system

Abstract

We provide the system. [Solution] A database means for inputting and storing user information, A natural language processing system for generating user interactions and extracting information, A text generation means for generating and editing text based on extracted information, Image generation means for generating images and designs, A system that includes publishing support means for outputting generated content in a publishing format.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The process of an individual creating a book by leveraging their past experiences and knowledge is highly difficult because it requires specialized techniques and knowledge, and many people are unable to complete this process. As a result, there is a problem that valuable practical experiences and knowledge are buried without being widely shared.

Means for Solving the Problems

[0005] The present invention provides a system that includes a database means for inputting and storing user information, a natural language processing means for generating user interactions and extracting information, a text generation means for generating and editing text based on the extracted information, an image generation means for generating images and designs, and a publishing support means for outputting the generated content in a publishing format. This system enables users to efficiently and effectively create and widely share books based on their own experiences and knowledge, even without specialized knowledge.

[0006] "User information" refers to attribute data such as name, occupation, and areas of interest provided by the system's users.

[0007] "Database means" refers to a digital storage system for efficiently storing and managing user information.

[0008] "Natural language processing means" refers to technologies that extract information through interaction with the user, analyze the input, and identify important points.

[0009] "Text generation means" refers to technology that generates text based on extracted information and edits it into book format.

[0010] "Image generation means" refers to technology that automatically generates insert images and cover designs that match the content of a book.

[0011] "Publishing support methods" refer to technologies that provide assistance in distributing generated content in the form of printed books or ebooks. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3]It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0014] First, the language used in the following description will be explained.

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention provides a system that facilitates the creation of books based on the experiences and knowledge of specific users. The system is configured as follows:

[0034] First, the user accesses the system and enters their profile information and areas of interest. The server then saves this user information to a database and prepares to generate personalized content based on this data.

[0035] Next, the server generates a list of interview questions using natural language processing technology based on the theme set by the user. These questions are customized to specifically elicit the user's experiences and knowledge.

[0036] Users answer generated questions via their devices. The answers are sent to a server in real time and converted into text using speech recognition technology. The server then analyzes the text data using natural language processing to extract the information necessary for the book.

[0037] Based on the extracted information, the server uses a text generation system to create a document. The document is structured from an introduction to a conclusion, reflecting the user's intent and structural requirements.

[0038] Subsequently, the server uses image generation tools to create insert images and cover designs for use in the book. This makes it easier for users to visualize the overall structure of the book, including its visual content.

[0039] The generated text and images are presented to the user via their device, allowing for modifications and feedback. If necessary, the server re-edits the content to reflect the user's feedback.

[0040] Ultimately, the server utilizes publishing support tools to facilitate publication in the user's chosen format (print or ebook). This process includes automated procedures and the selection of optimized distribution channels.

[0041] For example, if a business person in their 40s wants to create a book about their sales experience, this system can effectively generate text and design, allowing them to prepare the book for publication in a short period of time. In this way, by implementing this invention, anyone can easily create and widely share a book.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] Users access the system using their devices and create accounts. They enter profile information and topics of interest and send it to the server. The server stores the received information in a database and prepares it for use in subsequent processes.

[0045] Step 2:

[0046] The server uses natural language processing technology to generate a list of interview questions based on the theme selected by the user. This list of questions is customized to elicit the user's experience and knowledge and is used in the next interview step.

[0047] Step 3:

[0048] The terminal initiates an interview session based on a pre-set schedule and presents the user with questions generated by the server. The user answers these questions verbally or in text.

[0049] Step 4:

[0050] The server processes user responses in real time and converts them into text using speech recognition technology. The converted data is then analyzed using natural language processing to extract essential information necessary for book creation.

[0051] Step 5:

[0052] Based on the extracted key information, the server uses text generation tools to create text that follows the structure of a book. The text includes each chapter, from the introduction to the conclusion, and fully reflects the content of the user interviews.

[0053] Step 6:

[0054] The server uses image generation capabilities to create the necessary insert images and cover designs for the book. Designs are created according to user settings and themes, enriching the visual content.

[0055] Step 7:

[0056] The system prompts the user to review the generated text and images through their device. The user reviews the content and provides feedback if there are any corrections or additional requests.

[0057] Step 8:

[0058] The server makes necessary edits and adjustments based on user feedback. This editing cycle may be repeated until the content is reviewed again and the final book draft is finalized.

[0059] Step 9:

[0060] The server uses publishing support tools to publish books in the format specified by the user (print book or e-book). It automates publishing procedures such as obtaining an ISBN and setting up sales channels, and then asks the user for final confirmation.

[0061] (Example 1)

[0062] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0063] For users, effectively compiling their experiences and knowledge to create a book has traditionally required significant time and specialized skills, making it a high hurdle for the average person. To address this challenge, a newly developed system is needed to enable anyone to easily create a book and streamline the entire publishing process.

[0064] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0065] In this invention, the server includes information recording means for inputting and storing user information, natural language processing means for automatically generating interview questions based on themes set by the user, and recognition means for converting the user's answers into text data via speech recognition. This makes it possible for users to easily and quickly create books that reflect their own experiences and knowledge without requiring high technical knowledge.

[0066] "Information recording means" refers to a device or configuration that has the function of temporarily or permanently storing profile information and themes entered by users.

[0067] "Natural language processing means" refers to a technology or module used to generate interview questions based on a theme set by the user and to analyze the user's responses.

[0068] "Recognition means" refers to a system or component that uses speech recognition technology to convert a user's voice response into text data.

[0069] A "text generation model" is an algorithm or model that utilizes artificial intelligence to automatically generate coherent texts and structures based on extracted knowledge data.

[0070] An "image generation device" is a device or software for generating visual content, such as images to be inserted into a book or cover designs, for automatic creation.

[0071] An "editing cycle mechanism" is a function or process that receives feedback from users and modifies and re-edits the generated content based on that feedback.

[0072] "Publishing support tools" refer to systems or tools that output completed content in the most suitable format as a paper book or e-book, and provide support for publishing procedures and distribution.

[0073] This invention is a system that makes it easier for users to create books based on their own experience and knowledge. The system consists of a server, terminals, and various artificial intelligence technologies.

[0074] Users access the system through their terminal and, as part of the initial setup, enter their profile information and topics of interest. For example, they might set the topic to "Best Practices for Customer Management." This information is stored in a database by the server using data recording mechanisms and used in subsequent processes.

[0075] The server automatically generates interview questions using natural language processing based on stored theme information. The questions are customized to elicit the user's experiences, and might take the form of, for example, "What were some of the most effective customer interactions you've had in the past?"

[0076] The user answers these questions on their device and sends the answers to the server via a recognition device with speech recognition technology. The server converts the received speech data into text and generates book content using a text generation model. In this process, an algorithm known as a generative AI model is used. An example of a prompt message might be, "Please create interview questions to generate a story based on your sales experience."

[0077] The server then uses an image generator to design insert images and covers for books. This facilitates the creation of books with visual content.

[0078] The generated text and images are presented to the user via their device. After reviewing the content, the user provides additional feedback, and the content is adjusted and revised through the editing cycle. Finally, publishing support tools are used to publish the work in either print or ebook format.

[0079] This system allows users to quickly create and effectively publish high-quality books, even without specialized publishing knowledge.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] Users access the system using a terminal and enter their profile information and book theme. This information includes the user's name, occupation, hobbies, and the theme they want to write about (e.g., sales knowledge). The server receives this information and stores it in a database using an information recording device. The stored data is then used in subsequent processing steps to generate personalized content.

[0083] Step 2:

[0084] The server automatically generates interview questions using natural language processing based on theme information stored in the database. It uses the set theme as input and feeds it into a question generation AI model to output a specific list of questions. The prompt message is in the form of "Please create questions based on this theme." The output question list is displayed on the user's terminal.

[0085] Step 3:

[0086] The user answers questions generated through the terminal. Specifically, they input their answers via voice or text into the terminal. Voice responses are converted into text data via a recognition device with speech recognition technology. The server receives this text data and temporarily stores it in a database. In this step, the input is the user's response, and the output is the corresponding text data.

[0087] Step 4:

[0088] The server uses a text generation model to analyze temporarily stored text data and construct sentences. Using user responses as input, an AI algorithm generates a logical and consistent story required for a book. The output is nearly complete text content. For example, a specific story such as "A collection of success stories based on user experiences" can be generated.

[0089] Step 5:

[0090] The server uses an image generation device to automatically generate images and cover designs to be inserted into books. It creates related images based on the theme and generated text. This process involves prompting a specific visual generation AI model and outputting visual data. For example, it can generate illustrations of sales scenes or business backgrounds. This visual data is provided to the user for review.

[0091] Step 6:

[0092] Users provide feedback and suggest revisions to the text and images they view on their devices. They submit feedback and revision suggestions as input, and the server re-edits the content using an editing cycle. This results in final content tailored to the user's preferences and requests. The output is the revised text and images.

[0093] Step 7:

[0094] The server supports the publication of completed content in the user's preferred format (print book or e-book) via publishing support tools. The input is final-verified content, and the server outputs necessary procedures for the publishing process and notifications to the user. This allows users to quickly bring their books to market.

[0095] (Application Example 1)

[0096] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0097] Traditional content generation and distribution systems have difficulty adequately reflecting the individual circumstances and interests of users, limiting their ability to provide personalized content. Furthermore, there is a growing need for content revisions that reflect user feedback in real time, and for rapid display tailored to the user's situation.

[0098] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0099] In this invention, the server includes data storage means for inputting and saving user information, natural language processing means for generating user interactions and extracting information, and document generation means for generating and editing text based on the extracted information. This makes it possible to generate and deliver user-optimized content in real time and display it efficiently.

[0100] "Data storage means" refers to a device or function for receiving, appropriately storing, and managing user information.

[0101] "Natural language processing means" refers to technologies or processes for generating dialogue with users and extracting useful information from that dialogue.

[0102] A "document generation means" is a function that generates and edits text in a specified format and style based on extracted information.

[0103] "Image generation means" refers to a device or function for automatically generating visual elements contained in content.

[0104] "Publishing support tools" are support functions that prepare generated content for publication and output it in an appropriate format.

[0105] "Display control means" refers to a device or function for distributing content to a terminal and managing its display according to the user's situation.

[0106] The system for carrying out this invention uses a data device and a display device as hardware. This includes data storage means for inputting and storing user information, through which user interest and attribute information is received and stored.

[0107] The server generates dialogue with the user using natural language processing tools and extracts useful information from that dialogue. This clarifies the user's intentions and needs. Libraries such as "TENSORFLOW®" can be used for natural language processing.

[0108] Based on the extracted information, the server generates text using document generation tools. Document generation uses a generation AI model to automatically organize the text according to paragraphs and structure. This technology generates high-quality text, which is then delivered to the terminal.

[0109] Furthermore, the server automatically generates visual elements using image generation methods. This creates visual content that corresponds to the text. The server can also convert speech to text using "Google® Cloud Speech-to-Text," etc.

[0110] Ultimately, the server uses publishing support and display control means to deliver the generated content to the user's device. This allows the user to receive customized information in real time.

[0111] For example, if a user is wearing smart glasses, they can receive motivational messages via audio and visuals while jogging. An example of a prompt might be, "Generate an encouraging message based on the user's heart rate and speed data during jogging."

[0112] In this way, a system that integrates each of these methods makes it possible to generate and deliver individually optimized content to users.

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The server receives user profile information and topics of interest as input from the terminal. This input is stored and managed using data storage means. The stored information is used as a basis for subsequent processes.

[0116] Step 2:

[0117] The server uses natural language processing to generate dialogue from stored user information. It also extracts new information through interaction with the user. This process generates specific questions tailored to the user's interests. The input is the user's past information, and the output is the newly extracted information.

[0118] Step 3:

[0119] The server generates text using document generation tools based on the extracted information. Text is automatically generated by inputting prompt text into the generation AI model. The input is information extracted from the user, and the output is a document following a specific structure.

[0120] Step 4:

[0121] The server uses image generation capabilities to create visual content based on the generated text. Specifically, it generates images and designs appropriate to the user's theme and text content. The input is the generated text, and the output is various images.

[0122] Step 5:

[0123] The server uses publishing support and display control means to deliver generated content to the user's terminal. This process is optimized according to the user's situation, and the content is displayed in real time. Input consists of generated text and images, while output is the display on the user's terminal.

[0124] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0125] This invention makes user interaction more dynamic and personalized by incorporating an emotion engine into a system that supports the process of users creating books based on their own experiences and knowledge. The system configuration and program processing are described below.

[0126] First, the user accesses the system using a terminal and enters their profile information and the theme they want to turn into a book. This information is then sent to the server and stored in a database. Next, the server uses natural language processing technology to generate a list of interview questions based on the theme set by the user, and prepares to efficiently collect information through the interview.

[0127] The emotion engine analyzes the user's emotional state in real time while they are inputting voice or text, identifying emotions such as joy, sadness, and excitement. This information is used by the server to adjust the interview questions and progress accordingly. For example, if the user is excited, more detailed questions can be added to encourage their creative engagement. If the user shows anxiety or confusion, the difficulty of the questions can be lowered, or the interview can be paused to provide contextual explanations, optimizing the user's experience.

[0128] The data collected during interviews is converted into text according to the book's structure by the server's text generation system. During this process, the tone and style of the text are adjusted to reflect emotional data obtained from the emotion engine. Content tailored to the user's desired style is automatically created, making it possible to provide engaging content for readers.

[0129] In addition, the server utilizes image generation capabilities to create insert images and cover designs as visual elements for the book. As a result, the generated book becomes visually interesting as well as containing textual information.

[0130] Users review book drafts via their devices and provide feedback. This feedback process, too, is personalized based on user responses obtained through an emotion engine, resulting in customized revisions suggested by the server. The editing cycle can be repeated as needed to create a high-quality work that aligns with the user's intentions.

[0131] By incorporating an emotion engine in this way, the user experience can be improved, the entire book creation process can be streamlined, and the quality of the final content can be optimized. For example, when a writer creating a narrative feels a heightened emotion regarding the theme, the system could automatically suggest a thrilling development in the story, helping to create a more profound work.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] Users access the system via their terminal and enter their profile information and themes they wish to turn into books. This information is sent to the server and stored in a database.

[0135] Step 2:

[0136] The server automatically generates a list of interview questions using natural language processing technology based on themes set by the user. The questions used are designed to effectively draw out the user's knowledge and experience.

[0137] Step 3:

[0138] The server activates an emotion engine, analyzing the user's voice and text input in real time and preparing to recognize their emotional state.

[0139] Step 4:

[0140] Throughout the interview, the device presents the user with questions. The user answers these questions using voice or text. The emotion engine analyzes the user's emotions from their tone of voice and expressions during the response process and provides the data to the server in real time.

[0141] Step 5:

[0142] The server analyzes the received responses and sentiment data, and adjusts the interview questions and pace as needed. For example, if a user shows signs of anxiety, the server instructs the terminal to lower the difficulty of the questions or provide supplementary information.

[0143] Step 6:

[0144] Based on the analyzed information, the server automatically generates the text for the book using a text generation system. During this process, it adjusts the tone and style of the text, taking sentiment data into consideration, to more accurately reflect the user's intended meaning.

[0145] Step 7:

[0146] The server uses image generation tools to create images and cover designs to be inserted into the book. The generated visual content is then sent to the terminal.

[0147] Step 8:

[0148] Users review the content of the books generated through their devices and submit feedback. This feedback includes not only textual revision suggestions but also the user's emotional response.

[0149] Step 9:

[0150] The server uses user feedback and sentiment data to revise the book's content and design. If necessary, it repeats the editing cycle, seeking user confirmation again.

[0151] Step 10:

[0152] The server utilizes publishing support tools to publish books in the format chosen by the user. It automatically completes the necessary publishing procedures, such as obtaining an ISBN and setting up distribution channels, and then asks the user for final confirmation.

[0153] (Example 2)

[0154] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0155] In today's information society, there is a demand for users to easily generate content that reflects their own experiences and emotions. However, conventional systems have difficulty adequately considering users' emotions, making it challenging to generate personalized content based on individual user experiences. Furthermore, there were challenges in flexibly adjusting the progress of interviews and quickly revising content based on feedback.

[0156] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0157] In this invention, the server includes a storage medium means for inputting and storing user information, a language analysis means for generating dialogue with the user and extracting information, and an emotion analysis means for analyzing the user's voice and text to identify their emotional state. This enables the generation of personalized content that takes the user's emotions into consideration. Furthermore, by flexibly adjusting the interview content based on the emotional state, efficient and user-responsive dialogue is achieved, and rapid corrections are made based on feedback.

[0158] "Storage medium means" refers to a data storage device for saving information entered by the user, and is a device that stores and manages data.

[0159] "Language analysis means" refers to analysis methods and algorithms that use natural language processing technology to generate dialogue with users and extract information.

[0160] "Emotional analysis methods" refer to technical techniques for identifying a user's emotional state from their voice or text and obtaining emotional data.

[0161] "Document generation means" refers to text generation technology that generates and edits text based on collected information and sentiment data to create final content.

[0162] "Visual generation methods" refer to methods that include graphics generation technologies for automatically generating images and designs, thereby creating visually appealing content.

[0163] "Publishing support means" refers to the processes and technologies for outputting and providing generated content in a publishable format.

[0164] This invention is a system that personalizes the user experience through content generation that takes into account the user's emotional state. Specific embodiments of this system are described below.

[0165] Users access the system through a terminal and input their profile information and themes they wish to turn into books. This input information is stored on a storage medium by the server. The server uses language analysis tools that utilize natural language processing technology to generate dialogue based on the information provided by the user. Specifically, it utilizes natural language processing libraries (e.g., spaCy and NLTK).

[0166] The user's emotional state is identified by analyzing voice and text in real time using emotion analysis tools. The emotion analysis algorithm uses machine learning models to obtain emotional data from the user's tone and word choices.

[0167] Through text generation methods, extracted information and sentiment data are integrated to generate articles and book chapters. This step is performed using a generation AI model (e.g., GPT-3®), and the tone and style of the text are adjusted according to the user's preferred style.

[0168] Visual elements are created using visual generation methods. Image generation algorithms (e.g., DALL-E) are used to automatically generate insert images and cover art to be included in books. This ensures that the content is not only textual but also visually appealing.

[0169] Finally, users review the completed content through their devices and provide feedback. This feedback is incorporated by the publishing support system, and revisions are made as needed. The server processes this feedback based on sentiment analysis and adjusts the content to meet user expectations.

[0170] Specific example: For instance, when creating a story, the system can help add depth to the narrative by suggesting thrilling plot developments at scenes where specific emotions input by the user are heightened.

[0171] Example prompt: "How can you make the story more thrilling at the emotional points the user has indicated?"

[0172] In this way, this system enables the creation of emotionally engaging and creative content for users through a series of means combining hardware and software.

[0173] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0174] Step 1:

[0175] Users access the system through a terminal and input profile information and themes they wish to turn into books. This input information is sent to a server and stored on a storage medium. The input here is text data about the user's themes and preferences, and the stored data is used in the next step.

[0176] Step 2:

[0177] The server generates an interview question list using language analysis tools based on stored theme information. Specifically, it utilizes a natural language processing library to extract keywords related to the input theme and construct questions to elicit information. The output is the generated question list.

[0178] Step 3:

[0179] The user answers interview questions according to a generated list. The user's answers are entered into the terminal in voice or text format. This information is sent to a server, where the emotional state is analyzed in real time by an emotion analysis system. The input is the user's voice and text data, and the output is the analyzed emotion data.

[0180] Step 4:

[0181] The server dynamically adjusts the interview process based on emotional data obtained from emotion analysis tools and interview responses. For example, if the user is agitated, it uses a generative AI model to delve deeper into the questions. The input is the emotional state and responses, and the output is the adjusted interview process.

[0182] Step 5:

[0183] The server uses text generation tools to integrate data obtained from interviews with sentiment data to generate book chapters. This process utilizes a generative AI model to automatically generate text tailored to the user's style. The input consists of interview responses and sentiment data, while the output is the resulting text.

[0184] Step 6:

[0185] The server automatically generates images and designs to be inserted into the book using a visual generation system. In this step, images are generated according to the book's theme and emotional data. The input is emotional data and theme information, and the output is the generated visual content.

[0186] Step 7:

[0187] Users review the content generated through their devices and provide feedback. This feedback is sent to the server, and the content is modified based on the feedback. The input is the feedback information, and the output is the modified content.

[0188] (Application Example 2)

[0189] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0190] The present invention aims to provide a system that offers personalized product recommendations and purchasing support tailored to the user's emotional state at the time of purchasing activities on e-commerce sites and the like. This aims to improve the user's purchasing experience and increase customer satisfaction.

[0191] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0192] In this invention, the server includes storage means for inputting and storing user information, emotion analysis means for analyzing the user's emotional state through image input, and recommendation means for providing product information based on the analyzed emotional information. This enables dynamic and personalized purchasing support that responds to the user's emotional state.

[0193] "User information" refers to the personal data and profile of users that are entered into the system.

[0194] A "storage device" is a system component that has the function of storing and managing data.

[0195] "Language processing means" refers to technologies that have the function of interpreting natural language and generating dialogue with users.

[0196] "Character data generation means" refers to technology that automatically generates and edits text based on extracted information.

[0197] A "visual data generation means" is a system component that has the function of generating images and designs.

[0198] "Emotional analysis methods" refer to technologies that analyze a user's emotional state from data such as facial expressions and voice.

[0199] A "recommendation tool" is a system element that has the function of presenting product information suitable for the user based on analyzed emotional information.

[0200] "Publishing support tools" are technologies that have the function of preparing generated content into a format that can be published.

[0201] This invention utilizes several pieces of hardware and software to realize a system that provides users with a personalized purchasing experience. The system centers around a process that analyzes the user's emotional state in real time and makes product recommendations based on that analysis.

[0202] The server uses storage devices to save user information. To analyze the user's emotional state, it acquires image data from a camera and utilizes image processing and machine learning platforms such as OpenCV and TensorFlow to analyze this data. Once the user's emotions are analyzed, it communicates with the server using Flask and Django Rest Framework to recommend product information based on that analysis. This process enables recommendations optimized for the user's purchasing behavior.

[0203] As a concrete example, when a user is browsing products using a smart device, the system can detect the user's smile and recommend products such as, "Why not check out our popular new autumn / winter collection?" In this way, it is possible to provide an interactive experience tailored to the user's emotions.

[0204] An example of a prompt message could be, "Identify emotions such as joy or surprise from the user's facial expression image, and recommend new product information that matches those emotions." This allows for the continuous provision of services that meet the user's needs.

[0205] This system significantly improves the user's purchasing experience and supports their purchasing decisions.

[0206] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0207] Step 1:

[0208] The user accesses the system using a terminal and enters user information. The terminal records the entered information and sends it to the server. The input includes the user's personal data and profile information, and storage means are used to store this in a database. As a result, the user's basic information is stored in the database.

[0209] Step 2:

[0210] The device uses its camera function to acquire real-time image data of the user's facial expressions. The server receives this image data and performs image processing using OpenCV. The acquired image data is then analyzed using TensorFlow to identify the user's emotional state. The output provides information identifying the analyzed emotion.

[0211] Step 3:

[0212] The server generates prompt sentences for product recommendations based on the analyzed sentiment data. These prompt sentences are then input into a generative AI model to generate optimal product information. The input is sentiment data, and the output is recommended product information. The generated product information is personalized according to the user's emotions.

[0213] Step 4:

[0214] The terminal displays recommended product information received from the server to the user. This display is done on the user's interface, highlighting products that the user is likely to be interested in. As a result, product information that increases the user's purchasing intent is displayed on the terminal.

[0215] Step 5:

[0216] The user provides feedback on the presented product. The device records the feedback and sends it back to the server. The server receives the feedback information and begins the process of revising the generated content. The content is optimized based on the feedback, improving the accuracy of recommendations in the future.

[0217] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0218] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search)<url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0219] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0223] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0224] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0225] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0226] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0227] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0228] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0229] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0230] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0231] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0232] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0233] This invention provides a system that facilitates the creation of books based on the experiences and knowledge of specific users. The system is configured as follows:

[0234] First, the user accesses the system and enters their profile information and areas of interest. The server then saves this user information to a database and prepares to generate personalized content based on this data.

[0235] Next, the server generates a list of interview questions using natural language processing technology based on the theme set by the user. These questions are customized to specifically elicit the user's experiences and knowledge.

[0236] Users answer generated questions via their devices. The answers are sent to a server in real time and converted into text using speech recognition technology. The server then analyzes the text data using natural language processing to extract the information necessary for the book.

[0237] Based on the extracted information, the server uses a text generation system to create a document. The document is structured from an introduction to a conclusion, reflecting the user's intent and structural requirements.

[0238] Subsequently, the server uses image generation tools to create insert images and cover designs for use in the book. This makes it easier for users to visualize the overall structure of the book, including its visual content.

[0239] The generated text and images are presented to the user via their device, allowing for modifications and feedback. If necessary, the server re-edits the content to reflect the user's feedback.

[0240] Ultimately, the server utilizes publishing support tools to facilitate publication in the user's chosen format (print or ebook). This process includes automated procedures and the selection of optimized distribution channels.

[0241] For example, if a business person in their 40s wants to create a book about their sales experience, this system can effectively generate text and design, allowing them to prepare the book for publication in a short period of time. In this way, by implementing this invention, anyone can easily create and widely share a book.

[0242] The following describes the processing flow.

[0243] Step 1:

[0244] Users access the system using their devices and create accounts. They enter profile information and topics of interest and send it to the server. The server stores the received information in a database and prepares it for use in subsequent processes.

[0245] Step 2:

[0246] The server uses natural language processing technology to generate a list of interview questions based on the theme selected by the user. This list of questions is customized to elicit the user's experience and knowledge and is used in the next interview step.

[0247] Step 3:

[0248] The terminal initiates an interview session based on a pre-set schedule and presents the user with questions generated by the server. The user answers these questions verbally or in text.

[0249] Step 4:

[0250] The server processes user responses in real time and converts them into text using speech recognition technology. The converted data is then analyzed using natural language processing to extract essential information necessary for book creation.

[0251] Step 5:

[0252] Based on the extracted key information, the server uses text generation tools to create text that follows the structure of a book. The text includes each chapter, from the introduction to the conclusion, and fully reflects the content of the user interviews.

[0253] Step 6:

[0254] The server uses image generation capabilities to create the necessary insert images and cover designs for the book. Designs are created according to user settings and themes, enriching the visual content.

[0255] Step 7:

[0256] The system prompts the user to review the generated text and images through their device. The user reviews the content and provides feedback if there are any corrections or additional requests.

[0257] Step 8:

[0258] The server makes necessary edits and adjustments based on user feedback. This editing cycle may be repeated until the content is reviewed again and the final book draft is finalized.

[0259] Step 9:

[0260] The server uses publishing support tools to publish books in the format specified by the user (print book or e-book). It automates publishing procedures such as obtaining an ISBN and setting up sales channels, and then asks the user for final confirmation.

[0261] (Example 1)

[0262] Next, we will describe Example 1. 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."

[0263] For users, effectively compiling their experiences and knowledge to create a book has traditionally required significant time and specialized skills, making it a high hurdle for the average person. To address this challenge, a newly developed system is needed to enable anyone to easily create a book and streamline the entire publishing process.

[0264] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0265] In this invention, the server includes information recording means for inputting and storing user information, natural language processing means for automatically generating interview questions based on themes set by the user, and recognition means for converting the user's answers into text data via speech recognition. This makes it possible for users to easily and quickly create books that reflect their own experiences and knowledge without requiring high technical knowledge.

[0266] "Information recording means" refers to a device or configuration that has the function of temporarily or permanently storing profile information and themes entered by users.

[0267] "Natural language processing means" refers to a technology or module used to generate interview questions based on a theme set by the user and to analyze the user's responses.

[0268] "Recognition means" refers to a system or component that uses speech recognition technology to convert a user's voice response into text data.

[0269] A "text generation model" is an algorithm or model that utilizes artificial intelligence to automatically generate coherent texts and structures based on extracted knowledge data.

[0270] An "image generation device" is a device or software for generating visual content, such as images to be inserted into a book or cover designs, for automatic creation.

[0271] An "editing cycle mechanism" is a function or process that receives feedback from users and modifies and re-edits the generated content based on that feedback.

[0272] "Publishing support tools" refer to systems or tools that output completed content in the most suitable format as a paper book or e-book, and provide support for publishing procedures and distribution.

[0273] This invention is a system that makes it easier for users to create books based on their own experience and knowledge. The system consists of a server, terminals, and various artificial intelligence technologies.

[0274] Users access the system through their terminal and, as part of the initial setup, enter their profile information and topics of interest. For example, they might set the topic to "Best Practices for Customer Management." This information is stored in a database by the server using data recording mechanisms and used in subsequent processes.

[0275] The server automatically generates interview questions using natural language processing based on stored theme information. The questions are customized to elicit the user's experiences, and might take the form of, for example, "What were some of the most effective customer interactions you've had in the past?"

[0276] The user answers these questions on their device and sends the answers to the server via a recognition device with speech recognition technology. The server converts the received speech data into text and generates book content using a text generation model. In this process, an algorithm known as a generative AI model is used. An example of a prompt message might be, "Please create interview questions to generate a story based on your sales experience."

[0277] The server then uses an image generator to design insert images and covers for books. This facilitates the creation of books with visual content.

[0278] The generated text and images are presented to the user via their device. After reviewing the content, the user provides additional feedback, and the content is adjusted and revised through the editing cycle. Finally, publishing support tools are used to publish the work in either print or ebook format.

[0279] With this system, even users without specialized publishing knowledge can quickly create high-quality books and effectively publish them.

[0280] The flow of the specific process in Example 1 will be described using FIG. 11.

[0281] Step 1:

[0282] The user uses the terminal to access the system and input their profile information and the theme of the book. The input information includes the user's name, occupation, hobbies, etc., as well as the theme they want for the book, such as "knowledge of sales". The server receives this and stores it in the database by means of information recording. The stored data is used for generating individualized content in later processing steps.

[0283] Step 2:

[0284] Based on the theme information stored in the database, the server automatically generates interview questions using natural language processing means. Using the set theme as input information, it inputs it into the question generation AI model to output a specific list of questions. This prompt text uses the form "Please create questions based on this theme". The output list of questions is presented to the user's terminal.

[0285] Step 3:

[0286] The user answers the questions generated through the terminal. Specifically, voice input or text input is performed on the terminal. The voice answer is converted into text data through recognition means with voice recognition technology. The server receives this text data and temporarily stores it in the database. The input for this step is the user's answer, and the output is the corresponding text data.

[0287] Step 4:

[0288] The server uses a text generation model to analyze temporarily stored text data and construct sentences. Using user responses as input, an AI algorithm generates a logical and consistent story required for a book. The output is nearly complete text content. For example, a specific story such as "A collection of success stories based on user experiences" can be generated.

[0289] Step 5:

[0290] The server uses an image generation device to automatically generate images and cover designs to be inserted into books. It creates related images based on the theme and generated text. This process involves prompting a specific visual generation AI model and outputting visual data. For example, it can generate illustrations of sales scenes or business backgrounds. This visual data is provided to the user for review.

[0291] Step 6:

[0292] Users provide feedback and suggest revisions to the text and images they view on their devices. They submit feedback and revision suggestions as input, and the server re-edits the content using an editing cycle. This results in final content tailored to the user's preferences and requests. The output is the revised text and images.

[0293] Step 7:

[0294] The server supports the publication of completed content in the user's preferred format (print book or e-book) via publishing support tools. The input is final-verified content, and the server outputs necessary procedures for the publishing process and notifications to the user. This allows users to quickly bring their books to market.

[0295] (Application Example 1)

[0296] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0297] Traditional content generation and distribution systems have difficulty adequately reflecting the individual circumstances and interests of users, limiting their ability to provide personalized content. Furthermore, there is a growing need for content revisions that reflect user feedback in real time, and for rapid display tailored to the user's situation.

[0298] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0299] In this invention, the server includes data storage means for inputting and saving user information, natural language processing means for generating user interactions and extracting information, and document generation means for generating and editing text based on the extracted information. This makes it possible to generate and deliver user-optimized content in real time and display it efficiently.

[0300] "Data storage means" refers to a device or function for receiving, appropriately storing, and managing user information.

[0301] "Natural language processing means" refers to technologies or processes for generating dialogue with users and extracting useful information from that dialogue.

[0302] A "document generation means" is a function that generates and edits text in a specified format and style based on extracted information.

[0303] "Image generation means" refers to a device or function for automatically generating visual elements contained in content.

[0304] "Publishing support tools" are support functions that prepare generated content for publication and output it in an appropriate format.

[0305] The "display control means" is a device or function for delivering content to a terminal according to the user's situation and managing the display.

[0306] The system for implementing this invention uses a data device and a display device as hardware. This includes data storage means for inputting and storing user information, and by this means, the user's interests and attribute information are received and stored.

[0307] The server uses natural language processing means to generate a dialogue with the user and extracts useful information from the dialogue. Thereby, the user's intentions and needs are clarified. For natural language processing, libraries such as "TensorFlow" can be used.

[0308] Based on the extracted information, the server uses document generation means to generate a text. For document generation, a generation AI model is used to automatically compile the text according to paragraphs and structures. By this technology, high-quality texts are generated and delivered to the terminal.

[0309] Furthermore, the server uses image generation means to automatically generate visual elements. Thereby, visual content corresponding to the text is created. The server can also convert voice to text using "Google Cloud Speech-to-Text" or the like.

[0310] Finally, the server uses publishing support means and display control means to deliver the generated content to the user's device. Thereby, the user can receive customized information in real time.

[0311] As a specific example, when the user is wearing smart glasses, it is possible to receive messages that boost motivation during jogging both audibly and visually. As an example of a prompt sentence, an instruction such as "Please generate an encouraging message based on the user's heart rate and speed data during jogging" can be considered.

[0312] In this way, a system that integrates each of these methods makes it possible to generate and deliver individually optimized content to users.

[0313] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0314] Step 1:

[0315] The server receives user profile information and topics of interest as input from the terminal. This input is stored and managed using data storage means. The stored information is used as a basis for subsequent processes.

[0316] Step 2:

[0317] The server uses natural language processing to generate dialogue from stored user information. It also extracts new information through interaction with the user. This process generates specific questions tailored to the user's interests. The input is the user's past information, and the output is the newly extracted information.

[0318] Step 3:

[0319] The server generates text using document generation tools based on the extracted information. Text is automatically generated by inputting prompt text into the generation AI model. The input is information extracted from the user, and the output is a document following a specific structure.

[0320] Step 4:

[0321] The server uses image generation capabilities to create visual content based on the generated text. Specifically, it generates images and designs appropriate to the user's theme and text content. The input is the generated text, and the output is various images.

[0322] Step 5:

[0323] The server uses publishing support and display control means to deliver generated content to the user's terminal. This process is optimized according to the user's situation, and the content is displayed in real time. Input consists of generated text and images, while output is the display on the user's terminal.

[0324] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0325] This invention makes user interaction more dynamic and personalized by incorporating an emotion engine into a system that supports the process of users creating books based on their own experiences and knowledge. The system configuration and program processing are described below.

[0326] First, the user accesses the system using a terminal and enters their profile information and the theme they want to turn into a book. This information is then sent to the server and stored in a database. Next, the server uses natural language processing technology to generate a list of interview questions based on the theme set by the user, and prepares to efficiently collect information through the interview.

[0327] The emotion engine analyzes the user's emotional state in real time while they are inputting voice or text, identifying emotions such as joy, sadness, and excitement. This information is used by the server to adjust the interview questions and progress accordingly. For example, if the user is excited, more detailed questions can be added to encourage their creative engagement. If the user shows anxiety or confusion, the difficulty of the questions can be lowered, or the interview can be paused to provide contextual explanations, optimizing the user's experience.

[0328] The data collected during interviews is converted into text according to the book's structure by the server's text generation system. During this process, the tone and style of the text are adjusted to reflect emotional data obtained from the emotion engine. Content tailored to the user's desired style is automatically created, making it possible to provide engaging content for readers.

[0329] In addition, the server utilizes image generation capabilities to create insert images and cover designs as visual elements for the book. As a result, the generated book becomes visually interesting as well as containing textual information.

[0330] Users review book drafts via their devices and provide feedback. This feedback process, too, is personalized based on user responses obtained through an emotion engine, resulting in customized revisions suggested by the server. The editing cycle can be repeated as needed to create a high-quality work that aligns with the user's intentions.

[0331] By incorporating an emotion engine in this way, the user experience can be improved, the entire book creation process can be streamlined, and the quality of the final content can be optimized. For example, when a writer creating a narrative feels a heightened emotion regarding the theme, the system could automatically suggest a thrilling development in the story, helping to create a more profound work.

[0332] The following describes the processing flow.

[0333] Step 1:

[0334] Users access the system via their terminal and enter their profile information and themes they wish to turn into books. This information is sent to the server and stored in a database.

[0335] Step 2:

[0336] The server automatically generates a list of interview questions using natural language processing technology based on themes set by the user. The questions used are designed to effectively draw out the user's knowledge and experience.

[0337] Step 3:

[0338] The server activates an emotion engine, analyzing the user's voice and text input in real time and preparing to recognize their emotional state.

[0339] Step 4:

[0340] Throughout the interview, the device presents the user with questions. The user answers these questions using voice or text. The emotion engine analyzes the user's emotions from their tone of voice and expressions during the response process and provides the data to the server in real time.

[0341] Step 5:

[0342] The server analyzes the received responses and sentiment data, and adjusts the interview questions and pace as needed. For example, if a user shows signs of anxiety, the server instructs the terminal to lower the difficulty of the questions or provide supplementary information.

[0343] Step 6:

[0344] Based on the analyzed information, the server automatically generates the text for the book using a text generation system. During this process, it adjusts the tone and style of the text, taking sentiment data into consideration, to more accurately reflect the user's intended meaning.

[0345] Step 7:

[0346] The server uses image generation tools to create images and cover designs to be inserted into the book. The generated visual content is then sent to the terminal.

[0347] Step 8:

[0348] Users review the content of the books generated through their devices and submit feedback. This feedback includes not only textual revision suggestions but also the user's emotional response.

[0349] Step 9:

[0350] The server uses user feedback and sentiment data to revise the book's content and design. If necessary, it repeats the editing cycle, seeking user confirmation again.

[0351] Step 10:

[0352] The server utilizes publishing support tools to publish books in the format chosen by the user. It automatically completes the necessary publishing procedures, such as obtaining an ISBN and setting up distribution channels, and then asks the user for final confirmation.

[0353] (Example 2)

[0354] Next, we will describe Example 2. 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".

[0355] In today's information society, there is a demand for users to easily generate content that reflects their own experiences and emotions. However, conventional systems have difficulty adequately considering users' emotions, making it challenging to generate personalized content based on individual user experiences. Furthermore, there were challenges in flexibly adjusting the progress of interviews and quickly revising content based on feedback.

[0356] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0357] In this invention, the server includes a storage medium means for inputting and storing user information, a language analysis means for generating dialogue with the user and extracting information, and an emotion analysis means for analyzing the user's voice and text to identify their emotional state. This enables the generation of personalized content that takes the user's emotions into consideration. Furthermore, by flexibly adjusting the interview content based on the emotional state, efficient and user-responsive dialogue is achieved, and rapid corrections are made based on feedback.

[0358] "Storage medium means" refers to a data storage device for saving information entered by the user, and is a device that stores and manages data.

[0359] "Language analysis means" refers to analysis methods and algorithms that use natural language processing technology to generate dialogue with users and extract information.

[0360] "Emotional analysis methods" refer to technical techniques for identifying a user's emotional state from their voice or text and obtaining emotional data.

[0361] "Document generation means" refers to text generation technology that generates and edits text based on collected information and sentiment data to create final content.

[0362] "Visual generation methods" refer to methods that include graphics generation technologies for automatically generating images and designs, thereby creating visually appealing content.

[0363] "Publishing support means" refers to the processes and technologies for outputting and providing generated content in a publishable format.

[0364] This invention is a system that personalizes the user experience through content generation that takes into account the user's emotional state. Specific embodiments of this system are described below.

[0365] Users access the system through a terminal and input their profile information and themes they wish to turn into books. This input information is stored on a storage medium by the server. The server uses language analysis tools that utilize natural language processing technology to generate dialogue based on the information provided by the user. Specifically, it utilizes natural language processing libraries (e.g., spaCy and NLTK).

[0366] The user's emotional state is identified by analyzing voice and text in real time using emotion analysis tools. The emotion analysis algorithm uses machine learning models to obtain emotional data from the user's tone and word choices.

[0367] Through text generation methods, extracted information and sentiment data are integrated to generate articles or book chapters. This step is performed using a generation AI model (e.g., GPT-3), and the tone and style of the text are adjusted according to the user's preferred style.

[0368] Visual elements are created using visual generation methods. Image generation algorithms (e.g., DALL-E) are used to automatically generate insert images and cover art to be included in books. This ensures that the content is not only textual but also visually appealing.

[0369] Finally, users review the completed content through their devices and provide feedback. This feedback is incorporated by the publishing support system, and revisions are made as needed. The server processes this feedback based on sentiment analysis and adjusts the content to meet user expectations.

[0370] Specific example: For instance, when creating a story, the system can help add depth to the narrative by suggesting thrilling plot developments at scenes where specific emotions input by the user are heightened.

[0371] Example prompt: "How can you make the story more thrilling at the emotional points the user has indicated?"

[0372] In this way, this system enables the creation of emotionally engaging and creative content for users through a series of means combining hardware and software.

[0373] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0374] Step 1:

[0375] Users access the system through a terminal and input profile information and themes they wish to turn into books. This input information is sent to a server and stored on a storage medium. The input here is text data about the user's themes and preferences, and the stored data is used in the next step.

[0376] Step 2:

[0377] The server generates an interview question list using language analysis tools based on stored theme information. Specifically, it utilizes a natural language processing library to extract keywords related to the input theme and construct questions to elicit information. The output is the generated question list.

[0378] Step 3:

[0379] The user answers interview questions according to a generated list. The user's answers are entered into the terminal in voice or text format. This information is sent to a server, where the emotional state is analyzed in real time by an emotion analysis system. The input is the user's voice and text data, and the output is the analyzed emotion data.

[0380] Step 4:

[0381] The server dynamically adjusts the interview process based on emotional data obtained from emotion analysis tools and interview responses. For example, if the user is agitated, it uses a generative AI model to delve deeper into the questions. The input is the emotional state and responses, and the output is the adjusted interview process.

[0382] Step 5:

[0383] The server uses text generation tools to integrate data obtained from interviews with sentiment data to generate book chapters. This process utilizes a generative AI model to automatically generate text tailored to the user's style. The input consists of interview responses and sentiment data, while the output is the resulting text.

[0384] Step 6:

[0385] The server automatically generates images and designs to be inserted into the book using a visual generation system. In this step, images are generated according to the book's theme and emotional data. The input is emotional data and theme information, and the output is the generated visual content.

[0386] Step 7:

[0387] Users review the content generated through their devices and provide feedback. This feedback is sent to the server, and the content is modified based on the feedback. The input is the feedback information, and the output is the modified content.

[0388] (Application Example 2)

[0389] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0390] The present invention aims to provide a system that offers personalized product recommendations and purchasing support tailored to the user's emotional state at the time of purchasing activities on e-commerce sites and the like. This aims to improve the user's purchasing experience and increase customer satisfaction.

[0391] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0392] In this invention, the server includes storage means for inputting and storing user information, emotion analysis means for analyzing the user's emotional state through image input, and recommendation means for providing product information based on the analyzed emotional information. This enables dynamic and personalized purchasing support that responds to the user's emotional state.

[0393] "User information" refers to the personal data and profile of users that are entered into the system.

[0394] A "storage device" is a system component that has the function of storing and managing data.

[0395] "Language processing means" refers to technologies that have the function of interpreting natural language and generating dialogue with users.

[0396] "Character data generation means" refers to technology that automatically generates and edits text based on extracted information.

[0397] A "visual data generation means" is a system component that has the function of generating images and designs.

[0398] "Emotional analysis methods" refer to technologies that analyze a user's emotional state from data such as facial expressions and voice.

[0399] A "recommendation tool" is a system element that has the function of presenting product information suitable for the user based on analyzed emotional information.

[0400] "Publishing support tools" are technologies that have the function of preparing generated content into a format that can be published.

[0401] This invention utilizes several pieces of hardware and software to realize a system that provides users with a personalized purchasing experience. The system centers around a process that analyzes the user's emotional state in real time and makes product recommendations based on that analysis.

[0402] The server uses storage devices to save user information. To analyze the user's emotional state, it acquires image data from a camera and utilizes image processing and machine learning platforms such as OpenCV and TensorFlow to analyze this data. Once the user's emotions are analyzed, it communicates with the server using Flask and Django Rest Framework to recommend product information based on that analysis. This process enables recommendations optimized for the user's purchasing behavior.

[0403] As a concrete example, when a user is browsing products using a smart device, the system can detect the user's smile and recommend products such as, "Why not check out our popular new autumn / winter collection?" In this way, it is possible to provide an interactive experience tailored to the user's emotions.

[0404] An example of a prompt message could be, "Identify emotions such as joy or surprise from the user's facial expression image, and recommend new product information that matches those emotions." This allows for the continuous provision of services that meet the user's needs.

[0405] This system significantly improves the user's purchasing experience and supports their purchasing decisions.

[0406] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0407] Step 1:

[0408] The user accesses the system using a terminal and enters user information. The terminal records the entered information and sends it to the server. The input includes the user's personal data and profile information, and storage means are used to store this in a database. As a result, the user's basic information is stored in the database.

[0409] Step 2:

[0410] The device uses its camera function to acquire real-time image data of the user's facial expressions. The server receives this image data and performs image processing using OpenCV. The acquired image data is then analyzed using TensorFlow to identify the user's emotional state. The output provides information identifying the analyzed emotion.

[0411] Step 3:

[0412] The server generates prompt sentences for product recommendations based on the analyzed sentiment data. These prompt sentences are then input into a generative AI model to generate optimal product information. The input is sentiment data, and the output is recommended product information. The generated product information is personalized according to the user's emotions.

[0413] Step 4:

[0414] The terminal displays recommended product information received from the server to the user. This display is done on the user's interface, highlighting products that the user is likely to be interested in. As a result, product information that increases the user's purchasing intent is displayed on the terminal.

[0415] Step 5:

[0416] The user provides feedback on the presented product. The device records the feedback and sends it back to the server. The server receives the feedback information and begins the process of revising the generated content. The content is optimized based on the feedback, improving the accuracy of recommendations in the future.

[0417] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0418] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0419] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0420] [Third Embodiment]

[0421] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0422] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0423] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0424] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0425] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0426] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0427] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0428] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0429] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0430] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0431] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0432] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0433] This invention provides a system that facilitates the creation of books based on the experiences and knowledge of specific users. The system is configured as follows:

[0434] First, the user accesses the system and enters their profile information and areas of interest. The server then saves this user information to a database and prepares to generate personalized content based on this data.

[0435] Next, the server generates a list of interview questions using natural language processing technology based on the theme set by the user. These questions are customized to specifically elicit the user's experiences and knowledge.

[0436] Users answer generated questions via their devices. The answers are sent to a server in real time and converted into text using speech recognition technology. The server then analyzes the text data using natural language processing to extract the information necessary for the book.

[0437] Based on the extracted information, the server uses a text generation system to create a document. The document is structured from an introduction to a conclusion, reflecting the user's intent and structural requirements.

[0438] Subsequently, the server uses image generation tools to create insert images and cover designs for use in the book. This makes it easier for users to visualize the overall structure of the book, including its visual content.

[0439] The generated text and images are presented to the user via their device, allowing for modifications and feedback. If necessary, the server re-edits the content to reflect the user's feedback.

[0440] Ultimately, the server utilizes publishing support tools to facilitate publication in the user's chosen format (print or ebook). This process includes automated procedures and the selection of optimized distribution channels.

[0441] For example, if a business person in their 40s wants to create a book about their sales experience, this system can effectively generate text and design, allowing them to prepare the book for publication in a short period of time. In this way, by implementing this invention, anyone can easily create and widely share a book.

[0442] The following describes the processing flow.

[0443] Step 1:

[0444] Users access the system using their devices and create accounts. They enter profile information and topics of interest and send it to the server. The server stores the received information in a database and prepares it for use in subsequent processes.

[0445] Step 2:

[0446] The server uses natural language processing technology to generate a list of interview questions based on the theme selected by the user. This list of questions is customized to elicit the user's experience and knowledge and is used in the next interview step.

[0447] Step 3:

[0448] The terminal initiates an interview session based on a pre-set schedule and presents the user with questions generated by the server. The user answers these questions verbally or in text.

[0449] Step 4:

[0450] The server processes user responses in real time and converts them into text using speech recognition technology. The converted data is then analyzed using natural language processing to extract essential information necessary for book creation.

[0451] Step 5:

[0452] Based on the extracted key information, the server uses text generation tools to create text that follows the structure of a book. The text includes each chapter, from the introduction to the conclusion, and fully reflects the content of the user interviews.

[0453] Step 6:

[0454] The server uses image generation capabilities to create the necessary insert images and cover designs for the book. Designs are created according to user settings and themes, enriching the visual content.

[0455] Step 7:

[0456] The system prompts the user to review the generated text and images through their device. The user reviews the content and provides feedback if there are any corrections or additional requests.

[0457] Step 8:

[0458] The server makes necessary edits and adjustments based on user feedback. This editing cycle may be repeated until the content is reviewed again and the final book draft is finalized.

[0459] Step 9:

[0460] The server uses publishing support tools to publish books in the format specified by the user (print book or e-book). It automates publishing procedures such as obtaining an ISBN and setting up sales channels, and then asks the user for final confirmation.

[0461] (Example 1)

[0462] Next, we will describe Example 1. 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."

[0463] For users, effectively compiling their experiences and knowledge to create a book has traditionally required significant time and specialized skills, making it a high hurdle for the average person. To address this challenge, a newly developed system is needed to enable anyone to easily create a book and streamline the entire publishing process.

[0464] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0465] In this invention, the server includes information recording means for inputting and storing user information, natural language processing means for automatically generating interview questions based on themes set by the user, and recognition means for converting the user's answers into text data via speech recognition. This makes it possible for users to easily and quickly create books that reflect their own experiences and knowledge without requiring high technical knowledge.

[0466] "Information recording means" refers to a device or configuration that has the function of temporarily or permanently storing profile information and themes entered by users.

[0467] "Natural language processing means" refers to a technology or module used to generate interview questions based on a theme set by the user and to analyze the user's responses.

[0468] "Recognition means" refers to a system or component that uses speech recognition technology to convert a user's voice response into text data.

[0469] A "text generation model" is an algorithm or model that utilizes artificial intelligence to automatically generate coherent texts and structures based on extracted knowledge data.

[0470] An "image generation device" is a device or software for generating visual content, such as images to be inserted into a book or cover designs, for automatic creation.

[0471] An "editing cycle mechanism" is a function or process that receives feedback from users and modifies and re-edits the generated content based on that feedback.

[0472] "Publishing support tools" refer to systems or tools that output completed content in the most suitable format as a paper book or e-book, and provide support for publishing procedures and distribution.

[0473] This invention is a system that makes it easier for users to create books based on their own experience and knowledge. The system consists of a server, terminals, and various artificial intelligence technologies.

[0474] Users access the system through their terminal and, as part of the initial setup, enter their profile information and topics of interest. For example, they might set the topic to "Best Practices for Customer Management." This information is stored in a database by the server using data recording mechanisms and used in subsequent processes.

[0475] The server automatically generates interview questions using natural language processing based on stored theme information. The questions are customized to elicit the user's experiences, and might take the form of, for example, "What were some of the most effective customer interactions you've had in the past?"

[0476] The user answers these questions on their device and sends the answers to the server via a recognition device with speech recognition technology. The server converts the received speech data into text and generates book content using a text generation model. In this process, an algorithm known as a generative AI model is used. An example of a prompt message might be, "Please create interview questions to generate a story based on your sales experience."

[0477] The server then uses an image generator to design insert images and covers for books. This facilitates the creation of books with visual content.

[0478] The generated text and images are presented to the user via their device. After reviewing the content, the user provides additional feedback, and the content is adjusted and revised through the editing cycle. Finally, publishing support tools are used to publish the work in either print or ebook format.

[0479] This system allows users to quickly create and effectively publish high-quality books, even without specialized publishing knowledge.

[0480] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0481] Step 1:

[0482] Users access the system using a terminal and enter their profile information and book theme. This information includes the user's name, occupation, hobbies, and the theme they want to write about (e.g., sales knowledge). The server receives this information and stores it in a database using an information recording device. The stored data is then used in subsequent processing steps to generate personalized content.

[0483] Step 2:

[0484] The server automatically generates interview questions using natural language processing based on theme information stored in the database. It uses the set theme as input and feeds it into a question generation AI model to output a specific list of questions. The prompt message is in the form of "Please create questions based on this theme." The output question list is displayed on the user's terminal.

[0485] Step 3:

[0486] The user answers questions generated through the terminal. Specifically, they input their answers via voice or text into the terminal. Voice responses are converted into text data via a recognition device with speech recognition technology. The server receives this text data and temporarily stores it in a database. In this step, the input is the user's response, and the output is the corresponding text data.

[0487] Step 4:

[0488] The server uses a text generation model to analyze temporarily stored text data and construct sentences. Using user responses as input, an AI algorithm generates a logical and consistent story required for a book. The output is nearly complete text content. For example, a specific story such as "A collection of success stories based on user experiences" can be generated.

[0489] Step 5:

[0490] The server uses an image generation device to automatically generate images and cover designs to be inserted into books. It creates related images based on the theme and generated text. This process involves prompting a specific visual generation AI model and outputting visual data. For example, it can generate illustrations of sales scenes or business backgrounds. This visual data is provided to the user for review.

[0491] Step 6:

[0492] Users provide feedback and suggest revisions to the text and images they view on their devices. They submit feedback and revision suggestions as input, and the server re-edits the content using an editing cycle. This results in final content tailored to the user's preferences and requests. The output is the revised text and images.

[0493] Step 7:

[0494] The server supports the publication of completed content in the user's preferred format (print book or e-book) via publishing support tools. The input is final-verified content, and the server outputs necessary procedures for the publishing process and notifications to the user. This allows users to quickly bring their books to market.

[0495] (Application Example 1)

[0496] Next, we will explain Application Example 1. In the following explanation, 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."

[0497] Traditional content generation and distribution systems have difficulty adequately reflecting the individual circumstances and interests of users, limiting their ability to provide personalized content. Furthermore, there is a growing need for content revisions that reflect user feedback in real time, and for rapid display tailored to the user's situation.

[0498] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0499] In this invention, the server includes data storage means for inputting and saving user information, natural language processing means for generating user interactions and extracting information, and document generation means for generating and editing text based on the extracted information. This makes it possible to generate and deliver user-optimized content in real time and display it efficiently.

[0500] "Data storage means" refers to a device or function for receiving, appropriately storing, and managing user information.

[0501] "Natural language processing means" refers to technologies or processes for generating dialogue with users and extracting useful information from that dialogue.

[0502] A "document generation means" is a function that generates and edits text in a specified format and style based on extracted information.

[0503] "Image generation means" refers to a device or function for automatically generating visual elements contained in content.

[0504] "Publishing support tools" are support functions that prepare generated content for publication and output it in an appropriate format.

[0505] "Display control means" refers to a device or function for distributing content to a terminal and managing its display according to the user's situation.

[0506] The system for carrying out this invention uses a data device and a display device as hardware. This includes data storage means for inputting and storing user information, through which user interest and attribute information is received and stored.

[0507] The server uses natural language processing to generate dialogue with the user and extracts useful information from that dialogue. This clarifies the user's intentions and needs. Libraries such as "TensorFlow" can be used for natural language processing.

[0508] Based on the extracted information, the server generates text using document generation tools. Document generation uses a generation AI model to automatically organize the text according to paragraphs and structure. This technology generates high-quality text, which is then delivered to the terminal.

[0509] Furthermore, the server automatically generates visual elements using image generation methods. This creates visual content that corresponds to the text. The server can also convert speech to text using tools such as "Google Cloud Speech-to-Text".

[0510] Ultimately, the server uses publishing support and display control means to deliver the generated content to the user's device. This allows the user to receive customized information in real time.

[0511] For example, if a user is wearing smart glasses, they can receive motivational messages via audio and visuals while jogging. An example of a prompt might be, "Generate an encouraging message based on the user's heart rate and speed data during jogging."

[0512] In this way, a system that integrates each of these methods makes it possible to generate and deliver individually optimized content to users.

[0513] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0514] Step 1:

[0515] The server receives user profile information and topics of interest as input from the terminal. This input is stored and managed using data storage means. The stored information is used as a basis for subsequent processes.

[0516] Step 2:

[0517] The server uses natural language processing to generate dialogue from stored user information. It also extracts new information through interaction with the user. This process generates specific questions tailored to the user's interests. The input is the user's past information, and the output is the newly extracted information.

[0518] Step 3:

[0519] The server generates text using document generation tools based on the extracted information. Text is automatically generated by inputting prompt text into the generation AI model. The input is information extracted from the user, and the output is a document following a specific structure.

[0520] Step 4:

[0521] The server uses image generation capabilities to create visual content based on the generated text. Specifically, it generates images and designs appropriate to the user's theme and text content. The input is the generated text, and the output is various images.

[0522] Step 5:

[0523] The server uses publishing support and display control means to deliver generated content to the user's terminal. This process is optimized according to the user's situation, and the content is displayed in real time. Input consists of generated text and images, while output is the display on the user's terminal.

[0524] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0525] This invention makes user interaction more dynamic and personalized by incorporating an emotion engine into a system that supports the process of users creating books based on their own experiences and knowledge. The system configuration and program processing are described below.

[0526] First, the user accesses the system using a terminal and enters their profile information and the theme they want to turn into a book. This information is then sent to the server and stored in a database. Next, the server uses natural language processing technology to generate a list of interview questions based on the theme set by the user, and prepares to efficiently collect information through the interview.

[0527] The emotion engine analyzes the user's emotional state in real time while they are inputting voice or text, identifying emotions such as joy, sadness, and excitement. This information is used by the server to adjust the interview questions and progress accordingly. For example, if the user is excited, more detailed questions can be added to encourage their creative engagement. If the user shows anxiety or confusion, the difficulty of the questions can be lowered, or the interview can be paused to provide contextual explanations, optimizing the user's experience.

[0528] The data collected during interviews is converted into text according to the book's structure by the server's text generation system. During this process, the tone and style of the text are adjusted to reflect emotional data obtained from the emotion engine. Content tailored to the user's desired style is automatically created, making it possible to provide engaging content for readers.

[0529] In addition, the server utilizes image generation capabilities to create insert images and cover designs as visual elements for the book. As a result, the generated book becomes visually interesting as well as containing textual information.

[0530] Users review book drafts via their devices and provide feedback. This feedback process, too, is personalized based on user responses obtained through an emotion engine, resulting in customized revisions suggested by the server. The editing cycle can be repeated as needed to create a high-quality work that aligns with the user's intentions.

[0531] By incorporating an emotion engine in this way, the user experience can be improved, the entire book creation process can be streamlined, and the quality of the final content can be optimized. For example, when a writer creating a narrative feels a heightened emotion regarding the theme, the system could automatically suggest a thrilling development in the story, helping to create a more profound work.

[0532] The following describes the processing flow.

[0533] Step 1:

[0534] Users access the system via their terminal and enter their profile information and themes they wish to turn into books. This information is sent to the server and stored in a database.

[0535] Step 2:

[0536] The server automatically generates a list of interview questions using natural language processing technology based on themes set by the user. The questions used are designed to effectively draw out the user's knowledge and experience.

[0537] Step 3:

[0538] The server activates an emotion engine, analyzing the user's voice and text input in real time and preparing to recognize their emotional state.

[0539] Step 4:

[0540] Throughout the interview, the device presents the user with questions. The user answers these questions using voice or text. The emotion engine analyzes the user's emotions from their tone of voice and expressions during the response process and provides the data to the server in real time.

[0541] Step 5:

[0542] The server analyzes the received responses and sentiment data, and adjusts the interview questions and pace as needed. For example, if a user shows signs of anxiety, the server instructs the terminal to lower the difficulty of the questions or provide supplementary information.

[0543] Step 6:

[0544] Based on the analyzed information, the server automatically generates the text for the book using a text generation system. During this process, it adjusts the tone and style of the text, taking sentiment data into consideration, to more accurately reflect the user's intended meaning.

[0545] Step 7:

[0546] The server uses image generation tools to create images and cover designs to be inserted into the book. The generated visual content is then sent to the terminal.

[0547] Step 8:

[0548] Users review the content of the books generated through their devices and submit feedback. This feedback includes not only textual revision suggestions but also the user's emotional response.

[0549] Step 9:

[0550] The server uses user feedback and sentiment data to revise the book's content and design. If necessary, it repeats the editing cycle, seeking user confirmation again.

[0551] Step 10:

[0552] The server utilizes publishing support tools to publish books in the format chosen by the user. It automatically completes the necessary publishing procedures, such as obtaining an ISBN and setting up distribution channels, and then asks the user for final confirmation.

[0553] (Example 2)

[0554] Next, we will describe Example 2. 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."

[0555] In today's information society, there is a demand for users to easily generate content that reflects their own experiences and emotions. However, conventional systems have difficulty adequately considering users' emotions, making it challenging to generate personalized content based on individual user experiences. Furthermore, there were challenges in flexibly adjusting the progress of interviews and quickly revising content based on feedback.

[0556] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0557] In this invention, the server includes a storage medium means for inputting and storing user information, a language analysis means for generating dialogue with the user and extracting information, and an emotion analysis means for analyzing the user's voice and text to identify their emotional state. This enables the generation of personalized content that takes the user's emotions into consideration. Furthermore, by flexibly adjusting the interview content based on the emotional state, efficient and user-responsive dialogue is achieved, and rapid corrections are made based on feedback.

[0558] "Storage medium means" refers to a data storage device for saving information entered by the user, and is a device that stores and manages data.

[0559] "Language analysis means" refers to analysis methods and algorithms that use natural language processing technology to generate dialogue with users and extract information.

[0560] "Emotional analysis methods" refer to technical techniques for identifying a user's emotional state from their voice or text and obtaining emotional data.

[0561] "Document generation means" refers to text generation technology that generates and edits text based on collected information and sentiment data to create final content.

[0562] "Visual generation methods" refer to methods that include graphics generation technologies for automatically generating images and designs, thereby creating visually appealing content.

[0563] "Publishing support means" refers to the processes and technologies for outputting and providing generated content in a publishable format.

[0564] This invention is a system that personalizes the user experience through content generation that takes into account the user's emotional state. Specific embodiments of this system are described below.

[0565] Users access the system through a terminal and input their profile information and themes they wish to turn into books. This input information is stored on a storage medium by the server. The server uses language analysis tools that utilize natural language processing technology to generate dialogue based on the information provided by the user. Specifically, it utilizes natural language processing libraries (e.g., spaCy and NLTK).

[0566] The user's emotional state is identified by analyzing voice and text in real time using emotion analysis tools. The emotion analysis algorithm uses machine learning models to obtain emotional data from the user's tone and word choices.

[0567] Through text generation methods, extracted information and sentiment data are integrated to generate articles or book chapters. This step is performed using a generation AI model (e.g., GPT-3), and the tone and style of the text are adjusted according to the user's preferred style.

[0568] Visual elements are created using visual generation methods. Image generation algorithms (e.g., DALL-E) are used to automatically generate insert images and cover art to be included in books. This ensures that the content is not only textual but also visually appealing.

[0569] Finally, users review the completed content through their devices and provide feedback. This feedback is incorporated by the publishing support system, and revisions are made as needed. The server processes this feedback based on sentiment analysis and adjusts the content to meet user expectations.

[0570] Specific example: For instance, when creating a story, the system can help add depth to the narrative by suggesting thrilling plot developments at scenes where specific emotions input by the user are heightened.

[0571] Example prompt: "How can you make the story more thrilling at the emotional points the user has indicated?"

[0572] In this way, this system enables the creation of emotionally engaging and creative content for users through a series of means combining hardware and software.

[0573] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0574] Step 1:

[0575] Users access the system through a terminal and input profile information and themes they wish to turn into books. This input information is sent to a server and stored on a storage medium. The input here is text data about the user's themes and preferences, and the stored data is used in the next step.

[0576] Step 2:

[0577] The server generates an interview question list using language analysis tools based on stored theme information. Specifically, it utilizes a natural language processing library to extract keywords related to the input theme and construct questions to elicit information. The output is the generated question list.

[0578] Step 3:

[0579] The user answers interview questions according to a generated list. The user's answers are entered into the terminal in voice or text format. This information is sent to a server, where the emotional state is analyzed in real time by an emotion analysis system. The input is the user's voice and text data, and the output is the analyzed emotion data.

[0580] Step 4:

[0581] The server dynamically adjusts the interview process based on emotional data obtained from emotion analysis tools and interview responses. For example, if the user is agitated, it uses a generative AI model to delve deeper into the questions. The input is the emotional state and responses, and the output is the adjusted interview process.

[0582] Step 5:

[0583] The server uses text generation tools to integrate data obtained from interviews with sentiment data to generate book chapters. This process utilizes a generative AI model to automatically generate text tailored to the user's style. The input consists of interview responses and sentiment data, while the output is the resulting text.

[0584] Step 6:

[0585] The server automatically generates images and designs to be inserted into the book using a visual generation system. In this step, images are generated according to the book's theme and emotional data. The input is emotional data and theme information, and the output is the generated visual content.

[0586] Step 7:

[0587] Users review the content generated through their devices and provide feedback. This feedback is sent to the server, and the content is modified based on the feedback. The input is the feedback information, and the output is the modified content.

[0588] (Application Example 2)

[0589] Next, we will explain application example 2. In the following explanation, 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."

[0590] The present invention aims to provide a system that offers personalized product recommendations and purchasing support tailored to the user's emotional state at the time of purchasing activities on e-commerce sites and the like. This aims to improve the user's purchasing experience and increase customer satisfaction.

[0591] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0592] In this invention, the server includes storage means for inputting and storing user information, emotion analysis means for analyzing the user's emotional state through image input, and recommendation means for providing product information based on the analyzed emotional information. This enables dynamic and personalized purchasing support that responds to the user's emotional state.

[0593] "User information" refers to the personal data and profile of users that are entered into the system.

[0594] A "storage device" is a system component that has the function of storing and managing data.

[0595] "Language processing means" refers to technologies that have the function of interpreting natural language and generating dialogue with users.

[0596] "Character data generation means" refers to technology that automatically generates and edits text based on extracted information.

[0597] A "visual data generation means" is a system component that has the function of generating images and designs.

[0598] "Emotional analysis methods" refer to technologies that analyze a user's emotional state from data such as facial expressions and voice.

[0599] A "recommendation tool" is a system element that has the function of presenting product information suitable for the user based on analyzed emotional information.

[0600] "Publishing support tools" are technologies that have the function of preparing generated content into a format that can be published.

[0601] This invention utilizes several pieces of hardware and software to realize a system that provides users with a personalized purchasing experience. The system centers around a process that analyzes the user's emotional state in real time and makes product recommendations based on that analysis.

[0602] The server uses storage devices to save user information. To analyze the user's emotional state, it acquires image data from a camera and utilizes image processing and machine learning platforms such as OpenCV and TensorFlow to analyze this data. Once the user's emotions are analyzed, it communicates with the server using Flask and Django Rest Framework to recommend product information based on that analysis. This process enables recommendations optimized for the user's purchasing behavior.

[0603] As a concrete example, when a user is browsing products using a smart device, the system can detect the user's smile and recommend products such as, "Why not check out our popular new autumn / winter collection?" In this way, it is possible to provide an interactive experience tailored to the user's emotions.

[0604] An example of a prompt message could be, "Identify emotions such as joy or surprise from the user's facial expression image, and recommend new product information that matches those emotions." This allows for the continuous provision of services that meet the user's needs.

[0605] This system significantly improves the user's purchasing experience and supports their purchasing decisions.

[0606] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0607] Step 1:

[0608] The user accesses the system using a terminal and enters user information. The terminal records the entered information and sends it to the server. The input includes the user's personal data and profile information, and storage means are used to store this in a database. As a result, the user's basic information is stored in the database.

[0609] Step 2:

[0610] The device uses its camera function to acquire real-time image data of the user's facial expressions. The server receives this image data and performs image processing using OpenCV. The acquired image data is then analyzed using TensorFlow to identify the user's emotional state. The output provides information identifying the analyzed emotion.

[0611] Step 3:

[0612] The server generates prompt sentences for product recommendations based on the analyzed sentiment data. These prompt sentences are then input into a generative AI model to generate optimal product information. The input is sentiment data, and the output is recommended product information. The generated product information is personalized according to the user's emotions.

[0613] Step 4:

[0614] The terminal displays recommended product information received from the server to the user. This display is done on the user's interface, highlighting products that the user is likely to be interested in. As a result, product information that increases the user's purchasing intent is displayed on the terminal.

[0615] Step 5:

[0616] The user provides feedback on the presented product. The device records the feedback and sends it back to the server. The server receives the feedback information and begins the process of revising the generated content. The content is optimized based on the feedback, improving the accuracy of recommendations in the future.

[0617] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0618] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0619] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0620] [Fourth Embodiment]

[0621] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0622] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0623] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0624] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0625] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0626] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0627] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0628] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0629] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0630] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0631] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0632] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0633] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0634] This invention provides a system that facilitates the creation of books based on the experiences and knowledge of specific users. The system is configured as follows:

[0635] First, the user accesses the system and enters their profile information and areas of interest. The server then saves this user information to a database and prepares to generate personalized content based on this data.

[0636] Next, the server generates a list of interview questions using natural language processing technology based on the theme set by the user. These questions are customized to specifically elicit the user's experiences and knowledge.

[0637] Users answer generated questions via their devices. The answers are sent to a server in real time and converted into text using speech recognition technology. The server then analyzes the text data using natural language processing to extract the information necessary for the book.

[0638] Based on the extracted information, the server uses a text generation system to create a document. The document is structured from an introduction to a conclusion, reflecting the user's intent and structural requirements.

[0639] Subsequently, the server uses image generation tools to create insert images and cover designs for use in the book. This makes it easier for users to visualize the overall structure of the book, including its visual content.

[0640] The generated text and images are presented to the user via their device, allowing for modifications and feedback. If necessary, the server re-edits the content to reflect the user's feedback.

[0641] Ultimately, the server utilizes publishing support tools to facilitate publication in the user's chosen format (print or ebook). This process includes automated procedures and the selection of optimized distribution channels.

[0642] For example, if a business person in their 40s wants to create a book about their sales experience, this system can effectively generate text and design, allowing them to prepare the book for publication in a short period of time. In this way, by implementing this invention, anyone can easily create and widely share a book.

[0643] The following describes the processing flow.

[0644] Step 1:

[0645] Users access the system using their devices and create accounts. They enter profile information and topics of interest and send it to the server. The server stores the received information in a database and prepares it for use in subsequent processes.

[0646] Step 2:

[0647] The server uses natural language processing technology to generate a list of interview questions based on the theme selected by the user. This list of questions is customized to elicit the user's experience and knowledge and is used in the next interview step.

[0648] Step 3:

[0649] The terminal initiates an interview session based on a pre-set schedule and presents the user with questions generated by the server. The user answers these questions verbally or in text.

[0650] Step 4:

[0651] The server processes user responses in real time and converts them into text using speech recognition technology. The converted data is then analyzed using natural language processing to extract essential information necessary for book creation.

[0652] Step 5:

[0653] Based on the extracted key information, the server uses text generation tools to create text that follows the structure of a book. The text includes each chapter, from the introduction to the conclusion, and fully reflects the content of the user interviews.

[0654] Step 6:

[0655] The server uses image generation capabilities to create the necessary insert images and cover designs for the book. Designs are created according to user settings and themes, enriching the visual content.

[0656] Step 7:

[0657] The system prompts the user to review the generated text and images through their device. The user reviews the content and provides feedback if there are any corrections or additional requests.

[0658] Step 8:

[0659] The server makes necessary edits and adjustments based on user feedback. This editing cycle may be repeated until the content is reviewed again and the final book draft is finalized.

[0660] Step 9:

[0661] The server uses publishing support tools to publish books in the format specified by the user (print book or e-book). It automates publishing procedures such as obtaining an ISBN and setting up sales channels, and then asks the user for final confirmation.

[0662] (Example 1)

[0663] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0664] For users, effectively compiling their experiences and knowledge to create a book has traditionally required significant time and specialized skills, making it a high hurdle for the average person. To address this challenge, a newly developed system is needed to enable anyone to easily create a book and streamline the entire publishing process.

[0665] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0666] In this invention, the server includes information recording means for inputting and storing user information, natural language processing means for automatically generating interview questions based on themes set by the user, and recognition means for converting the user's answers into text data via speech recognition. This makes it possible for users to easily and quickly create books that reflect their own experiences and knowledge without requiring high technical knowledge.

[0667] "Information recording means" refers to a device or configuration that has the function of temporarily or permanently storing profile information and themes entered by users.

[0668] "Natural language processing means" refers to a technology or module used to generate interview questions based on a theme set by the user and to analyze the user's responses.

[0669] "Recognition means" refers to a system or component that uses speech recognition technology to convert a user's voice response into text data.

[0670] A "text generation model" is an algorithm or model that utilizes artificial intelligence to automatically generate coherent texts and structures based on extracted knowledge data.

[0671] An "image generation device" is a device or software for generating visual content, such as images to be inserted into a book or cover designs, for automatic creation.

[0672] An "editing cycle mechanism" is a function or process that receives feedback from users and modifies and re-edits the generated content based on that feedback.

[0673] "Publishing support tools" refer to systems or tools that output completed content in the most suitable format as a paper book or e-book, and provide support for publishing procedures and distribution.

[0674] This invention is a system that makes it easier for users to create books based on their own experience and knowledge. The system consists of a server, terminals, and various artificial intelligence technologies.

[0675] Users access the system through their terminal and, as part of the initial setup, enter their profile information and topics of interest. For example, they might set the topic to "Best Practices for Customer Management." This information is stored in a database by the server using data recording mechanisms and used in subsequent processes.

[0676] The server automatically generates interview questions using natural language processing based on stored theme information. The questions are customized to elicit the user's experiences, and might take the form of, for example, "What were some of the most effective customer interactions you've had in the past?"

[0677] The user answers these questions on their device and sends the answers to the server via a recognition device with speech recognition technology. The server converts the received speech data into text and generates book content using a text generation model. In this process, an algorithm known as a generative AI model is used. An example of a prompt message might be, "Please create interview questions to generate a story based on your sales experience."

[0678] The server then uses an image generator to design insert images and covers for books. This facilitates the creation of books with visual content.

[0679] The generated text and images are presented to the user via their device. After reviewing the content, the user provides additional feedback, and the content is adjusted and revised through the editing cycle. Finally, publishing support tools are used to publish the work in either print or ebook format.

[0680] This system allows users to quickly create and effectively publish high-quality books, even without specialized publishing knowledge.

[0681] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0682] Step 1:

[0683] Users access the system using a terminal and enter their profile information and book theme. This information includes the user's name, occupation, hobbies, and the theme they want to write about (e.g., sales knowledge). The server receives this information and stores it in a database using an information recording device. The stored data is then used in subsequent processing steps to generate personalized content.

[0684] Step 2:

[0685] The server automatically generates interview questions using natural language processing based on theme information stored in the database. It uses the set theme as input and feeds it into a question generation AI model to output a specific list of questions. The prompt message is in the form of "Please create questions based on this theme." The output question list is displayed on the user's terminal.

[0686] Step 3:

[0687] The user answers questions generated through the terminal. Specifically, they input their answers via voice or text into the terminal. Voice responses are converted into text data via a recognition device with speech recognition technology. The server receives this text data and temporarily stores it in a database. In this step, the input is the user's response, and the output is the corresponding text data.

[0688] Step 4:

[0689] The server uses a text generation model to analyze temporarily stored text data and construct sentences. Using user responses as input, an AI algorithm generates a logical and consistent story required for a book. The output is nearly complete text content. For example, a specific story such as "A collection of success stories based on user experiences" can be generated.

[0690] Step 5:

[0691] The server uses an image generation device to automatically generate images and cover designs to be inserted into books. It creates related images based on the theme and generated text. This process involves prompting a specific visual generation AI model and outputting visual data. For example, it can generate illustrations of sales scenes or business backgrounds. This visual data is provided to the user for review.

[0692] Step 6:

[0693] Users provide feedback and suggest revisions to the text and images they view on their devices. They submit feedback and revision suggestions as input, and the server re-edits the content using an editing cycle. This results in final content tailored to the user's preferences and requests. The output is the revised text and images.

[0694] Step 7:

[0695] The server supports the publication of completed content in the user's preferred format (print book or e-book) via publishing support tools. The input is final-verified content, and the server outputs necessary procedures for the publishing process and notifications to the user. This allows users to quickly bring their books to market.

[0696] (Application Example 1)

[0697] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0698] Traditional content generation and distribution systems have difficulty adequately reflecting the individual circumstances and interests of users, limiting their ability to provide personalized content. Furthermore, there is a growing need for content revisions that reflect user feedback in real time, and for rapid display tailored to the user's situation.

[0699] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0700] In this invention, the server includes data storage means for inputting and saving user information, natural language processing means for generating user interactions and extracting information, and document generation means for generating and editing text based on the extracted information. This makes it possible to generate and deliver user-optimized content in real time and display it efficiently.

[0701] "Data storage means" refers to a device or function for receiving, appropriately storing, and managing user information.

[0702] "Natural language processing means" refers to technologies or processes for generating dialogue with users and extracting useful information from that dialogue.

[0703] A "document generation means" is a function that generates and edits text in a specified format and style based on extracted information.

[0704] "Image generation means" refers to a device or function for automatically generating visual elements contained in content.

[0705] "Publishing support tools" are support functions that prepare generated content for publication and output it in an appropriate format.

[0706] "Display control means" refers to a device or function for distributing content to a terminal and managing its display according to the user's situation.

[0707] The system for carrying out this invention uses a data device and a display device as hardware. This includes data storage means for inputting and storing user information, through which user interest and attribute information is received and stored.

[0708] The server uses natural language processing to generate dialogue with the user and extracts useful information from that dialogue. This clarifies the user's intentions and needs. Libraries such as "TensorFlow" can be used for natural language processing.

[0709] Based on the extracted information, the server generates text using document generation tools. Document generation uses a generation AI model to automatically organize the text according to paragraphs and structure. This technology generates high-quality text, which is then delivered to the terminal.

[0710] Furthermore, the server automatically generates visual elements using image generation methods. This creates visual content that corresponds to the text. The server can also convert speech to text using tools such as "Google Cloud Speech-to-Text".

[0711] Ultimately, the server uses publishing support and display control means to deliver the generated content to the user's device. This allows the user to receive customized information in real time.

[0712] For example, if a user is wearing smart glasses, they can receive motivational messages via audio and visuals while jogging. An example of a prompt might be, "Generate an encouraging message based on the user's heart rate and speed data during jogging."

[0713] In this way, a system that integrates each of these methods makes it possible to generate and deliver individually optimized content to users.

[0714] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0715] Step 1:

[0716] The server receives user profile information and topics of interest as input from the terminal. This input is stored and managed using data storage means. The stored information is used as a basis for subsequent processes.

[0717] Step 2:

[0718] The server uses natural language processing to generate dialogue from stored user information. It also extracts new information through interaction with the user. This process generates specific questions tailored to the user's interests. The input is the user's past information, and the output is the newly extracted information.

[0719] Step 3:

[0720] The server generates text using document generation tools based on the extracted information. Text is automatically generated by inputting prompt text into the generation AI model. The input is information extracted from the user, and the output is a document following a specific structure.

[0721] Step 4:

[0722] The server uses image generation capabilities to create visual content based on the generated text. Specifically, it generates images and designs appropriate to the user's theme and text content. The input is the generated text, and the output is various images.

[0723] Step 5:

[0724] The server uses publishing support and display control means to deliver generated content to the user's terminal. This process is optimized according to the user's situation, and the content is displayed in real time. Input consists of generated text and images, while output is the display on the user's terminal.

[0725] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0726] This invention makes user interaction more dynamic and personalized by incorporating an emotion engine into a system that supports the process of users creating books based on their own experiences and knowledge. The system configuration and program processing are described below.

[0727] First, the user accesses the system using a terminal and enters their profile information and the theme they want to turn into a book. This information is then sent to the server and stored in a database. Next, the server uses natural language processing technology to generate a list of interview questions based on the theme set by the user, and prepares to efficiently collect information through the interview.

[0728] The emotion engine analyzes the user's emotional state in real time while they are inputting voice or text, identifying emotions such as joy, sadness, and excitement. This information is used by the server to adjust the interview questions and progress accordingly. For example, if the user is excited, more detailed questions can be added to encourage their creative engagement. If the user shows anxiety or confusion, the difficulty of the questions can be lowered, or the interview can be paused to provide contextual explanations, optimizing the user's experience.

[0729] The data collected during interviews is converted into text according to the book's structure by the server's text generation system. During this process, the tone and style of the text are adjusted to reflect emotional data obtained from the emotion engine. Content tailored to the user's desired style is automatically created, making it possible to provide engaging content for readers.

[0730] In addition, the server utilizes image generation capabilities to create insert images and cover designs as visual elements for the book. As a result, the generated book becomes visually interesting as well as containing textual information.

[0731] Users review book drafts via their devices and provide feedback. This feedback process, too, is personalized based on user responses obtained through an emotion engine, resulting in customized revisions suggested by the server. The editing cycle can be repeated as needed to create a high-quality work that aligns with the user's intentions.

[0732] By incorporating an emotion engine in this way, the user experience can be improved, the entire book creation process can be streamlined, and the quality of the final content can be optimized. For example, when a writer creating a narrative feels a heightened emotion regarding the theme, the system could automatically suggest a thrilling development in the story, helping to create a more profound work.

[0733] The following describes the processing flow.

[0734] Step 1:

[0735] Users access the system via their terminal and enter their profile information and themes they wish to turn into books. This information is sent to the server and stored in a database.

[0736] Step 2:

[0737] The server automatically generates a list of interview questions using natural language processing technology based on themes set by the user. The questions used are designed to effectively draw out the user's knowledge and experience.

[0738] Step 3:

[0739] The server activates an emotion engine, analyzing the user's voice and text input in real time and preparing to recognize their emotional state.

[0740] Step 4:

[0741] Throughout the interview, the device presents the user with questions. The user answers these questions using voice or text. The emotion engine analyzes the user's emotions from their tone of voice and expressions during the response process and provides the data to the server in real time.

[0742] Step 5:

[0743] The server analyzes the received responses and sentiment data, and adjusts the interview questions and pace as needed. For example, if a user shows signs of anxiety, the server instructs the terminal to lower the difficulty of the questions or provide supplementary information.

[0744] Step 6:

[0745] Based on the analyzed information, the server automatically generates the text for the book using a text generation system. During this process, it adjusts the tone and style of the text, taking sentiment data into consideration, to more accurately reflect the user's intended meaning.

[0746] Step 7:

[0747] The server uses image generation tools to create images and cover designs to be inserted into the book. The generated visual content is then sent to the terminal.

[0748] Step 8:

[0749] Users review the content of the books generated through their devices and submit feedback. This feedback includes not only textual revision suggestions but also the user's emotional response.

[0750] Step 9:

[0751] The server uses user feedback and sentiment data to revise the book's content and design. If necessary, it repeats the editing cycle, seeking user confirmation again.

[0752] Step 10:

[0753] The server utilizes publishing support tools to publish books in the format chosen by the user. It automatically completes the necessary publishing procedures, such as obtaining an ISBN and setting up distribution channels, and then asks the user for final confirmation.

[0754] (Example 2)

[0755] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0756] In today's information society, there is a demand for users to easily generate content that reflects their own experiences and emotions. However, conventional systems have difficulty adequately considering users' emotions, making it challenging to generate personalized content based on individual user experiences. Furthermore, there were challenges in flexibly adjusting the progress of interviews and quickly revising content based on feedback.

[0757] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0758] In this invention, the server includes a storage medium means for inputting and storing user information, a language analysis means for generating dialogue with the user and extracting information, and an emotion analysis means for analyzing the user's voice and text to identify their emotional state. This enables the generation of personalized content that takes the user's emotions into consideration. Furthermore, by flexibly adjusting the interview content based on the emotional state, efficient and user-responsive dialogue is achieved, and rapid corrections are made based on feedback.

[0759] "Storage medium means" refers to a data storage device for saving information entered by the user, and is a device that stores and manages data.

[0760] "Language analysis means" refers to analysis methods and algorithms that use natural language processing technology to generate dialogue with users and extract information.

[0761] "Emotional analysis methods" refer to technical techniques for identifying a user's emotional state from their voice or text and obtaining emotional data.

[0762] "Document generation means" refers to text generation technology that generates and edits text based on collected information and sentiment data to create final content.

[0763] "Visual generation methods" refer to methods that include graphics generation technologies for automatically generating images and designs, thereby creating visually appealing content.

[0764] "Publishing support means" refers to the processes and technologies for outputting and providing generated content in a publishable format.

[0765] This invention is a system that personalizes the user experience through content generation that takes into account the user's emotional state. Specific embodiments of this system are described below.

[0766] Users access the system through a terminal and input their profile information and themes they wish to turn into books. This input information is stored on a storage medium by the server. The server uses language analysis tools that utilize natural language processing technology to generate dialogue based on the information provided by the user. Specifically, it utilizes natural language processing libraries (e.g., spaCy and NLTK).

[0767] The user's emotional state is identified by analyzing voice and text in real time using emotion analysis tools. The emotion analysis algorithm uses machine learning models to obtain emotional data from the user's tone and word choices.

[0768] Through text generation methods, extracted information and sentiment data are integrated to generate articles or book chapters. This step is performed using a generation AI model (e.g., GPT-3), and the tone and style of the text are adjusted according to the user's preferred style.

[0769] Visual elements are created using visual generation methods. Image generation algorithms (e.g., DALL-E) are used to automatically generate insert images and cover art to be included in books. This ensures that the content is not only textual but also visually appealing.

[0770] Finally, users review the completed content through their devices and provide feedback. This feedback is incorporated by the publishing support system, and revisions are made as needed. The server processes this feedback based on sentiment analysis and adjusts the content to meet user expectations.

[0771] Specific example: For instance, when creating a story, the system can help add depth to the narrative by suggesting thrilling plot developments at scenes where specific emotions input by the user are heightened.

[0772] Example prompt: "How can you make the story more thrilling at the emotional points the user has indicated?"

[0773] In this way, this system enables the creation of emotionally engaging and creative content for users through a series of means combining hardware and software.

[0774] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0775] Step 1:

[0776] Users access the system through a terminal and input profile information and themes they wish to turn into books. This input information is sent to a server and stored on a storage medium. The input here is text data about the user's themes and preferences, and the stored data is used in the next step.

[0777] Step 2:

[0778] The server generates an interview question list using language analysis tools based on stored theme information. Specifically, it utilizes a natural language processing library to extract keywords related to the input theme and construct questions to elicit information. The output is the generated question list.

[0779] Step 3:

[0780] The user answers interview questions according to a generated list. The user's answers are entered into the terminal in voice or text format. This information is sent to a server, where the emotional state is analyzed in real time by an emotion analysis system. The input is the user's voice and text data, and the output is the analyzed emotion data.

[0781] Step 4:

[0782] The server dynamically adjusts the interview process based on emotional data obtained from emotion analysis tools and interview responses. For example, if the user is agitated, it uses a generative AI model to delve deeper into the questions. The input is the emotional state and responses, and the output is the adjusted interview process.

[0783] Step 5:

[0784] The server uses text generation tools to integrate data obtained from interviews with sentiment data to generate book chapters. This process utilizes a generative AI model to automatically generate text tailored to the user's style. The input consists of interview responses and sentiment data, while the output is the resulting text.

[0785] Step 6:

[0786] The server automatically generates images and designs to be inserted into the book using a visual generation system. In this step, images are generated according to the book's theme and emotional data. The input is emotional data and theme information, and the output is the generated visual content.

[0787] Step 7:

[0788] Users review the content generated through their devices and provide feedback. This feedback is sent to the server, and the content is modified based on the feedback. The input is the feedback information, and the output is the modified content.

[0789] (Application Example 2)

[0790] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0791] The present invention aims to provide a system that offers personalized product recommendations and purchasing support tailored to the user's emotional state at the time of purchasing activities on e-commerce sites and the like. This aims to improve the user's purchasing experience and increase customer satisfaction.

[0792] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0793] In this invention, the server includes storage means for inputting and storing user information, emotion analysis means for analyzing the user's emotional state through image input, and recommendation means for providing product information based on the analyzed emotional information. This enables dynamic and personalized purchasing support that responds to the user's emotional state.

[0794] "User information" refers to the personal data and profile of users that are entered into the system.

[0795] A "storage device" is a system component that has the function of storing and managing data.

[0796] "Language processing means" refers to technologies that have the function of interpreting natural language and generating dialogue with users.

[0797] "Character data generation means" refers to technology that automatically generates and edits text based on extracted information.

[0798] A "visual data generation means" is a system component that has the function of generating images and designs.

[0799] "Emotional analysis methods" refer to technologies that analyze a user's emotional state from data such as facial expressions and voice.

[0800] A "recommendation tool" is a system element that has the function of presenting product information suitable for the user based on analyzed emotional information.

[0801] "Publishing support tools" are technologies that have the function of preparing generated content into a format that can be published.

[0802] This invention utilizes several pieces of hardware and software to realize a system that provides users with a personalized purchasing experience. The system centers around a process that analyzes the user's emotional state in real time and makes product recommendations based on that analysis.

[0803] The server uses storage devices to save user information. To analyze the user's emotional state, it acquires image data from a camera and utilizes image processing and machine learning platforms such as OpenCV and TensorFlow to analyze this data. Once the user's emotions are analyzed, it communicates with the server using Flask and Django Rest Framework to recommend product information based on that analysis. This process enables recommendations optimized for the user's purchasing behavior.

[0804] As a concrete example, when a user is browsing products using a smart device, the system can detect the user's smile and recommend products such as, "Why not check out our popular new autumn / winter collection?" In this way, it is possible to provide an interactive experience tailored to the user's emotions.

[0805] An example of a prompt message could be, "Identify emotions such as joy or surprise from the user's facial expression image, and recommend new product information that matches those emotions." This allows for the continuous provision of services that meet the user's needs.

[0806] This system significantly improves the user's purchasing experience and supports their purchasing decisions.

[0807] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0808] Step 1:

[0809] The user accesses the system using a terminal and enters user information. The terminal records the entered information and sends it to the server. The input includes the user's personal data and profile information, and storage means are used to store this in a database. As a result, the user's basic information is stored in the database.

[0810] Step 2:

[0811] The device uses its camera function to acquire real-time image data of the user's facial expressions. The server receives this image data and performs image processing using OpenCV. The acquired image data is then analyzed using TensorFlow to identify the user's emotional state. The output provides information identifying the analyzed emotion.

[0812] Step 3:

[0813] The server generates prompt sentences for product recommendations based on the analyzed sentiment data. These prompt sentences are then input into a generative AI model to generate optimal product information. The input is sentiment data, and the output is recommended product information. The generated product information is personalized according to the user's emotions.

[0814] Step 4:

[0815] The terminal displays recommended product information received from the server to the user. This display is done on the user's interface, highlighting products that the user is likely to be interested in. As a result, product information that increases the user's purchasing intent is displayed on the terminal.

[0816] Step 5:

[0817] The user provides feedback on the presented product. The device records the feedback and sends it back to the server. The server receives the feedback information and begins the process of revising the generated content. The content is optimized based on the feedback, improving the accuracy of recommendations in the future.

[0818] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0819] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0820] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0821] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0822] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0823] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0824] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0825] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0826] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0827] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0828] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0829] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0830] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0831] 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.

[0832] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0833] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0834] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0835] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0836] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0837] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0838] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0839] The following is further disclosed regarding the embodiments described above.

[0840] (Claim 1)

[0841] A database means for inputting and storing user information,

[0842] A natural language processing system for generating user interactions and extracting information,

[0843] A text generation means for generating and editing text based on extracted information,

[0844] Image generation means for generating images and designs,

[0845] A system that includes publishing support means for outputting generated content in a publishing format.

[0846] (Claim 2)

[0847] The system according to claim 1, characterized by flexibly generating questions according to the content of the interview.

[0848] (Claim 3)

[0849] The system according to claim 1, further comprising means for implementing an editing cycle to modify generated content based on user feedback.

[0850] "Example 1"

[0851] (Claim 1)

[0852] Information recording means for inputting and storing user information,

[0853] A natural language processing method that automatically generates interview questions based on a theme set by the user,

[0854] A recognition means that converts the user's response into text data via speech recognition,

[0855] A text generation model for generating and constructing text based on extracted knowledge data,

[0856] An image generation device for generating insert images and designs for books,

[0857] An editing cycle means that presents the generated text and images to the user, receives feedback, and makes corrections,

[0858] Publishing support means for outputting content in publishing format,

[0859] A system that includes this.

[0860] (Claim 2)

[0861] The system according to claim 1, characterized by having a question generation means that flexibly generates questions according to the content of the interview and elicits the user's experience and knowledge.

[0862] (Claim 3)

[0863] The system according to claim 1, comprising editing means for re-editing generated content in response to user feedback and constructing a book as the final output.

[0864] "Application Example 1"

[0865] (Claim 1)

[0866] A data storage means for inputting and saving user information,

[0867] A natural language processing system for generating user interactions and extracting information,

[0868] A document generation means for generating and editing text based on extracted information,

[0869] Image generation means for generating images and designs,

[0870] A publishing support means for outputting the generated content in a publishing format,

[0871] A system including display control means for delivering content to a terminal according to the user's situation.

[0872] (Claim 2)

[0873] The system according to claim 1, characterized by flexibly generating questions according to the content of the interview.

[0874] (Claim 3)

[0875] The system according to claim 1, further comprising means for implementing an editing cycle to modify generated content based on user feedback.

[0876] "Example 2 of combining an emotion engine"

[0877] (Claim 1)

[0878] A storage medium means for inputting and saving user information,

[0879] A language analysis tool for generating user interactions and extracting information,

[0880] An emotion analysis means for identifying an emotional state by analyzing the user's voice and text,

[0881] A document generation means for generating and editing text based on extracted information and identified emotional states,

[0882] Visual generation means for generating images and designs,

[0883] A system that includes publishing support means for outputting generated content in a publishing format.

[0884] (Claim 2)

[0885] The system according to claim 1, characterized by generating flexible questions that correspond to the content of the interview based on the emotional state and adjusting the progress of the interview.

[0886] (Claim 3)

[0887] The system according to claim 1, further comprising means for implementing an editing cycle to modify generated content based on sentiment analysis in response to user feedback.

[0888] "Application example 2 when combining with an emotional engine"

[0889] (Claim 1)

[0890] A storage means for inputting and saving user information,

[0891] A language processing means for generating dialogue with users and extracting information,

[0892] A character data generation means for generating and editing text based on extracted information,

[0893] A means for generating visual data for generating images and designs,

[0894] An emotion analysis method for analyzing the emotional state of a user through image input,

[0895] A recommendation system for providing product information based on analyzed sentiment information,

[0896] A system that includes publishing support means for outputting generated content in a publishing format.

[0897] (Claim 2)

[0898] The system according to claim 1, characterized by flexibly generating questions according to the content of the interview and the emotional state of the user.

[0899] (Claim 3)

[0900] The system according to claim 1, further comprising means for implementing an editing cycle to modify generated content in response to user feedback and emotional reactions. [Explanation of Symbols]

[0901] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A database means for inputting and storing user information, A natural language processing system for generating user interactions and extracting information, A text generation means for generating and editing text based on extracted information, Image generation means for generating images and designs, A system that includes publishing support means for outputting generated content in a publishing format.

2. The system according to claim 1, characterized by flexibly generating questions according to the content of the interview.

3. The system according to claim 1, further comprising means for implementing an editing cycle to modify generated content based on user feedback.

Images