files.

[1016] Users enter information about the book they want to read and upload the electronic data file. For example, the title might be "Theory and Practice of Business Strategy," the author might be "Ichiro Suzuki," and the electronic data file might be "business_strategy.pdf."

[1017] The server receives this data and begins analyzing it, which involves splitting the uploaded book content into chapters. The results of this analysis are also stored in the database.

[1018] Providing customized reading support

[1019] The server generates a personalized reading plan based on the user's profile and book information. This reading plan includes a plan to help the user efficiently progress through the book, including chapter summaries and simple descriptions.

[1020] The server provides this reading plan to the user.

[1021] The user begins reading based on the plan provided, and while reading a particular chapter, can request a summary or simple explanation.

[1022] Use of emotion engine

[1023] The server uses an emotion engine to recognize the user's emotions when they submit a request for a summary or simple explanation of a particular chapter. This emotion data is used to improve the user's reading experience. For example, if the user is struggling to understand, the server can provide a more concise explanation or additional examples.

[1024] Reading progress and comprehension assessment

[1025] The server periodically generates questions to assess the user's reading progress, which are used to verify the user's level of comprehension.

[1026] The user answers questions from the server and submits them.

[1027] The server receives these responses and also evaluates the user's emotions using an emotion engine, for example encouraging the user to proceed to the next chapter if they are excited.

[1028] Providing Feedback

[1029] The server generates feedback based on the user's answers and sentiment data to improve the user's reading experience, including advice on next steps and improving comprehension.

[1030] The user receives this feedback and decides to proceed to the next chapter.

[1031] Specific examples

[1032] Consider a scenario in which user Tanaka Taro is reading a book called "Theory and Practice of Business Strategy" to improve his business knowledge. Tanaka Taro logs in to the system, sets his reading goals, and then uploads the book's title, author, and electronic data. The server analyzes the data and generates and provides a reading plan that includes summaries and explanations for each chapter. Tanaka Taro reads efficiently according to this plan, and if he has difficulty understanding a particular chapter, he requests a simpler explanation through the emotion engine. The server responds by providing additional explanation. Periodically, the server checks Tanaka Taro's progress and comprehension in the form of questions and evaluates his emotion data using the emotion engine. Based on the results, the server provides feedback to optimize Tanaka Taro's reading experience.

[1033] This system allows Taro Tanaka to read efficiently and acquire the necessary knowledge in a short time. In addition, by utilizing the emotion engine, it is possible to provide optimal support according to individual needs and emotions.

[1034] The processing flow will be explained below.

[1035] This invention relates to a custom-made reading support system that utilizes generative AI and an emotion engine, allowing users to efficiently progress through reading according to their reading goals. A specific embodiment of this system will be described below, divided into processing steps.

[1036] Step 1:

[1037] The user enters his / her name, email address, and reading goal in the registration form and submits it. For example, the name is "Taro Tanaka," the email address is "taro@example.com," and the reading goal is "improving business knowledge."

[1038] The server receives this registration information, creates a user profile, and stores it in a database.

[1039] Step 2:

[1040] The server provides a form that accepts book titles, authors, and uploads of electronic data files.

[1041] Users enter information about the book they want to read and upload the electronic data file. For example, the title might be "Theory and Practice of Business Strategy," the author might be "Ichiro Suzuki," and the electronic data file might be "business_strategy.pdf."

[1042] Step 3:

[1043] The server receives the uploaded electronic data and begins analyzing the book contents.

[1044] The server divides the book into chapters and stores the data of the divided chapters in a database.

[1045] Step 4:

[1046] The server generates a personalized reading plan based on the user's profile and book data.

[1047] This reading plan includes summaries and simple explanations for each chapter.

[1048] Step 5:

[1049] The server provides the generated reading plan to the user.

[1050] The user begins reading according to the provided reading plan.

[1051] Step 6:

[1052] A user submits a request for a summary or simple description of a particular chapter, for example, "Please give me a summary of Chapter 1."

[1053] The server receives the request, generates the necessary summary and explanation, and provides it to the user, including using an emotion engine to analyze the user's emotion at the time of the request and providing it in the most appropriate format based on that emotion.

[1054] Step 7:

[1055] The server periodically generates questions to assess the user's reading progress and comprehension, such as "What are the key strategies discussed in Chapter 1?"

[1056] The server sends the generated question to the user.

[1057] Step 8:

[1058] The user answers questions from the server, for example, "The primary strategy is cost leadership."

[1059] The server receives the user's answers and evaluates their understanding.

[1060] Step 9:

[1061] The server generates and provides feedback to the user based on the user's answers.

[1062] This feedback allows the user to get advice on how to proceed to the next chapter or improve their understanding.

[1063] Step 10:

[1064] The emotion engine constantly monitors the user's emotions while reading, and if it detects that the user is feeling stressed, it will provide simple expressions or suggest taking a break, thereby improving the user's reading experience.

[1065] Specific examples

[1066] Consider a scenario in which user Tanaka Taro is reading a book called "Theory and Practice of Business Strategy" to improve his business knowledge. Tanaka Taro logs in to the system, sets his reading goals, and then uploads the book's title, author, and electronic data. The server analyzes the data and generates and provides a reading plan that includes summaries and explanations for each chapter. Tanaka Taro reads efficiently according to this plan, and if he has difficulty understanding a particular chapter, he requests a simpler explanation through the emotion engine. The server responds by providing additional explanation. The server periodically checks Tanaka Taro's progress and level of understanding in the form of questions and evaluates his emotion data at that time using the emotion engine. Based on the results, the server provides feedback to optimize Tanaka Taro's reading experience.

[1067] Example 2

[1068] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1069] Conventional reading support systems do not consider how to efficiently progress through reading, and in particular lack individualized support based on the user's reading goals and emotional state. They also lacked measures to address difficulties when users were having difficulty understanding the content of a book, and did not provide effective feedback to optimize the user's reading experience. Furthermore, they lacked a means to regularly evaluate the user's reading progress and comprehension and provide appropriate feedback.

[1070] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1071] In this invention, the server includes means for inputting reading goals, means for uploading electronic data of books, means for analyzing the content of the uploaded books and dividing them into chapters, means for generating an individual reading plan based on the user's profile and book information, means for providing the generated reading plan to the user, means for accepting requests from the user for summaries and brief explanations, means for generating responses to the accepted requests using a sentiment analysis engine, means for periodically generating questions to evaluate the user's reading progress and comprehension, and means for evaluating the user's answers to the generated questions and providing feedback, thereby optimizing the user's reading experience and enabling efficient reading progress, support for comprehension, and feedback tailored to individual needs.

[1072] The "means for inputting reading goals" is an interface that allows users to input their own reading purposes and goals into the system.

[1073] The "means for uploading electronic book data" is an interface that allows users to upload electronic files of books they wish to read to the system.

[1074] The "means for analyzing the contents of the uploaded book and dividing it into chapters" refers to an algorithm or program that analyzes the contents of the e-book file and divides it into chapters.

[1075] The "means for generating an individual reading plan based on a user's profile and book information" is a program for automatically creating an efficient reading plan based on user information and book contents.

[1076] The "means for providing the generated reading plan to the user" refers to an interface or notification function for presenting the generated reading plan to the user.

[1077] The "means for accepting a request for a summary or a brief explanation from a user" is an interface that allows a user to request additional information or a brief explanation of the content from the system.

[1078] The "means for generating a response using an emotion analysis engine" refers to an algorithm or program for analyzing a user's emotion and level of understanding and generating an appropriate response.

[1079] "Means for generating questions to periodically assess a user's reading progress and level of comprehension" refers to a program that enables the system to automatically generate questions to check a user's reading progress and level of comprehension.

[1080] The "means for evaluating the user's answers to the generated questions and providing feedback" is a program that analyzes the user's answers and provides appropriate advice and recommendations for the next learning step.

[1081] The present invention relates to a custom-made reading support system that uses generative AI and a sentiment analysis engine, and enables users to efficiently progress through reading according to their reading goals. Specific embodiments of this system are described below.

[1082] This system operates according to the following steps: Each process is executed by either the server, the terminal, or the user.

[1083] User Registration and Profiling

[1084] The server provides a new user registration form, which includes fields for name, email address, and reading goal.

[1085] The user inputs and submits this information. For example, the user's name is "Yamada Taro," the user's email address is "taro@example.com," and the user's reading goal is "improving business knowledge."

[1086] The server creates a user profile based on the received information and stores it in a database.

[1087] Book information input and analysis

[1088] The server provides a form for uploading book titles, authors and electronic data files.

[1089] The user enters information about the book they want to read and uploads the electronic data file. For example, the title is "Theory and Practice of Business Strategy," the author is "Ichiro Sato," and the electronic data file is "business_strategy.pdf."

[1090] The server receives this data, analyzes the contents of the book using AI, and divides it into chapters. The results of this analysis are also stored in a database.

[1091] Providing customized reading support

[1092] The server generates a personalized reading plan based on the user's profile and book information, including chapter summaries and simple descriptions.

[1093] The server provides this reading plan to the user.

[1094] The user begins reading based on the plan provided, and while reading a particular chapter, can request a summary or simple explanation.

[1095] Use of emotion engine

[1096] The server uses a sentiment analysis engine to recognize the user's emotions when they submit a request for a summary or simple explanation of a particular chapter. This emotional data is used to improve the user's reading experience. For example, if the user is struggling to understand, the server can provide a more concise explanation or additional examples.

[1097] Reading progress and comprehension assessment

[1098] The server periodically generates questions to assess the user's reading progress, which are used to verify the user's level of comprehension.

[1099] The user answers questions from the server and submits them.

[1100] The server receives these responses and uses a sentiment analysis engine to evaluate the user's emotions as well, for example encouraging them to move on to the next chapter if they are excited.

[1101] Providing Feedback

[1102] The server generates feedback based on the user's answers and sentiment data, including advice on next steps to take or improve understanding.

[1103] The user receives this feedback and decides to proceed to the next chapter.

[1104] Specific examples

[1105] As an example, consider a scenario in which user Yamada Taro is reading a book called "Theory and Practice of Business Strategy" to improve his business knowledge. Yamada Taro logs in to the system, sets his reading goals, and then uploads the book's title, author, and electronic data. The server analyzes the data and generates and provides a reading plan that includes summaries and explanations for each chapter. Yamada Taro reads efficiently according to this plan, and if he has difficulty understanding a particular chapter, he requests a simpler explanation through the emotion engine. The server responds by providing additional explanation. Periodically, the server checks Yamada Taro's progress and level of understanding in the form of questions and evaluates the emotional data using the emotion analysis engine. Based on the results, it provides feedback to optimize Yamada Taro's reading experience.

[1106] Usage example (example of prompt for generative AI model)

[1107] Please provide a brief summary of Chapter 1 of "The Theory and Practice of Business Strategy."

[1108] I find this chapter difficult. Can you use the Emotion Engine to provide a simpler explanation?

[1109] Generate feedback that encourages progression to the next chapter.

[1110] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1111] Step 1:

[1112] The server presents a new user registration form. The user enters their name, email address, and reading goals, and submits it. The server receives this information, generates a user profile, and stores it in a database.

[1113] Input: User's name, email address, reading goal

[1114] Output: User profile (e.g. user ID, name, email address, reading goal)

[1115] What happens: The server generates a user profile and stores it in a database.

[1116] Step 2:

[1117] The server provides a form for uploading the book title, author, and electronic data file. The user enters this information and uploads the electronic data file. The server receives this data and begins analyzing it.

[1118] Input: Book title, author, electronic data file

[1119] Output: Contents of each chapter of the book (analysis results)

[1120] Specific operation: The server analyzes the electronic data file, divides the book contents into chapters, and stores the analysis results in a database.

[1121] Step 3:

[1122] The server generates an individual reading plan based on the user profile and book information, provides the generated reading plan to the user, and the user begins reading based on this plan.

[1123] Input: User profile, book information (analysis results)

[1124] Output: Individual reading plan (e.g., chapter summaries, brief descriptions)

[1125] Specific behavior: The server creates a reading plan and provides it through the user interface.

[1126] Step 4:

[1127] When a user is reading a particular chapter, they can request a summary or simple explanation. The server receives this request and uses a sentiment analysis engine to recognize the user's sentiment.

[1128] Input: User request (request for summary or brief explanation)

[1129] Output: User's emotional state and response (e.g., a more concise explanation, additional examples)

[1130] Specific operation: The server analyzes the user's emotions using an emotion analysis engine and generates an appropriate response.

[1131] Step 5:

[1132] The server periodically generates questions to assess the user's reading progress. The user answers the questions and submits them. The server receives these answers and evaluates the user's emotions using a sentiment analysis engine.

[1133] Input: User progress, answers to comprehension questions

[1134] Output: User comprehension assessment and sentiment data

[1135] What it does: The server generates questions and presents them to the user. The user submits answers, which the server then evaluates.

[1136] Step 6:

[1137] The server generates feedback based on the user's answers and emotional data, including advice on next steps or improving comprehension. The user receives this feedback and decides to proceed to the next chapter.

[1138] Input: User responses, emotion data

[1139] Output: Feedback (e.g., suggestions for next chapter, advice for improving comprehension)

[1140] What happens: The server generates feedback and provides it to the user.

[1141] (Application example 2)

[1142] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1143] Existing reading support systems face challenges in providing optimal support that takes into account the individual needs and emotional state of each user. It is particularly difficult to provide effective feedback when a user is struggling to understand a particular chapter or is behind in their progress. Another issue is the lack of real-time feedback that utilizes emotional data when assessing reading progress and comprehension.

[1144] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for inputting reading goals, a means for uploading electronic book data, a means for analyzing the content of the uploaded book and dividing it into chapters, a means for generating an individual reading plan based on the user's profile and book information, a means for providing the generated reading plan to the user, a means for periodically generating questions to assess the user's reading progress and comprehension, a means for evaluating the user's answers to the generated questions and providing feedback, a means for analyzing the user's emotional state using an emotion engine, and a means for optimizing feedback based on the analyzed emotion data. This enables optimal support based on the user's individual needs and emotional state, allowing for efficient reading. Furthermore, utilizing real-time emotion data enables accurate assessment of reading progress and comprehension, enabling effective feedback.

[1145] "Means for inputting reading goals" refers to an interface that allows users to input their reading goals and the objectives they wish to achieve into the system.

[1146] "Means for uploading electronic book data" is a function that allows users to upload electronic book data files to the system.

[1147] "Means for analyzing the contents of uploaded books and dividing them into chapters" refers to a function in which the system analyzes the electronic data of uploaded books and divides and organizes the contents into chapters.

[1148] "Means for generating an individual reading plan based on the user's profile and book information" refers to the function by which the system automatically generates a reading plan optimized for each user based on the user's profile information and the content of the book.

[1149] "Means for providing the user with the generated reading plan" refers to the function by which the system presents the user with the generated individual reading plan and allows the user to proceed with their reading based on it.

[1150] "Means for generating questions to periodically assess a user's reading progress and comprehension" refers to a function that allows the system to generate questions to check a user's comprehension and progress according to a certain period of time or reading progress.

[1151] "Means for evaluating the user's answers to the generated questions and providing feedback" refers to a function that analyzes the answers given by the user to the questions, evaluates their reading progress and level of understanding based on that, and provides appropriate feedback.

[1152] "Means for analyzing the user's emotional state using an emotion engine" refers to the function by which the system recognizes and analyzes emotions from the user's input and actions, and acquires that data.

[1153] The "means for optimizing feedback based on analyzed emotional data" is a function that provides feedback and support according to the emotional state of the user based on the analyzed emotional data.

[1154] This invention relates to a custom-made reading support system that utilizes generative AI and an emotion engine, allowing users to efficiently progress through reading according to their reading goals. This system is mainly composed of a server and a terminal, and operates as follows:

[1155] User Registration and Profiling

[1156] The server provides a new user registration form. The user enters their name, email address, and reading goal through their terminal and submits it. For example, the name is "Reader A," the email address is "reader@example.com," and the reading goal is "Improvement of Expertise." The server receives this information and creates a user profile. This profile is stored in a database.

[1157] Book information input and analysis

[1158] The server provides a form for uploading the book title, author, and electronic data file. The user enters the information of the book they want to read through their terminal and uploads the electronic data file. For example, the title is "Theory and Practice of Specialized Knowledge," the author is "Author B," and the electronic data file is "specialized_knowledge.pdf." The server receives this data and begins analysis. The analysis includes dividing the content of the uploaded book into chapters. The analysis results are also stored in the database.

[1159] Providing customized reading support

[1160] The server generates an individual reading plan based on the user's profile and book information. This reading plan includes a plan for the user to efficiently progress through the book. For example, it may include summaries and simple explanations of each chapter. The server provides this reading plan to the user. The user can start reading based on the plan provided and request summaries and simple explanations while reading a specific chapter.

[1161] Use of emotion engine

[1162] The server uses an emotion engine to recognize the user's emotions when they submit a request for a summary or brief explanation of a particular chapter. This emotion data is used to improve the user's reading experience. For example, if the user is having difficulty understanding, the server can provide a more concise explanation or additional examples.

[1163] Reading progress and comprehension assessment

[1164] The server periodically generates questions to evaluate the user's reading progress. These questions are intended to confirm the user's level of understanding. The user answers the questions from the server and submits them via their device. The server receives these answers and evaluates the user's emotions using an emotion engine. For example, if the user is excited, it encourages them to move on to the next chapter.

[1165] Providing Feedback

[1166] The server generates feedback based on the user's answers and sentiment data. This feedback is intended to improve the user's reading experience and includes advice on next steps and improving comprehension. The user receives this feedback and decides to proceed to the next chapter.

[1167] Specific examples

[1168] For example, consider a scenario in which a user, "Reader A," is reading a book called "Theory and Practice of Specialized Knowledge" to improve their expertise. "Reader A" logs in to the system, sets their reading goals, and then uploads the book's title, author, and electronic data. The server analyzes the data and generates and provides a reading plan that includes summaries and explanations for each chapter. "Reader A" reads efficiently according to this plan, and if they have difficulty understanding a particular chapter, they request a simpler explanation through the emotion engine. The server responds by providing additional explanation. The server periodically checks "Reader A's" progress and comprehension in the form of questions and evaluates their emotional data using the emotion engine. By providing feedback based on the results, it is possible to optimize "Reader A's" reading experience.

[1169] Specific examples of input prompts for the generative AI model used

[1170] For example, if "Reader A" finishes reading the first chapter and writes that he or she "doesn't understand," the following prompt can be used to connect with the emotion engine:

[1171] "User: Reader A. Goal: Improve expertise. Book: Theory and Practice of Expertise.

[1172] Reader A finished reading Chapter 1 and commented, "I don't understand it." Please analyze this sentiment with the emotion engine and generate feedback for the next step."

[1173] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1174] Step 1:

[1175] A user accesses the system through a terminal and displays a new user registration form. The user enters their name, email address, and reading goals and submits it. This information is sent to the server. The server creates a user profile based on the received information and stores it in a database. The input of this step is the user's personal information and reading goals, and the output is the user profile stored in the database.

[1176] Step 2:

[1177] The server provides a form for uploading the book title, author, and electronic data file. The user enters the book information through the terminal and uploads the electronic data file. This information is sent to the server. The server analyzes the received data and divides the book content into chapters. The analysis results are also stored in the database. The input of this step is the book information and electronic data file uploaded by the user, and the output is the analyzed book content for each chapter.

[1178] Step 3:

[1179] The server generates a personalized reading plan based on the user profile and book information, including chapter summaries and simple descriptions. The server then sends this reading plan to the user's device and displays it. The input for this step is the user profile and the parsed book content, and the output is the generated reading plan.

[1180] Step 4:

[1181] The user reads according to the reading plan provided through the terminal. While reading a particular chapter, the user sends a request to the server for a summary or simple explanation. The server receives this request and uses an emotion engine to analyze the user's emotion. The input of this step is the user's request, and the output is the analyzed emotion data.

[1182] Step 5:

[1183] The server provides the user with a more concise explanation or additional examples based on the emotion data. This feedback is sent to the user's device and displayed. The input of this step is the analyzed emotion data, and the output is the optimized feedback.

[1184] Step 6:

[1185] The server periodically generates questions to assess the user's reading progress, including questions to check comprehension. The questions are sent to the user's device and displayed. The input of this step is the user's reading progress, and the output is the generated questions.

[1186] Step 7:

[1187] The user answers questions through the terminal and sends the answers to the server. The server receives the answers and evaluates the user's emotions using the emotion engine. The input of this step is the user's answers, and the output is the evaluation result.

[1188] Step 8:

[1189] The server generates feedback based on the user's answers and emotional data. The feedback includes advice on how to proceed to the next step or improve understanding. The feedback is sent to the user's device and displayed. The inputs of this step are the evaluation results and the analyzed emotional data, and the output is the feedback.

[1190] Examples of use

[1191] The scenario involves "Reader A" starting to read a book called "Theory and Practice of Specialized Knowledge." "Reader A" logs into the system, sets a reading goal, and uploads the book's title, author, and electronic data. The server analyzes the data and generates and provides summaries and explanations for each chapter. If "Reader A" has difficulty understanding, the server responds to requests through the emotion engine and provides additional explanations. The server also periodically checks the progress and level of comprehension, evaluates the emotion data, and provides feedback.

[1192] Specific examples of input prompts for generative AI models

[1193] "User: Reader A. Goal: Improve expertise. Book: Theory and Practice of Expertise.

[1194] Reader A finished reading Chapter 1 and commented, "I don't understand it." Please analyze this sentiment with the emotion engine and generate feedback for the next step."

[1195] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1196] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1197] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1198] [Fourth embodiment]

[1199] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1200] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1201] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1202] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1203] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1204] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1205] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1206] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1207] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1208] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1209] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1210] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1211] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1212] This invention relates to a custom-made reading support system that utilizes generative AI, enabling users to efficiently progress through reading according to their reading goals. Specific embodiments of this system are described below.

[1213] User Registration and Profiling

[1214] The server provides a new user registration form.

[1215] The user inputs his / her name, email address, and reading goal, and submits the information. For example, the name is "Taro Tanaka," the email address is "taro@example.com," and the reading goal is "improving business knowledge."

[1216] The server receives this information and creates a user profile, which is stored in a database.

[1217] Book information input and analysis

[1218] The server provides a form for uploading book titles, authors and electronic data files.

[1219] Users enter information about the book they want to read and upload the electronic data file. For example, the title might be "Theory and Practice of Business Strategy," the author might be "Ichiro Suzuki," and the electronic data file might be "business_strategy.pdf."

[1220] The server receives this data and begins analyzing it, which involves splitting the uploaded book content into chapters. The results of this analysis are also stored in the database.

[1221] Providing customized reading support

[1222] The server generates a personalized reading plan based on the user's profile and book information. This reading plan includes a plan to help the user efficiently progress through the book, including chapter summaries and simple descriptions.

[1223] The server provides this reading plan to the user.

[1224] The user begins reading based on the plan provided, and while reading a particular chapter, can request a summary or simple explanation.

[1225] Reading progress and comprehension assessment

[1226] The server periodically generates questions to assess the user's reading progress, which are used to verify the user's level of comprehension.

[1227] The user answers questions from the server and submits them.

[1228] The server evaluates these answers and provides feedback, for example assessing whether the user has understood the key concepts of a particular chapter and helping them progress to the next chapter based on the results.

[1229] Specific examples

[1230] Consider a scenario in which a user, Taro Tanaka, reads a book called "Theory and Practice of Business Strategy" to improve his business knowledge. Taro Tanaka logs in to the system, sets his reading goals, and then uploads the book's title, author, and electronic data. The server analyzes the data and generates and provides a reading plan that includes summaries and explanations of each chapter. Taro Tanaka reads efficiently according to this plan and periodically answers questions from the server to check his understanding. The server evaluates his answers and provides feedback to support Taro Tanaka's reading.

[1231] This system allows Taro Tanaka to read efficiently and acquire the necessary knowledge in a short time. In addition, by lowering the barrier to reading, it is expected that the amount of reading will increase and the market will expand.

[1232] The processing flow will be explained below.

[1233] Step 1:

[1234] Users enter their name, email address, and reading goal into a registration form and submit it.

[1235] The server receives this registration information, creates a user profile, and stores it in a database.

[1236] Step 2:

[1237] The server provides a form that accepts book titles, authors, and uploads of electronic data files.

[1238] The user enters information about the book they want to read and uploads the electronic data file.

[1239] Step 3:

[1240] The server receives the uploaded electronic data and begins analyzing the book contents.

[1241] The server divides the book into chapters and stores the data of the divided chapters in a database.

[1242] Step 4:

[1243] The server generates a personalized reading plan based on the user's profile and book data.

[1244] This reading plan includes summaries and simple explanations for each chapter.

[1245] Step 5:

[1246] The server provides the generated reading plan to the user.

[1247] The user begins reading according to the provided reading plan.

[1248] Step 6:

[1249] A user submits a request for a summary or simple description of a particular chapter.

[1250] The server receives the request and generates the necessary summary or explanation to provide to the user.

[1251] Step 7:

[1252] The server periodically generates questions to assess the user's reading progress and comprehension.

[1253] The server sends the generated question to the user.

[1254] Step 8:

[1255] The user answers questions posed by the server.

[1256] The server receives the user's answers and evaluates their understanding.

[1257] Step 9:

[1258] The server generates and provides feedback to the user based on the user's answers.

[1259] This feedback allows the user to get advice on how to proceed to the next chapter or improve their understanding.

[1260] Example 1

[1261] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1262] Conventional reading support systems have difficulty providing efficient reading plans tailored to individual users' reading goals, and are inadequate in properly evaluating and providing feedback on users' reading progress and comprehension. They also lack the functionality to request summaries of specific chapters or simple explanations, hindering effective learning. Furthermore, it is difficult to provide personalized support based on user profiles, and the process of analyzing large amounts of book information is time-consuming.

[1263] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1264] In this invention, the server includes: a means for creating a user profile; a means for inputting reading goals; a means for uploading electronic book data; a means for analyzing the uploaded book content and dividing it into chapters; a means for generating an individual reading plan based on the user profile and book information; a means for providing the generated reading plan to the user; a means for periodically generating questions to assess the user's reading progress and comprehension; a means for evaluating the user's answers to the generated questions and providing feedback; and a means for generating reading plans and questions using a generative AI model. This allows for the provision of an efficient reading plan tailored to the user's individual reading goals, and for appropriate evaluation and feedback of the user's reading progress and comprehension. Furthermore, the server also provides a function for requesting summaries and simplified explanations of specific chapters, thereby supporting effective learning for the user.

[1265] "User Profile" refers to data containing a user's personal information and reading goals.

[1266] "Reading goal" refers to the purpose or goal a user wishes to achieve in reading.

[1267] "Electronic Book Data" refers to a file containing the contents of a Book stored in digital form.

[1268] "Upload" refers to the act of a user sending data from their own device to a server.

[1269] "Analysis" refers to the process of dividing the received electronic data of a book into content and extracting information.

[1270] A "chapter" refers to a section into which the contents of a book are divided.

[1271] "Reading plan" refers to data that includes a plan for efficiently progressing through reading in accordance with the user's reading goals.

[1272] "Question" refers to a question generated to assess a user's reading progress and comprehension.

[1273] "Feedback" refers to information about improvements and next steps provided based on the user's responses.

[1274] A "generative AI model" refers to an algorithm that uses artificial intelligence to automatically handle specific tasks.

[1275] A "summary" is a brief summary of the contents of each chapter of a book.

[1276] "Simple expression" refers to an explanation that simplifies complex content to make it easy to understand.

[1277] "Request" means a request sent by a User to a Server for particular information or functionality.

[1278] "Progress" refers to the process and progress of the user's reading.

[1279] "Support" refers to assistance provided to a user in taking the next step.

[1280] This invention relates to a custom-made reading support system that utilizes generative AI models. This system can efficiently guide users through reading according to their reading goals, and can appropriately evaluate the user's reading progress and comprehension and provide feedback.

[1281] Hardware and Software Configuration

[1282] The server acts as a web server, receiving requests from users and processing them accordingly. The server is built using, for example, the Python Flask framework. The database uses SQLAlchemy to manage user profiles and book information.

[1283] Users access the server from a browser using a device connected to the Internet (e.g., a PC, smartphone, or tablet).

[1284] Processing Details

[1285] First, the server presents a new user registration form, which contains fields where the user can enter their name, email address, and reading goal. After the user enters the information in the form and clicks the submit button, the server receives the data and creates a user profile, which is then stored in a database.

[1286] The server then provides a form for uploading the book title, author, and electronic data file. The user enters the book information in this form and uploads the electronic data, such as a PDF file. The server receives the uploaded data and uses the Python PyMuPDF library to parse the book and split it into chapters. The results of this analysis are also stored in the database.

[1287] The server then generates a personalized reading plan based on the user profile and book information. This plan uses OpenAI's GPT-3 API to prompt a generative AI model to generate summaries and simplified explanations. The generated reading plan is then provided to the user, for example, in PDF format or as a web page.

[1288] As the user reads, the server periodically generates questions to assess the user's progress. These questions are also generated using a generative AI model. The user answers the questions provided by the server, and the answers are sent to the server. The server evaluates the received answers using a natural language processing algorithm and provides feedback to the user. Based on this feedback, the server supports the user in progressing to the next chapter.

[1289] Specific examples

[1290] For example, consider a user who has a goal to "improve his business knowledge." He uploads a book called "Theory and Practice of Business Strategy" to the system. The server analyzes the book and generates summaries for each chapter. An example prompt for the generated code might look like this:

[1291] "Generate a summary of Chapter 1 of Business Strategy Theory and Practice."

[1292] "Generate questions for users to assess their reading progress."

[1293] "Please explain how the new reading plan will help Taro Tanaka improve his business knowledge."

[1294] In this way, the system provides efficient reading assistance tailored to the user's specific reading goals.

[1295] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1296] Step 1: Provide a user registration form

[1297] The server provides a web form for user registration using HTML and CSS.

[1298] The user enters profile information such as their name, email address, and desired genre of reading into the form and clicks the submit button. For example, the user enters their name "Yamada Taro," their email address "yamada@example.com," and their reading goal "skill acquisition."

[1299] (Input) Name, email address, and reading goal entered by the user

[1300] (Output) User registration data received by the server

[1301] The server stores the received data in a database using SQLAlchemy.

[1302] Step 2: Provide a book information input form

[1303] The server provides a form for uploading the book title, author, and PDF file.

[1304] The user enters the title of the book they want to read, the author's name, and the PDF file into the form and clicks the upload button. For example, enter the title "Technology Basics," the author "Ichiro Sato," and the PDF file "tech_basics.pdf."

[1305] (Input) User-entered book title, author name, and uploaded PDF file

[1306] (Output) Book information and electronic data files received by the server

[1307] The server analyzes the received PDF file using Python's PyMuPDF library and divides the content into chapters. The analysis results are also stored in a database.

[1308] Step 3: Generate a Reading Plan

[1309] The server generates a personalized reading plan based on the user profile and the analyzed book information.

[1310] Specifically, it uses OpenAI's GPT-3 API to send prompt sentences summarizing the uploaded book to a generative model to obtain a summary.

[1311] (Input) User profile, book information, prompt for GPT-3

[1312] (Output) Generated summary and reading plan

[1313] For example, generate a summary of "Technology Fundamentals" and create a plan such as "Read one chapter per week."

[1314] Step 4: Provide a reading plan

[1315] The server provides the generated reading plan to the user.

[1316] Specifically, the results are published in PDF format or as a web page, and users are notified by email.

[1317] (Input) Generated reading plan

[1318] (Output) Links to the reading plans that the user can access

[1319] Users can view and download the reading plan from the provided link.

[1320] Step 5: Generate questions to assess reading progress

[1321] The server periodically generates questions to assess the user's reading progress.

[1322] These questions are also generated using generative AI models, for example, to create questions that assess key points based on what you've read.

[1323] (Input) User's reading progress data, prompt text for GPT-3

[1324] (Output) A list of generated questions

[1325] Users periodically answer provided questions and send their answers to the server.

[1326] Step 6: Rate answers and provide feedback

[1327] The server evaluates the received user responses using natural language processing algorithms.

[1328] For example, it analyzes whether the answers are accurate and how much they understand.

[1329] (Input) User response data

[1330] (Output) Evaluation results and feedback

[1331] The server provides the user with customized feedback based on the evaluation results, supporting their progress to the next chapter.

[1332] These are the specific processing steps of this system's program. This flow allows users to study efficiently according to their reading goals. In addition, by receiving appropriate feedback based on their progress, they can acquire knowledge effectively.

[1333] (Application example 1)

[1334] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1335] While support systems for efficient reading existed in the past, they were difficult to customize to meet the user's reading goals or to provide detailed evaluations of reading progress and comprehension. Furthermore, efficiently analyzing large volumes of book data and generating summaries required significant time and effort. The present invention aims to solve these problems by providing an optimal reading plan tailored to the user's reading goals and supporting efficient reading.

[1336] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1337] In this invention, the server includes: means for inputting reading goals; means for uploading electronic book data; means for analyzing the content of the uploaded book and dividing it into chapters; means for generating summaries of the uploaded book using a generative AI model; means for incorporating the generated summaries into a reading plan; means for generating an individual reading plan based on a user's profile and book information; means for providing the generated reading plan to the user; means for periodically generating questions to evaluate the user's reading progress and comprehension; and means for evaluating the user's answers to the generated questions and providing feedback. This enables the provision of an optimal reading plan based on the user's reading goals, as well as evaluation and feedback of the user's progress and comprehension.

[1338] A "reading goal" is a specific purpose or task related to reading that a user wants to achieve.

[1339] "Electronic data of a book" refers to content data of a book stored in electronic form.

[1340] "Analysis" refers to the process of dividing the uploaded book into chapters and analyzing the content to make it easier to understand.

[1341] A "generative AI model" is an artificial intelligence model that summarizes the contents of a book or generates information tailored to the user's requests.

[1342] A "reading plan" is a plan for efficient reading based on the user's reading goals and profile.

[1343] A "summary" is information that condenses the contents of a book and extracts only the main points.

[1344] A "profile" is a collection of data such as a user's attribute information and reading goals.

[1345] A "question" is a server-generated question used to assess a user's reading progress and comprehension.

[1346] "Feedback" is evaluation and advice provided based on the user's answers.

[1347] This invention relates to a custom-made reading support system that utilizes generative AI, and enables users to efficiently progress through reading according to their reading goals. A specific embodiment of this system will be described.

[1348] User Registration and Profiling

[1349] The server provides a new user registration form. The user enters their name, email address, and reading goal, and submits it. For example, the name might be "Yamada Taro," the email address might be "taro@example.com," and the reading goal might be "improving business knowledge." The server receives this information and creates a user profile. This profile is stored in a database.

[1350] Book information input and analysis

[1351] The server provides a form for uploading the book title, author, and electronic data file. The user enters information about the book they want to read and uploads the electronic data file. For example, the title is "Theory and Practice of Business Strategy," the author is "Ichiro Suzuki," and the electronic data file is "business_strategy.pdf." The server receives this data and begins analysis. The analysis includes dividing the contents of the uploaded book into chapters. It also uses a generative AI model to generate a summary of the uploaded book. The analysis results and summary information are also stored in the database.

[1352] Providing customized reading support

[1353] The server generates an individual reading plan based on the user's profile and book information. This reading plan includes a plan for the user to efficiently progress through the book. For example, it includes summaries and simple explanations of each chapter. The user begins reading based on this reading plan. While reading a specific chapter, the user can request summaries and simple explanations.

[1354] Reading progress and comprehension assessment

[1355] The server periodically generates questions to assess the user's reading progress. These questions are designed to confirm the user's level of understanding. The user answers the questions from the server and submits them. The server evaluates these answers and provides feedback. For example, the server may evaluate whether the user has understood the key concepts of a particular chapter and help the user progress to the next chapter based on the results.

[1356] Hardware and software used

[1357] Hardware: Server (SQLite for database and server-side processing)

[1358] software:

[1359] SQLite: Used as a local database to manage user information, book information, and reading plans.

[1360] Transformers: Generate book summaries using the Hugging Face pipeline.

[1361] Examples and prompts

[1362] As a concrete example, let us consider a scenario in which a user named "Yamada Taro" uploads the book "Theory and Practice of Business Strategy" to the app with the goal of "improving his business knowledge." The app generates a summary of the book and provides it as a reading plan. Yamada Taro reads efficiently according to this plan, and periodically answers questions from the server to check his level of understanding.

[1363] An example of a prompt for the generative AI model is as follows:

[1364] Summarize the book's contents:

[1365] Book Title: Business Strategy Theory and Practice

[1366] Content: <Book content text uploaded here>

[1367] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1368] Step 1:

[1369] The server presents a new user registration form. The user enters their name, email address, and reading goal, then submits it. Based on the information entered, the server creates a user profile and stores it in a SQLite database.

[1370] Input: User's name, email address, reading goal

[1371] Output: User profile stored in the database

[1372] Step 2:

[1373] The server provides a form for uploading the book title, author, and electronic data file. The user enters the information about the book they want to read and uploads the electronic data file. The server receives this data and begins analyzing it.

[1374] Input: Book title, author, electronic data file

[1375] Output: Book data for analysis

[1376] Step 3:

[1377] The server then processes the uploaded book by dividing it into chapters. In this step, it analyzes the text in the book's electronic data file and performs data calculations to divide it into chapters. The analysis results are stored in a database.

[1378] Input: Contents of electronic data file

[1379] Output: Book data split by chapters

[1380] Step 4:

[1381] The server uses a generative AI model (such as the Hugging Face pipeline) to generate a summary for each chapter. This process takes the book's contents as input and performs data calculations to generate a summary. The generated summary is also stored in a database.

[1382] Input: Book data divided into chapters

[1383] Output: Summary of each chapter

[1384] Step 5:

[1385] The server generates a personalized reading plan based on the user's profile and book information, including chapter summaries and simple descriptions, and stores the plan in a database and provides it to the user.

[1386] Input: User profile, chapter summaries

[1387] Output: Individual Reading Plans

[1388] Step 6:

[1389] The user begins reading according to the personalized reading plan provided by the server. If a summary or brief explanation is needed during the reading, the user sends a request to the server, and the server responds by providing additional information.

[1390] Input: Reading plan, user request

[1391] Output: Additional information needed

[1392] Step 7:

[1393] The server periodically generates questions to assess the user's reading progress, and delivers the questions to the user, who then answers them.

[1394] Input: User's reading progress

[1395] Output: Assessment questions

[1396] Step 8:

[1397] The server receives and evaluates the user's answers, provides feedback based on the evaluation results, and supports the user in progressing to the next chapter.

[1398] Input: User's answer

[1399] Output: Evaluation results, feedback

[1400] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1401] This invention relates to a custom-made reading support system that utilizes generative AI and an emotion engine, and enables users to efficiently progress through reading according to their reading goals. Specific embodiments of this system are described below.

[1402] User Registration and Profiling

[1403] The server provides a new user registration form.

[1404] The user inputs his / her name, email address, and reading goal, and submits the information. For example, the name is "Taro Tanaka," the email address is "taro@example.com," and the reading goal is "improving business knowledge."

[1405] The server receives this information and creates a user profile, which is stored in a database.

[1406] Book information input and analysis

[1407] The server provides a form for uploading book titles, authors and electronic data files.

[1408] Users enter information about the book they want to read and upload the electronic data file. For example, the title might be "Theory and Practice of Business Strategy," the author might be "Ichiro Suzuki," and the electronic data file might be "business_strategy.pdf."

[1409] The server receives this data and begins analyzing it, which involves splitting the uploaded book content into chapters. The results of this analysis are also stored in the database.

[1410] Providing customized reading support

[1411] The server generates a personalized reading plan based on the user's profile and book information. This reading plan includes a plan to help the user efficiently progress through the book, including chapter summaries and simple descriptions.

[1412] The server provides this reading plan to the user.

[1413] The user begins reading based on the plan provided, and while reading a particular chapter, can request a summary or simple explanation.

[1414] Use of emotion engine

[1415] The server uses an emotion engine to recognize the user's emotions when they submit a request for a summary or simple explanation of a particular chapter. This emotion data is used to improve the user's reading experience. For example, if the user is struggling to understand, the server can provide a more concise explanation or additional examples.

[1416] Reading progress and comprehension assessment

[1417] The server periodically generates questions to assess the user's reading progress, which are used to verify the user's level of comprehension.

[1418] The user answers questions from the server and submits them.

[1419] The server receives these responses and also evaluates the user's emotions using an emotion engine, for example encouraging the user to proceed to the next chapter if they are excited.

[1420] Providing Feedback

[1421] The server generates feedback based on the user's answers and sentiment data to improve the user's reading experience, including advice on next steps and improving comprehension.

[1422] The user receives this feedback and decides to proceed to the next chapter.

[1423] Specific examples

[1424] Consider a scenario in which user Tanaka Taro is reading a book called "Theory and Practice of Business Strategy" to improve his business knowledge. Tanaka Taro logs in to the system, sets his reading goals, and then uploads the book's title, author, and electronic data. The server analyzes the data and generates and provides a reading plan that includes summaries and explanations for each chapter. Tanaka Taro reads efficiently according to this plan, and if he has difficulty understanding a particular chapter, he requests a simpler explanation through the emotion engine. The server responds by providing additional explanation. Periodically, the server checks Tanaka Taro's progress and comprehension in the form of questions and evaluates his emotion data using the emotion engine. Based on the results, the server provides feedback to optimize Tanaka Taro's reading experience.

[1425] This system allows Taro Tanaka to read efficiently and acquire the necessary knowledge in a short time. In addition, by utilizing the emotion engine, it is possible to provide optimal support according to individual needs and emotions.

[1426] The processing flow will be explained below.

[1427] This invention relates to a custom-made reading support system that utilizes generative AI and an emotion engine, allowing users to efficiently progress through reading according to their reading goals. A specific embodiment of this system will be described below, divided into processing steps.

[1428] Step 1:

[1429] The user enters his / her name, email address, and reading goal in the registration form and submits it. For example, the name is "Taro Tanaka," the email address is "taro@example.com," and the reading goal is "improving business knowledge."

[1430] The server receives this registration information, creates a user profile, and stores it in a database.

[1431] Step 2:

[1432] The server provides a form that accepts book titles, authors, and uploads of electronic data files.

[1433] Users enter information about the book they want to read and upload the electronic data file. For example, the title might be "Theory and Practice of Business Strategy," the author might be "Ichiro Suzuki," and the electronic data file might be "business_strategy.pdf."

[1434] Step 3:

[1435] The server receives the uploaded electronic data and begins analyzing the book contents.

[1436] The server divides the book into chapters and stores the data of the divided chapters in a database.

[1437] Step 4:

[1438] The server generates a personalized reading plan based on the user's profile and book data.

[1439] This reading plan includes summaries and simple explanations for each chapter.

[1440] Step 5:

[1441] The server provides the generated reading plan to the user.

[1442] The user begins reading according to the provided reading plan.

[1443] Step 6:

[1444] A user submits a request for a summary or simple description of a particular chapter, for example, "Please give me a summary of Chapter 1."

[1445] The server receives the request, generates the necessary summary and explanation, and provides it to the user, including using an emotion engine to analyze the user's emotion at the time of the request and providing it in the most appropriate format based on that emotion.

[1446] Step 7:

[1447] The server periodically generates questions to assess the user's reading progress and comprehension, such as "What are the key strategies discussed in Chapter 1?"

[1448] The server sends the generated question to the user.

[1449] Step 8:

[1450] The user answers questions from the server, for example, "The primary strategy is cost leadership."

[1451] The server receives the user's answers and evaluates their understanding.

[1452] Step 9:

[1453] The server generates and provides feedback to the user based on the user's answers.

[1454] This feedback allows the user to get advice on how to proceed to the next chapter or improve their understanding.

[1455] Step 10:

[1456] The emotion engine constantly monitors the user's emotions while reading, and if it detects that the user is feeling stressed, it will provide simple expressions or suggest taking a break, thereby improving the user's reading experience.

[1457] Specific examples

[1458] Consider a scenario in which user Tanaka Taro is reading a book called "Theory and Practice of Business Strategy" to improve his business knowledge. Tanaka Taro logs in to the system, sets his reading goals, and then uploads the book's title, author, and electronic data. The server analyzes the data and generates and provides a reading plan that includes summaries and explanations for each chapter. Tanaka Taro reads efficiently according to this plan, and if he has difficulty understanding a particular chapter, he requests a simpler explanation through the emotion engine. The server responds by providing additional explanation. The server periodically checks Tanaka Taro's progress and level of understanding in the form of questions and evaluates his emotion data at that time using the emotion engine. Based on the results, the server provides feedback to optimize Tanaka Taro's reading experience.

[1459] Example 2

[1460] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1461] Conventional reading support systems do not consider how to efficiently progress through reading, and in particular lack individualized support based on the user's reading goals and emotional state. They also lacked measures to address difficulties when users were having difficulty understanding the content of a book, and did not provide effective feedback to optimize the user's reading experience. Furthermore, they lacked a means to regularly evaluate the user's reading progress and comprehension and provide appropriate feedback.

[1462] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1463] In this invention, the server includes means for inputting reading goals, means for uploading electronic data of books, means for analyzing the content of the uploaded books and dividing them into chapters, means for generating an individual reading plan based on the user's profile and book information, means for providing the generated reading plan to the user, means for accepting requests from the user for summaries and brief explanations, means for generating responses to the accepted requests using a sentiment analysis engine, means for periodically generating questions to evaluate the user's reading progress and comprehension, and means for evaluating the user's answers to the generated questions and providing feedback, thereby optimizing the user's reading experience and enabling efficient reading progress, support for comprehension, and feedback tailored to individual needs.

[1464] The "means for inputting reading goals" is an interface that allows users to input their own reading purposes and goals into the system.

[1465] The "means for uploading electronic book data" is an interface that allows users to upload electronic files of books they wish to read to the system.

[1466] The "means for analyzing the contents of the uploaded book and dividing it into chapters" refers to an algorithm or program that analyzes the contents of the e-book file and divides it into chapters.

[1467] The "means for generating an individual reading plan based on a user's profile and book information" is a program for automatically creating an efficient reading plan based on user information and book contents.

[1468] The "means for providing the generated reading plan to the user" refers to an interface or notification function for presenting the generated reading plan to the user.

[1469] The "means for accepting a request for a summary or a brief explanation from a user" is an interface that allows a user to request additional information or a brief explanation of the content from the system.

[1470] The "means for generating a response using an emotion analysis engine" refers to an algorithm or program for analyzing a user's emotion and level of understanding and generating an appropriate response.

[1471] "Means for generating questions to periodically assess a user's reading progress and level of comprehension" refers to a program that enables the system to automatically generate questions to check a user's reading progress and level of comprehension.

[1472] The "means for evaluating the user's answers to the generated questions and providing feedback" is a program that analyzes the user's answers and provides appropriate advice and recommendations for the next learning step.

[1473] The present invention relates to a custom-made reading support system that uses generative AI and a sentiment analysis engine, and enables users to efficiently progress through reading according to their reading goals. Specific embodiments of this system are described below.

[1474] This system operates according to the following steps: Each process is executed by either the server, the terminal, or the user.

[1475] User Registration and Profiling

[1476] The server provides a new user registration form, which includes fields for name, email address, and reading goal.

[1477] The user inputs and submits this information. For example, the user's name is "Yamada Taro," the user's email address is "taro@example.com," and the user's reading goal is "improving business knowledge."

[1478] The server creates a user profile based on the received information and stores it in a database.

[1479] Book information input and analysis

[1480] The server provides a form for uploading book titles, authors and electronic data files.

[1481] The user enters information about the book they want to read and uploads the electronic data file. For example, the title is "Theory and Practice of Business Strategy," the author is "Ichiro Sato," and the electronic data file is "business_strategy.pdf."

[1482] The server receives this data, analyzes the contents of the book using AI, and divides it into chapters. The results of this analysis are also stored in a database.

[1483] Providing customized reading support

[1484] The server generates a personalized reading plan based on the user's profile and book information, including chapter summaries and simple descriptions.

[1485] The server provides this reading plan to the user.

[1486] The user begins reading based on the plan provided, and while reading a particular chapter, can request a summary or simple explanation.

[1487] Use of emotion engine

[1488] The server uses a sentiment analysis engine to recognize the user's emotions when they submit a request for a summary or simple explanation of a particular chapter. This emotional data is used to improve the user's reading experience. For example, if the user is struggling to understand, the server can provide a more concise explanation or additional examples.

[1489] Reading progress and comprehension assessment

[1490] The server periodically generates questions to assess the user's reading progress, which are used to verify the user's level of comprehension.

[1491] The user answers questions from the server and submits them.

[1492] The server receives these responses and uses a sentiment analysis engine to evaluate the user's emotions as well, for example encouraging them to move on to the next chapter if they are excited.

[1493] Providing Feedback

[1494] The server generates feedback based on the user's answers and sentiment data, including advice on next steps to take or improve understanding.

[1495] The user receives this feedback and decides to proceed to the next chapter.

[1496] Specific examples

[1497] As an example, consider a scenario in which user Yamada Taro is reading a book called "Theory and Practice of Business Strategy" to improve his business knowledge. Yamada Taro logs in to the system, sets his reading goals, and then uploads the book's title, author, and electronic data. The server analyzes the data and generates and provides a reading plan that includes summaries and explanations for each chapter. Yamada Taro reads efficiently according to this plan, and if he has difficulty understanding a particular chapter, he requests a simpler explanation through the emotion engine. The server responds by providing additional explanation. Periodically, the server checks Yamada Taro's progress and level of understanding in the form of questions and evaluates the emotional data using the emotion analysis engine. Based on the results, it provides feedback to optimize Yamada Taro's reading experience.

[1498] Usage example (example of prompt for generative AI model)

[1499] Please provide a brief summary of Chapter 1 of "The Theory and Practice of Business Strategy."

[1500] I find this chapter difficult. Can you use the Emotion Engine to provide a simpler explanation?

[1501] Generate feedback that encourages progression to the next chapter.

[1502] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1503] Step 1:

[1504] The server presents a new user registration form. The user enters their name, email address, and reading goals, and submits it. The server receives this information, generates a user profile, and stores it in a database.

[1505] Input: User's name, email address, reading goal

[1506] Output: User profile (e.g. user ID, name, email address, reading goal)

[1507] What happens: The server generates a user profile and stores it in a database.

[1508] Step 2:

[1509] The server provides a form for uploading the book title, author, and electronic data file. The user enters this information and uploads the electronic data file. The server receives this data and begins analyzing it.

[1510] Input: Book title, author, electronic data file

[1511] Output: Contents of each chapter of the book (analysis results)

[1512] Specific operation: The server analyzes the electronic data file, divides the book contents into chapters, and stores the analysis results in a database.

[1513] Step 3:

[1514] The server generates an individual reading plan based on the user profile and book information, provides the generated reading plan to the user, and the user begins reading based on this plan.

[1515] Input: User profile, book information (analysis results)

[1516] Output: Individual reading plan (e.g., chapter summaries, brief descriptions)

[1517] Specific behavior: The server creates a reading plan and provides it through the user interface.

[1518] Step 4:

[1519] When a user is reading a particular chapter, they can request a summary or simple explanation. The server receives this request and uses a sentiment analysis engine to recognize the user's sentiment.

[1520] Input: User request (request for summary or brief explanation)

[1521] Output: User's emotional state and response (e.g., a more concise explanation, additional examples)

[1522] Specific operation: The server analyzes the user's emotions using an emotion analysis engine and generates an appropriate response.

[1523] Step 5:

[1524] The server periodically generates questions to assess the user's reading progress. The user answers the questions and submits them. The server receives these answers and evaluates the user's emotions using a sentiment analysis engine.

[1525] Input: User progress, answers to comprehension questions

[1526] Output: User comprehension assessment and sentiment data

[1527] What it does: The server generates questions and presents them to the user. The user submits answers, which the server then evaluates.

[1528] Step 6:

[1529] The server generates feedback based on the user's answers and emotional data, including advice on next steps or improving comprehension. The user receives this feedback and decides to proceed to the next chapter.

[1530] Input: User responses, emotion data

[1531] Output: Feedback (e.g., suggestions for next chapter, advice for improving comprehension)

[1532] What happens: The server generates feedback and provides it to the user.

[1533] (Application example 2)

[1534] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1535] Existing reading support systems face challenges in providing optimal support that takes into account the individual needs and emotional state of each user. It is particularly difficult to provide effective feedback when a user is struggling to understand a particular chapter or is behind in their progress. Another issue is the lack of real-time feedback that utilizes emotional data when assessing reading progress and comprehension.

[1536] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for inputting reading goals, a means for uploading electronic book data, a means for analyzing the content of the uploaded book and dividing it into chapters, a means for generating an individual reading plan based on the user's profile and book information, a means for providing the generated reading plan to the user, a means for periodically generating questions to assess the user's reading progress and comprehension, a means for evaluating the user's answers to the generated questions and providing feedback, a means for analyzing the user's emotional state using an emotion engine, and a means for optimizing feedback based on the analyzed emotion data. This enables optimal support based on the user's individual needs and emotional state, allowing for efficient reading. Furthermore, utilizing real-time emotion data enables accurate assessment of reading progress and comprehension, enabling effective feedback.

[1537] "Means for inputting reading goals" refers to an interface that allows users to input their reading goals and the objectives they wish to achieve into the system.

[1538] "Means for uploading electronic book data" is a function that allows users to upload electronic book data files to the system.

[1539] "Means for analyzing the contents of uploaded books and dividing them into chapters" refers to a function in which the system analyzes the electronic data of uploaded books and divides and organizes the contents into chapters.

[1540] "Means for generating an individual reading plan based on the user's profile and book information" refers to the function by which the system automatically generates a reading plan optimized for each user based on the user's profile information and the content of the book.

[1541] "Means for providing the user with the generated reading plan" refers to the function by which the system presents the user with the generated individual reading plan and allows the user to proceed with their reading based on it.

[1542] "Means for generating questions to periodically assess a user's reading progress and comprehension" refers to a function that allows the system to generate questions to check a user's comprehension and progress according to a certain period of time or reading progress.

[1543] "Means for evaluating the user's answers to the generated questions and providing feedback" refers to a function that analyzes the answers given by the user to the questions, evaluates their reading progress and level of understanding based on that, and provides appropriate feedback.

[1544] "Means for analyzing the user's emotional state using an emotion engine" refers to the function by which the system recognizes and analyzes emotions from the user's input and actions, and acquires that data.

[1545] The "means for optimizing feedback based on analyzed emotional data" is a function that provides feedback and support according to the emotional state of the user based on the analyzed emotional data.

[1546] This invention relates to a custom-made reading support system that utilizes generative AI and an emotion engine, allowing users to efficiently progress through reading according to their reading goals. This system is mainly composed of a server and a terminal, and operates as follows:

[1547] User Registration and Profiling

[1548] The server provides a new user registration form. The user enters their name, email address, and reading goal through their terminal and submits it. For example, the name is "Reader A," the email address is "reader@example.com," and the reading goal is "Improvement of Expertise." The server receives this information and creates a user profile. This profile is stored in a database.

[1549] Book information input and analysis

[1550] The server provides a form for uploading the book title, author, and electronic data file. The user enters the information of the book they want to read through their terminal and uploads the electronic data file. For example, the title is "Theory and Practice of Specialized Knowledge," the author is "Author B," and the electronic data file is "specialized_knowledge.pdf." The server receives this data and begins analysis. The analysis includes dividing the content of the uploaded book into chapters. The analysis results are also stored in the database.

[1551] Providing customized reading support

[1552] The server generates an individual reading plan based on the user's profile and book information. This reading plan includes a plan for the user to efficiently progress through the book. For example, it may include summaries and simple explanations of each chapter. The server provides this reading plan to the user. The user can start reading based on the plan provided and request summaries and simple explanations while reading a specific chapter.

[1553] Use of emotion engine

[1554] The server uses an emotion engine to recognize the user's emotions when they submit a request for a summary or brief explanation of a particular chapter. This emotion data is used to improve the user's reading experience. For example, if the user is having difficulty understanding, the server can provide a more concise explanation or additional examples.

[1555] Reading progress and comprehension assessment

[1556] The server periodically generates questions to evaluate the user's reading progress. These questions are intended to confirm the user's level of understanding. The user answers the questions from the server and submits them via their device. The server receives these answers and evaluates the user's emotions using an emotion engine. For example, if the user is excited, it encourages them to move on to the next chapter.

[1557] Providing Feedback

[1558] The server generates feedback based on the user's answers and sentiment data. This feedback is intended to improve the user's reading experience and includes advice on next steps and improving comprehension. The user receives this feedback and decides to proceed to the next chapter.

[1559] Specific examples

[1560] For example, consider a scenario in which a user, "Reader A," is reading a book called "Theory and Practice of Specialized Knowledge" to improve their expertise. "Reader A" logs in to the system, sets their reading goals, and then uploads the book's title, author, and electronic data. The server analyzes the data and generates and provides a reading plan that includes summaries and explanations for each chapter. "Reader A" reads efficiently according to this plan, and if they have difficulty understanding a particular chapter, they request a simpler explanation through the emotion engine. The server responds by providing additional explanation. The server periodically checks "Reader A's" progress and comprehension in the form of questions and evaluates their emotional data using the emotion engine. By providing feedback based on the results, it is possible to optimize "Reader A's" reading experience.

[1561] Specific examples of input prompts for the generative AI model used

[1562] For example, if "Reader A" finishes reading the first chapter and writes that he or she "doesn't understand," the following prompt can be used to connect with the emotion engine:

[1563] "User: Reader A. Goal: Improve expertise. Book: Theory and Practice of Expertise.

[1564] Reader A finished reading Chapter 1 and commented, "I don't understand it." Please analyze this sentiment with the emotion engine and generate feedback for the next step."

[1565] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1566] Step 1:

[1567] A user accesses the system through a terminal and displays a new user registration form. The user enters their name, email address, and reading goals and submits it. This information is sent to the server. The server creates a user profile based on the received information and stores it in a database. The input of this step is the user's personal information and reading goals, and the output is the user profile stored in the database.

[1568] Step 2:

[1569] The server provides a form for uploading the book title, author, and electronic data file. The user enters the book information through the terminal and uploads the electronic data file. This information is sent to the server. The server analyzes the received data and divides the book content into chapters. The analysis results are also stored in the database. The input of this step is the book information and electronic data file uploaded by the user, and the output is the analyzed book content for each chapter.

[1570] Step 3:

[1571] The server generates a personalized reading plan based on the user profile and book information, including chapter summaries and simple descriptions. The server then sends this reading plan to the user's device and displays it. The input for this step is the user profile and the parsed book content, and the output is the generated reading plan.

[1572] Step 4:

[1573] The user reads according to the reading plan provided through the terminal. While reading a particular chapter, the user sends a request to the server for a summary or simple explanation. The server receives this request and uses an emotion engine to analyze the user's emotion. The input of this step is the user's request, and the output is the analyzed emotion data.

[1574] Step 5:

[1575] The server provides the user with a more concise explanation or additional examples based on the emotion data. This feedback is sent to the user's device and displayed. The input of this step is the analyzed emotion data, and the output is the optimized feedback.

[1576] Step 6:

[1577] The server periodically generates questions to assess the user's reading progress, including questions to check comprehension. The questions are sent to the user's device and displayed. The input of this step is the user's reading progress, and the output is the generated questions.

[1578] Step 7:

[1579] The user answers questions through the terminal and sends the answers to the server. The server receives the answers and evaluates the user's emotions using the emotion engine. The input of this step is the user's answers, and the output is the evaluation result.

[1580] Step 8:

[1581] The server generates feedback based on the user's answers and emotional data. The feedback includes advice on how to proceed to the next step or improve understanding. The feedback is sent to the user's device and displayed. The inputs of this step are the evaluation results and the analyzed emotional data, and the output is the feedback.

[1582] Examples of use

[1583] The scenario involves "Reader A" starting to read a book called "Theory and Practice of Specialized Knowledge." "Reader A" logs into the system, sets a reading goal, and uploads the book's title, author, and electronic data. The server analyzes the data and generates and provides summaries and explanations for each chapter. If "Reader A" has difficulty understanding, the server responds to requests through the emotion engine and provides additional explanations. The server also periodically checks the progress and level of comprehension, evaluates the emotion data, and provides feedback.

[1584] Specific examples of input prompts for generative AI models

[1585] "User: Reader A. Goal: Improve expertise. Book: Theory and Practice of Expertise.

[1586] Reader A finished reading Chapter 1 and commented, "I don't understand it." Please analyze this sentiment with the emotion engine and generate feedback for the next step."

[1587] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1588] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1589] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1590] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1591] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1592] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1593] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1594] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1595] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1596] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1597] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1598] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1599] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1600] 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.

[1601] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1602] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1603] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1604] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1605] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1606] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1607] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1608] The following is further disclosed regarding the above embodiment.

[1609] (Claim 1)

[1610] a means for inputting reading goals;

[1611] A means for uploading electronic data of books;

[1612] A means to analyze the contents of the uploaded book and divide it into chapters,

[1613] means for generating a personalized reading plan based on the user's profile and book information;

[1614] a means for providing the generated reading plan to a user;

[1615] means for generating questions to periodically assess a user's reading progress and comprehension;

[1616] means for evaluating the user's answers to the generated questions and providing feedback;

[1617] A system including:

[1618] (Claim 2)

[1619] 10. The system of claim 1, further comprising means for a user to submit a request for a summary or simplified description of a particular chapter.

[1620] (Claim 3)

[1621] 2. The system according to claim 1, further comprising means for supporting the user to proceed to the next chapter according to the user's progress.

[1622] "Example 1"

[1623] (Claim 1)

[1624] means for creating a user profile;

[1625] a means for inputting reading goals;

[1626] A means for uploading electronic data of books;

[1627] A means to analyze the contents of the uploaded book and divide it into chapters,

[1628] means for generating a personalized reading plan based on the user profile and book information;

[1629] a means for providing the generated reading plan to a user;

[1630] means for generating questions to periodically assess a user's reading progress and comprehension;

[1631] means for evaluating the user's answers to the generated questions and providing feedback;

[1632] means for generating reading plans and questions using a generative AI model;

[1633] A system including:

[1634] (Claim 2)

[1635] 10. The system of claim 1, further comprising means for a user to submit a request for a summary or simplified description of a particular chapter.

[1636] (Claim 3)

[1637] 10. The system according to claim 1, further comprising means for supporting the user to proceed to the next chapter according to the user's progress.

[1638] "Application Example 1"

[1639] (Claim 1)

[1640] a means for inputting reading goals;

[1641] A means for uploading electronic data of books;

[1642] A means to analyze the contents of the uploaded book and divide it into chapters,

[1643] means for generating a personalized reading plan based on the user's profile and book information;

[1644] a means for generating summaries of uploaded books using a generative AI model;

[1645] A means of incorporating the generated summaries into a reading plan;

[1646] a means for providing the generated reading plan to a user;

[1647] means for generating questions to periodically assess a user's reading progress and comprehension;

[1648] means for evaluating the user's answers to the generated questions and providing feedback;

[1649] A system including:

[1650] (Claim 2)

[1651] 10. The system of claim 1, further comprising means for a user to submit a request for a summary or simplified description of a particular chapter.

[1652] (Claim 3)

[1653] 10. The system according to claim 1, further comprising means for supporting the user to proceed to the next chapter according to the user's progress.

[1654] "Example 2: Combining Emotion Engines"

[1655] (Claim 1)

[1656] a means for inputting reading goals;

[1657] A means for uploading electronic data of books;

[1658] A means to analyze the contents of the uploaded book and divide it into chapters,

[1659] means for generating a personalized reading plan based on the user's profile and book information;

[1660] a means for providing the generated reading plan to a user;

[1661] a means for receiving a request from a user for a summary or brief description;

[1662] a means for generating a response to the received request using a sentiment analysis engine;

[1663] means for generating questions to periodically assess a user's reading progress and comprehension;

[1664] means for evaluating the user's answers to the generated questions and providing feedback;

[1665] A system including:

[1666] (Claim 2)

[1667] 10. The system of claim 1, further comprising means for assessing a user's emotional state using a sentiment analysis engine and providing a more concise explanation or additional examples if the user is having difficulty understanding.

[1668] (Claim 3)

[1669] 2. The system according to claim 1, further comprising means for supporting the user to proceed to the next chapter according to the user's progress.

[1670] "Application example 2 when combining emotion engines"

[1671] (Claim 1)

[1672] a means for inputting reading goals;

[1673] A means for uploading electronic data of books;

[1674] A means to analyze the contents of the uploaded book and divide it into chapters,

[1675] means for generating a personalized reading plan based on the user's profile and book information;

[1676] a means for providing the generated reading plan to a user;

[1677] a means for generating questions to periodically assess a user's reading progress and comprehension;

[1678] a means for evaluating user responses to generated questions and providing feedback;

[1679] means for analyzing the emotional state of a user using an emotion engine;

[1680] a means for optimizing feedback based on the analyzed emotion data; and

[1681] A system including:

[1682] (Claim 2)

[1683] 10. The system of claim 1, further comprising means for a user to submit a request for a summary or simplified description of a particular chapter.

[1684] (Claim 3)

[1685] 2. The system according to claim 1, further comprising means for supporting the user to proceed to the next chapter according to the user's progress and the analyzed emotion data. [Explanation of symbols]

[1686] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for inputting reading goals; A means for uploading electronic data of books; A means to analyze the contents of the uploaded book and divide it into chapters, means for generating a personalized reading plan based on the user's profile and book information; a means for providing the generated reading plan to a user; means for generating questions to periodically assess a user's reading progress and comprehension; means for evaluating the user's answers to the generated questions and providing feedback; A system including:

2. 10. The system of claim 1, further comprising means for a user to submit a request for a summary or simple description of a particular chapter.

3. 2. The system according to claim 1, further comprising means for supporting the user to proceed to the next chapter according to the user's progress.

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