System

Abstract

A system is provided.SOLUTION: A system comprising: means for a user to input fragments of a story or narrative using an input device; server means for receiving data transmitted from the input device; means for automatically analyzing the data received by the server means and converting it into structured data; artificial intelligence means for automatically generating a story plot or detailed text based on the structured data; means for converting the generated story into a publishing format; and means for providing information necessary for a publishing procedure using the publishing format.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Many people want to turn their thoughts and stories into books, but they often lack the know-how and get stuck on the idea. Many users also find it difficult to understand and follow the process of publishing a book. For this reason, there is a demand for a system that allows people to easily turn their thoughts into books. [Means for solving the problem]

[0005] This invention solves this problem with a system in which users input ideas or fragments of a story using an input device, the data is received by a server, which automatically analyzes and converts it into structured data. AI then automatically generates the story, converts it into a publishing format, and provides the information necessary for the publishing process. Specifically, the AI ​​analyzes the user's input data based on the theme, structure, character settings, etc., and includes a function to regenerate the story while receiving feedback. This makes it easy to go through the process of publishing a book, even if you don't have the know-how.

[0006] "User" refers to the person who uses the system to input their thoughts and stories.

[0007] An "input device" is a device used by a user to input text data, including a smartphone or a personal computer.

[0008] The term "server means" refers to a computing resource that receives data sent from an input device, analyzes it, and performs necessary processing.

[0009] "Structured data" refers to data that has been received, parsed, and organized into a specific format.

[0010] "Artificial intelligence means" refers to algorithms or systems that automatically analyze user input data and generate story plots and detailed text.

[0011] "Natural language processing technology" refers to technology for analyzing text data, understanding its meaning, and processing it.

[0012] "Publication format" refers to the particular format in which a document has been formatted for publication as a book, such as PDF or ePub.

[0013] "Publishing procedures" refers to the steps and procedures required to officially publish a book version of a story, including obtaining an ISBN, registering copyright, etc.

[0014] "Feedback" refers to corrections or additional instructions given by the user to the system. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] This invention is a system that allows users to input fragments of their own thoughts or stories and automatically compiles them into a book. The system includes the following main components:

[0037] 1. The user uses an input device (such as a smartphone or PC) to input fragments of everyday thoughts or stories. For example, the user might input something like, "I had a dream this morning. It was about a huge castle and mysterious creatures..." into a dedicated smartphone application.

[0038] 2. The terminal sends the entered text data to the server. Each time the user completes input, the text data is sent to the server via the Internet.

[0039] 3. The server converts the received text data into structured data and organizes it into the appropriate format, including checking character encoding and handling line breaks and delimiters.

[0040] 4. The server analyzes the content of the input text using AI algorithms, specifically natural language processing (NLP) techniques, to extract themes, themes, character traits, and scenes from the text.

[0041] 5. The server applies an automatic generation algorithm based on the analysis results to create a first draft. Using a plot generation engine, the server generates a story outline based on the extracted themes and character settings, and then uses a language model to automatically generate text including detailed scene descriptions and dialogue.

[0042] 6. The server sends the created draft to the user for review. The user can review the draft via email or in-app notification and make specific corrections or additions.

[0043] 7. The user checks the content of the first draft and makes any necessary corrections or additions via the terminal. For example, the user sends feedback such as, "I would like Character A to be portrayed as more courageous."

[0044] 8. The device sends the feedback text to the server.

[0045] 9. The server regenerates the story based on the feedback. The server detects the corrections and regenerates the text based on the user's requests to create the final version.

[0046] 10. The server converts the final draft into book format. The server adjusts the font size and page layout and converts it into the final PDF or ePub format.

[0047] 11. The server organizes the information necessary for the publishing process and provides it to the user. The server provides the user with specific instructions, such as how to obtain an ISBN, how to register copyright, and how to upload to a publishing platform.

[0048] Specific examples

[0049] Usage example: A user writes down story ideas in a diary format

[0050] 1. A user uses a terminal to type a note such as, "Today, I talked to a stranger at a nice cafe."

[0051] 2. The device sends this memo to the server.

[0052] 3. The server analyzes keywords such as "cafe" and "stranger" to extract themes and settings.

[0053] 4. Based on the extracted information, the server generates a plot in which the conversation between the protagonist and a stranger he meets by chance in a cafe becomes the key to the story.

[0054] 5. The server creates a first draft including detailed scene descriptions based on the generated plot and sends it to the user.

[0055] 6. The user submits a correction request asking for more specific descriptions of strangers' appearances.

[0056] 7. The server adds detailed descriptions of the appearance based on the requests and creates the final draft.

[0057] 8. The server organizes the final draft, converts it into book format, and guides the user through procedures such as obtaining an ISBN and registering copyright.

[0058] This allows users to easily publish their ideas in book form, even if they do not have any special knowledge or skills.

[0059] The processing flow will be explained below.

[0060] Step 1:

[0061] The user inputs thoughts or story fragments using an input device.

[0062] Specifically, the user enters text such as "I had a dream this morning. A huge castle and mysterious creatures..." into a dedicated application on their smartphone or computer.

[0063] Step 2:

[0064] The terminal transmits the input text data to the server.

[0065] Each time the user completes an input, the terminal transmits the text data to a server over the Internet in real time.

[0066] Step 3:

[0067] The server converts the received text data into structured data and organizes it into an appropriate format.

[0068] This includes checking character encoding, normalizing line breaks and delimiters, and removing unnecessary whitespace.

[0069] Step 4:

[0070] The server analyzes the content of the text entered using AI algorithms.

[0071] The server uses natural language processing (NLP) technology to extract elements such as themes, subject matter, character traits, and scenes from the text.

[0072] Step 5:

[0073] The server applies an automatic generation algorithm based on the analysis results to create a first draft.

[0074] The server uses a plot generation engine to generate a story outline based on the extracted themes and character settings, and automatically generates text including detailed scene descriptions and dialogue using language models such as Generative Pretrained Transformers (GPT).

[0075] Step 6:

[0076] The server sends the generated draft to the user for confirmation.

[0077] The server will send a request to the user to review the first draft via email or in-app notification, allowing the user to review it.

[0078] Step 7:

[0079] The user checks the content and requests corrections or additions via the terminal.

[0080] Users read the first draft and enter corrections and additional feedback, such as "I would like Character A to be portrayed as more courageous."

[0081] Step 8:

[0082] The terminal sends the feedback text to the server.

[0083] Any corrections or additions requested by the user are sent from the terminal to the server.

[0084] Step 9:

[0085] The server then recreates the story, incorporating the feedback.

[0086] The server detects the corrections and regenerates the text based on the user's feedback.

[0087] Step 10:

[0088] The server converts the regenerated final draft into book format.

[0089] The server adjusts the font size and page layout, and finally converts it into PDF or ePub format.

[0090] Step 11:

[0091] The server organizes the information necessary for the publishing procedure and provides it to the user.

[0092] The server provides users with detailed instructions on how to obtain an ISBN, how to register copyright, and how to upload to a publishing platform.

[0093] Example 1

[0094] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0095] Until now, there has been a lack of efficient and easy ways for users to turn their thoughts and story fragments into books. Manually organizing and editing text is time-consuming, labor-intensive, and requires specialized knowledge, making it a significant barrier for many users. Therefore, there is a need for an efficient and automated system that allows users to easily turn their ideas into books and publish them.

[0096] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0097] In this invention, the server includes means for automatically analyzing text data and converting it into structured data, artificial intelligence means for automatically generating a story plot and detailed text based on the structured data, and means for receiving feedback from users and regenerating the story by reflecting that feedback. This allows users to efficiently turn their ideas into books and easily publish them without specialized knowledge.

[0098] An "input device" is a device that allows a user to input thoughts or fragments of a story, and specifically includes terminals such as smartphones and personal computers.

[0099] The term "server means" refers to a computer system for receiving and processing data sent from an input device.

[0100] The "means for converting into structured data" is a system that has the function of analyzing the text data received by the server means, checking the character encoding, processing line breaks and delimiters, and organizing the data.

[0101] "Artificial intelligence means" refers to AI algorithms and models that automatically generate story plots and detailed text based on structured data.

[0102] The "means for converting into a publishing format" is a system that has the function of adjusting the font size and page layout and converting the generated story into PDF or ePub format in order to publish it as a book.

[0103] "Means for providing information necessary for publishing procedures" refers to a function that guides users through specific steps such as how to obtain an ISBN, copyright registration procedures, and how to upload to a publishing platform.

[0104] "Natural language processing technology" refers to technology for analyzing input data for themes, structure, and character settings, and includes the use of NLP libraries and algorithms.

[0105] "Means for regenerating a story based on feedback" refers to the function of the server means to receive feedback from users and modify and regenerate the text of the story based on the content of that feedback.

[0106] MODE FOR CARRYING OUT THE INVENTION

[0107] This invention is a system that allows users to input fragments of their own thoughts or stories and automatically compiles them into a book. The system includes the following main components:

[0108] System Components

[0109] 1. Input Devices

[0110] The user uses an input device such as a smartphone or PC to input fragments of everyday thoughts or stories. Specifically, a dedicated application or web interface is used. For example, the user might input, "I had a dream this morning. It was about a huge castle and mysterious creatures..."

[0111] 2. Server Means

[0112] The terminal receives the text data entered by the user and sends it to the server via the Internet using an HTTP POST request.

[0113] The server converts the received text data into structured data by first checking the character encoding, then processing line breaks and delimiters, converting it to JSON format, and escaping special characters.

[0114] The server analyzes the text content using natural language processing (NLP) techniques, such as SpaCy and NLTK, to extract themes, themes, character traits, and scenes from the text.

[0115] Based on the analysis results, the server applies an automated generation algorithm to create a first draft. It uses a plot generation engine to generate a story outline based on the extracted themes and character settings. It then uses a language model (e.g., OpenAI's GPT-3) to generate sentences including detailed scene descriptions and dialogue.

[0116] The server sends the created draft to the user, who receives it via email or in-app notification.

[0117] The server receives feedback from the user, regenerates the story based on that feedback, and creates a final draft from the regenerated text.

[0118] The server converts the final manuscript into a publication format, adjusting the font size and page layout, and converting it into the final PDF or ePub format.

[0119] The server organizes and provides users with the information necessary for the publishing process, including how to obtain an ISBN, how to register copyright, and how to upload to a publishing platform.

[0120] Specific examples

[0121] Usage example: A user writes down story ideas in a diary format

[0122] 1. A user uses a terminal to type a note such as, "Today, I talked to a stranger at a nice cafe."

[0123] 2. The device sends this memo to the server.

[0124] 3. The server converts the received text into JSON format and escapes special characters.

[0125] 4. The server uses natural language processing technology to analyze the keywords "cafe" and "stranger" and extract themes and settings.

[0126] 5. Based on the extracted information, the server generates a plot, such as "The conversation between the protagonist and a stranger he meets by chance in a cafe will be key to the story." Using a generative AI model, the server creates a first draft that includes detailed scene descriptions and dialogue.

[0127] 6. The server sends the first draft to the user.

[0128] 7. The user reads the first draft and inputs a request for revisions on the terminal, saying, "Please be more specific in describing the stranger's appearance."

[0129] 8. The terminal resends the correction request to the server.

[0130] 9. The server analyzes the corrections and creates a final draft with the regenerated text.

[0131] 10. The server converts the final draft into PDF or ePub format and provides the book format to the user.

[0132] 11. The server guides the user on how to obtain an ISBN and register copyright.

[0133] In this way, the system allows users to easily turn their ideas into books and publish them without any special knowledge or skills.

[0134] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0135] Step 1:

[0136] The user inputs everyday thoughts or fragments of stories using an input device such as a smartphone or PC. The input data is in text format, such as "I had a dream this morning. A huge castle and mysterious creatures..." This input data is saved in text format on the device.

[0137] Step 2:

[0138] The terminal receives the input text data and sends it to the server via the Internet. Specifically, it sends the input text to the server using an HTTP POST request. The input of this step is the text data entered by the user, and the output is the text data sent to the server.

[0139] Step 3:

[0140] The server converts the received text data into structured data. First, the server checks the character encoding of the text data, then processes line breaks and delimiters. Specifically, it converts the text data into JSON format and escapes special characters. The input of this step is the text data received by the server, and the output is structured data converted into JSON format.

[0141] Step 4:

[0142] The server uses structured data to analyze the content of the text using natural language processing (NLP) techniques. The server uses an NLP library (e.g., SpaCy or NLTK) to extract themes, themes, character traits, and scenes from the text. The input for this step is structured data, and the output is the analysis results.

[0143] Step 5:

[0144] The server applies an automated generation algorithm based on the analysis results to create a first draft. It uses a plot generation engine to generate a story outline based on the extracted themes and character settings. It then uses a language model (e.g., OpenAI's GPT-3) to generate text with detailed scene descriptions and dialogue. The input for this step is the analysis results, and the output is the first draft text.

[0145] Step 6:

[0146] The server sends the created draft to the user. The server sends the draft to the user via email or in-app notification. The input of this step is the draft text, and the output is the draft sent to the user.

[0147] Step 7:

[0148] The user checks the content of the first draft and inputs any necessary corrections or requests for additions via the terminal. For example, the user may input feedback such as "I would like Character A to be portrayed as more courageous." The input for this step is the first draft that the user checked, and the resulting feedback. The output is the feedback text saved on the terminal.

[0149] Step 8:

[0150] The terminal sends the feedback text to the server. Specifically, it uses an HTTP POST request to send the feedback text requesting correction to the server. The input of this step is the feedback text received from the user, and the output is the feedback text sent to the server.

[0151] Step 9:

[0152] The server regenerates the story based on the feedback. The server detects corrections, regenerates the text based on the user's requests, and creates the final draft. The input to this step is the feedback text received by the server, and the output is the regenerated final draft text.

[0153] Step 10:

[0154] The server converts the final draft into book format, adjusting font size and page layout, and converting it into PDF or ePub format. The input for this step is the final draft text, and the output is the converted data in book format.

[0155] Step 11:

[0156] The server organizes the information necessary for the publishing process and provides it to the user. Specifically, it provides information on how to obtain an ISBN, how to register copyright, how to upload to a publishing platform, etc. The input for this step is data converted into book format, and the output is information on the publishing process.

[0157] (Application example 1)

[0158] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0159] Conventional story creation systems lack the functionality to automatically publish a book simply by inputting users' thoughts or fragments of a story. They also lack the ability to reflect feedback on the stories they create and regenerate them, or the ability to easily post them to content distribution platforms. Furthermore, they lack the information provided to simplify the publishing process, making it difficult for individuals to easily publish their own stories.

[0160] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0161] In this invention, the server includes: a means for a user to input thoughts or story fragments using an input device; a means for receiving data transmitted from the input device; a means for automatically analyzing the data received by the server means and converting it into structured data; an artificial intelligence means for automatically generating a story plot and detailed text based on the structured data; a means for converting the generated story into a publishing format; a means for providing information necessary for the publishing procedure using the publishing format; and a means for automatically posting the generated story to a content distribution platform. This allows users to easily input their own stories, which are then automatically analyzed, generated, regenerated, and distributed. Furthermore, the information necessary for the publishing procedure is also provided, allowing individuals to easily publish their own stories.

[0162] "User" refers to the person who uses the system to input their thoughts and story fragments.

[0163] An "input device" refers to a device such as a smartphone or computer that allows users to input text data.

[0164] "Data receiving means" refers to a mechanism by which the server receives text data sent from the input device.

[0165] "Server" refers to a computing device that processes input data and executes each means of the system.

[0166] "Structured data" refers to text data analyzed by a server that has been organized and systematized into a specific format.

[0167] "Artificial intelligence means" refers to technology that uses natural language processing technology and generative AI models to automatically generate story plots and detailed sentences.

[0168] "Publishing format" refers to the data format used to convert the generated story into a suitable format for a book or e-book.

[0169] "Information required for publishing procedures" refers to the specific information required to publish a book, such as how to obtain an ISBN, copyright registration procedures, and how to upload to a publishing platform.

[0170] "Content distribution platform" refers to an online service or website for publishing generated stories.

[0171] "Feedback" refers to corrections or requests for additions that a user provides to a generated story.

[0172] This invention relates to a system that allows users to easily input fragments of their own thoughts or stories and automatically compile them into a book. The system mainly consists of the following components:

[0173] Users use input devices such as smartphones and computers to input their daily events and ideas. For example, they can enter a note into a dedicated smartphone application saying, "Today, I talked to a stranger at a nice cafe."

[0174] The terminal sends the entered text data to the server, where it is securely transmitted over the Internet.

[0175] The server converts the received text data into structured data, which includes checking character encoding, handling line breaks and delimiters, etc.

[0176] The server uses natural language processing (NLP) to analyze the text and extract themes, characters, and scenes. For example, it extracts key elements of a story from keywords like "cafe" and "stranger."

[0177] The server uses a generative AI model based on the analysis results to automatically generate the plot and detailed sentences of the story. At this time, a generative AI model (e.g., GPT-3) is used to generate prompt sentences. For example, a plot such as "A conversation with a stranger you meet by chance in a cafe is the key to the story" is generated.

[0178] The first draft of the story is sent to the user, who can review it via email or in-app notifications, with any necessary corrections or additions requested. For example, feedback such as "be more specific in describing the stranger's appearance" is provided.

[0179] The server then recreates the story based on the feedback and creates a final draft, again using generative AI models to add specific dialogue and scenes.

[0180] The server converts the final draft into a publishing format and automatically posts it to a content distribution platform, where the resulting story is immediately made available online.

[0181] The server also provides information necessary for the publishing process, including specific steps on how to obtain an ISBN, register copyright, and upload to a publishing platform.

[0182] As a concrete example, consider a user writing down story ideas in a diary format:

[0183] 1. Input sentence: "Today, I talked to a stranger at a nice cafe."

[0184] 2. Prompt after analysis: "The conversation with a stranger you meet by chance in a cafe is the key to the story."

[0185] In this way, users can easily publish their ideas and share them on the content distribution platform, even if they do not have special knowledge or skills. This system provides a new means for individuals to easily generate and distribute creative content.

[0186] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0187] Step 1:

[0188] Users input fragments of thoughts or stories using an input device such as a smartphone or computer.

[0189] Input: Text data entered by the user (e.g., "Today, I spoke with a stranger at a nice cafe.")

[0190] Output: The input text data

[0191] Step 2:

[0192] The terminal transmits the input text data to the server.

[0193] Input: Text data entered in step 1

[0194] Output: Text data sent to the server (sent over the internet)

[0195] Step 3:

[0196] The server converts the received text data into structured data.

[0197] Input: Text data sent from the terminal

[0198] Data processing: Check character encoding, process line breaks and delimiters

[0199] Output: Structured data (e.g., JSON format data)

[0200] Step 4:

[0201] The server analyzes the text using natural language processing (NLP) technology to extract themes, characters, and scenes.

[0202] Input: Structured data from step 3

[0203] Data Computation: Using NLP techniques to analyze text and extract themes, characters, and scenes

[0204] Output: Analysis results (e.g., theme "cafe", character "stranger")

[0205] Step 5:

[0206] The server uses a generative AI model based on the analysis results to automatically generate the story plot and detailed text.

[0207] Input: Analysis results obtained in step 4

[0208] Data computation: Generative AI models (e.g., GPT-3) are used to generate prompts and create story outlines and detailed sentences.

[0209] Output: First draft of the story (e.g., "A conversation with a stranger I met by chance in a cafe is key to the story.")

[0210] Step 6:

[0211] The server sends the generated first draft of the story to the user.

[0212] Input: First draft of the story generated in step 5

[0213] Output: The first draft sent to the user (via email or in-app notification)

[0214] Step 7:

[0215] The user checks the first draft and sends any necessary corrections or requests for additions as feedback via the terminal.

[0216] Input: User feedback (e.g., "Please be more specific in describing strangers' appearances")

[0217] Output: Feedback text

[0218] Step 8:

[0219] The terminal sends the feedback text to the server.

[0220] Input: User feedback text

[0221] Output: Feedback text sent to the server (sent over the internet)

[0222] Step 9:

[0223] The server then recreates the story, incorporating the feedback.

[0224] Input: Feedback text and first draft

[0225] Data Computation: Using Generative AI Models to Regenerate Narratives Reflecting Feedback

[0226] Output: Final draft (e.g. "The story now includes a description of the stranger's appearance")

[0227] Step 10:

[0228] The server converts the final draft into a publishing format and automatically posts it to a content distribution platform.

[0229] Input: Final draft

[0230] Data processing: converting to publishing formats (e.g. PDF, ePub) and posting to content distribution platforms

[0231] Output: Final version posted to distribution platform

[0232] Step 11:

[0233] The server provides the information necessary for the publishing process.

[0234] Input: Final manuscript converted to publication format

[0235] Output: Specific information on how to obtain an ISBN, copyright registration procedures, and how to upload to publishing platforms.

[0236] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0237] This invention is a system in which users input fragments of their own thoughts or stories, and the text data is automatically analyzed, structured, and published as a book. The system also incorporates an emotion engine that analyzes the user's emotions, which also influences the mood and tone of the story. The specific configuration of the system and the program processing are explained below.

[0238] 1. User input:

[0239] A user inputs a thought or fragment of a story using an input device (such as a smartphone or PC). Specifically, the user inputs a note into the application, such as "Today, I talked to a stranger at a nice cafe."

[0240] 2. Data transmission by the device:

[0241] The terminal transmits the input text data to the server. Each time the user completes input, the terminal sends the text data to the server via the Internet.

[0242] 3. Data reception and structuring by the server:

[0243] The server converts the received text data into structured data and organizes it into the appropriate format, which includes checking character encoding, normalizing line breaks and delimiters, and removing unnecessary whitespace.

[0244] 4. Server-based sentiment analysis:

[0245] The server uses an emotion engine to analyze the user's emotion from the input text, for example, extracting "positive" emotion from the text "nice cafe."

[0246] 5. AI analysis and story generation by the server:

[0247] The server uses AI algorithms (including NLP technology) to analyze the input data for themes, themes, character traits, and scenes, and generates a story with the appropriate mood and tone based on the emotional information obtained from the emotion engine.

[0248] 6. Server-generated first draft:

[0249] The server uses a plot generation engine to create a story outline based on the analysis results, and automatically generates text including detailed scene descriptions and dialogue using a language model (e.g., GPT).

[0250] 7. Server sends first draft and requests confirmation:

[0251] The server sends the created draft to the user for confirmation. The user receives a request to review the draft via email or in-app notification and confirms the content.

[0252] 8. User Feedback:

[0253] The user checks the content and requests corrections or additions via the terminal. For example, they input feedback such as "I want Character A to be portrayed as more courageous."

[0254] 9. Sending Feedback via Device:

[0255] The terminal sends the feedback text to the server.

[0256] 10. Server Regeneration:

[0257] The server analyzes the feedback and uses an emotion engine to parse the intent of the feedback and recreate the story.

[0258] 11. Final Draft by Server:

[0259] The server creates a final draft based on the regenerated story and converts it into a book format, adjusting the font size and page layout, and finally converting it into PDF or ePub format.

[0260] 12. Server Providing Publishing Information:

[0261] The server organizes and provides users with information necessary for publishing, such as how to obtain an ISBN, copyright registration procedures, and how to upload to a publishing platform.

[0262] Specific examples

[0263] Usage example: A user writes down story ideas in a diary format

[0264] 1. A user uses a terminal to type a note such as, "Today, I talked to a stranger at a nice cafe."

[0265] 2. The device sends this memo to the server.

[0266] 3. The server analyzes the keywords "cafe" and "stranger" and extracts themes and settings.

[0267] 4. The server extracts "positive emotions" based on the analysis results and the emotion engine, and sets the tone of the story.

[0268] 5. The server generates a plot that states, "The conversation between the protagonist and a stranger he meets by chance in a cafe is the key to the story."

[0269] 6. The server creates a first draft including detailed scene descriptions based on the generated plot and sends it to the user.

[0270] 7. A user submits a correction request asking for more specific descriptions of strangers' appearances.

[0271] 8. The server uses an emotion engine to analyze the intent of the feedback, add detailed descriptions of the appearance, and create the final draft.

[0272] 9. The server organizes the final draft, converts it into book format, and then guides the user through procedures such as obtaining an ISBN and registering copyright.

[0273] This allows users to easily turn their ideas into books, even if they do not have any special knowledge or skills. The introduction of an emotion engine also optimizes the tone and atmosphere of the story, resulting in a more unique book.

[0274] The processing flow will be explained below.

[0275] Step 1:

[0276] The user inputs thoughts or story fragments using an input device.

[0277] Specifically, the user enters the text "Today, I talked to a stranger at a nice cafe" into a dedicated application on their smartphone or computer.

[0278] Step 2:

[0279] The terminal transmits the input text data to the server.

[0280] Once the user has completed the input, the device transmits the text data to a server in real time via the Internet.

[0281] Step 3:

[0282] The server converts the received text data into structured data and organizes it into an appropriate format.

[0283] The server checks the character encoding, normalizes line breaks and delimiters, removes unnecessary whitespace, etc.

[0284] Step 4:

[0285] The server uses an emotion engine to analyze the user's emotion from the input text.

[0286] For example, extract positive emotions from the phrase "nice cafe."

[0287] Step 5:

[0288] The server analyzes the text content using AI algorithms.

[0289] The server uses natural language processing (NLP) techniques to extract themes, themes, character traits, and scenes from the text.

[0290] Step 6:

[0291] The server generates a story plot and detailed text based on the analysis results and emotional information.

[0292] The server uses a plot generation engine to create a story outline based on the extracted themes and character settings, and then automatically generates detailed scene descriptions and dialogue using a language model (e.g., GPT).

[0293] Step 7:

[0294] The server sends the created draft to the user for confirmation.

[0295] The server will send the user a request to review the first draft via email or in-app notification.

[0296] Step 8:

[0297] The user checks the content and requests corrections or additions via the terminal.

[0298] Users read the first draft and enter feedback such as "I would like Character A to be portrayed as more courageous."

[0299] Step 9:

[0300] The terminal sends the feedback text to the server.

[0301] Any corrections or additions requested by the user are sent from the terminal to the server.

[0302] Step 10:

[0303] The server analyzes the feedback and uses an emotion engine to parse the intent of the feedback.

[0304] The server performs sentiment analysis on the feedback text to understand exactly what changes the user wants to make.

[0305] Step 11:

[0306] The server then recreates the story, incorporating the feedback.

[0307] The server detects the corrections and regenerates the text based on the feedback to produce the final draft.

[0308] Step 12:

[0309] The server converts the final draft into book format.

[0310] The server adjusts the font size and page layout, and finally converts it into PDF or ePub format.

[0311] Step 13:

[0312] The server organizes the information necessary for the publishing procedure and provides it to the user.

[0313] The server compiles information such as how to obtain an ISBN, copyright registration procedures, and how to upload to a publishing platform and presents it to the user.

[0314] Example 2

[0315] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0316] In today's world, many users are seeking an easy way to publish their thoughts and stories, but conventional systems require specialized knowledge and skills, making this difficult. Furthermore, there was a lack of means to properly optimize the atmosphere and tone of a story, making it difficult to reflect the user's intended emotions. Furthermore, the lack of flexibility in regenerating the story based on user feedback made it difficult to create a highly polished work.

[0317] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for automatically analyzing received data and converting it into structured data, artificial intelligence means for automatically generating a story plot and detailed sentences based on the structured data, means for optimizing the mood and tone of the generated story using a sentiment analysis engine, and means for receiving feedback from users and analyzing the feedback to regenerate the story. This makes it possible for users without specialized knowledge or skills to easily publish their own ideas and stories, optimize the mood and tone using a sentiment analysis engine, and regenerate a story that reflects the feedback.

[0318] "User" refers to an individual who uses the system to input their thoughts and story fragments.

[0319] "Input device" refers to a device such as a smartphone, PC, or tablet that allows a user to input text data.

[0320] A "server" refers to a computer system that receives data sent from an input device and performs processes such as analysis, data conversion, and story generation.

[0321] "Structured data" refers to data that has been converted from received text data into a regular format, and is organized to make it easier to analyze and process.

[0322] "Artificial intelligence means" refers to algorithms and technologies for automatically generating story plots and detailed text based on received structured data.

[0323] "Publishing format" refers to the standards and templates for converting the generated story into a format that can be published as a book (e.g., PDF or ePub format).

[0324] An "emotion analysis engine" refers to technology or software that analyzes emotions from text data and optimizes the mood and tone of the data.

[0325] "Feedback" refers to opinions such as requests for corrections or additions made by users to the generated story.

[0326] "Regeneration" refers to the process of reflecting on the feedback received and regenerating the story.

[0327] "Plot" refers to the outline or structure of a story, and is an outline that includes the overall flow of the story and major events.

[0328] This invention is a system that allows users to input fragments of their own thoughts or stories, analyzes and structures the text data, and then publishes it as a book. The system also includes an emotion engine that analyzes the user's emotions and a function that regenerates the story based on the feedback. The specific configuration of the system and the program processing are described below.

[0329] Hardware and Software

[0330] Hardware used: smartphone, PC, tablet (user input device), server (computing device for data analysis and story generation)

[0331] Software used: Application software (UI for user input), server software (data reception, analysis, generation, and transmission), sentiment analysis engine, AI algorithm (including natural language processing technology)

[0332] Program processing flow

[0333] User input: A user uses a smartphone or computer application to input a thought or fragment of a story, for example, "Today I spoke with a stranger in a nice cafe."

[0334] Sending data from the device: The device sends the text data entered to the server using an HTTP POST request.

[0335] Server data reception and structuring: The text data received by the server is taken and structured in JSON format. Character encoding is checked, line breaks and delimiters are normalized, and unnecessary whitespace is removed.

[0336] Sentiment analysis: The server uses an emotion engine to analyze emotions from text data. For example, it extracts "positive emotions" from the phrase "nice cafe." Sentiment analysis uses an emotion classification API.

[0337] AI analysis and story generation: The server uses natural language processing technology (such as the BERT model) to analyze the text data for themes, character traits, and scenes. Based on the analysis results, it sets the mood and tone of the story and generates a plot.

[0338] First Draft Creation: The server uses a plot generation engine to automatically generate a first draft including detailed scene descriptions and dialogue. A language model (e.g., GPT-3 or GPT-4 (registered trademark)) is used to generate sentences.

[0339] Sending and reviewing the first draft: The server sends the first draft to the user for review. The user receives a link to the first draft via email or in-app notification.

[0340] User feedback: The user reviews the content and inputs requests for corrections or additions. For example, they can send feedback such as, "Please be more specific in describing the stranger's appearance."

[0341] Regeneration: The server receives and analyzes the feedback, and generates a new story using the regeneration algorithm. The emotion engine analyzes the intent of the feedback.

[0342] Final drafting and conversion to publication format: The server creates a final draft based on the regenerated story and converts it to a publication format (PDF or ePub). It then cleans up the final draft and adjusts the font size and page layout.

[0343] Providing information on publishing procedures: The server provides users with the necessary information, such as how to obtain an ISBN, how to register copyright, and how to upload to a publishing platform.

[0344] Examples and prompts

[0345] Usage example: A user writes down story ideas in diary format

[0346] 1. A user uses a terminal to type a note such as, "Today, I talked to a stranger at a nice cafe."

[0347] 2. The device sends this memo to the server.

[0348] 3. The server analyzes the keywords "cafe" and "stranger" and extracts themes and settings.

[0349] 4. The server extracts "positive emotions" based on the analysis results and the emotion engine, and sets the tone of the story.

[0350] 5. The server generates a plot in which the conversation between the protagonist and a stranger he meets by chance in a cafe becomes the key to the story.

[0351] 6. The server creates a first draft based on the generated plot, including detailed scene descriptions, and sends it to the user.

[0352] 7. A user submits a correction request asking for more specific descriptions of strangers' appearances.

[0353] 8. The server uses an emotion engine to analyze the intent of the feedback, add detailed descriptions of the appearance, and create the final draft.

[0354] 9. The server organizes the final draft, converts it into book format, and then guides the user through procedures such as obtaining an ISBN and registering copyright.

[0355] Example prompt sentence:

[0356] "Today I had a conversation with a stranger at a nice cafe. Please generate a story that includes emotions."

[0357] This invention allows users to easily turn their ideas into books without any special knowledge or skills. By introducing an emotion engine, the tone and atmosphere of the story are optimized, resulting in a more unique book.

[0358] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0359] Step 1:

[0360] User-entered text

[0361] A user uses a smartphone or PC application to input a thought or fragment of a story, for example, "Today I spoke with a stranger at a nice cafe." The input data is sent from the device's input device to the application software.

[0362] input:

[0363] User-entered text (e.g., "I talked to a stranger at a nice cafe today.")

[0364] output:

[0365] Input text data stored on the device

[0366] Step 2:

[0367] Data transmission by the terminal

[0368] The device sends the text data entered by the user to the server. The device sends the data to the server over the Internet using an HTTP POST request.

[0369] input:

[0370] Input text data stored on the device

[0371] output:

[0372] Text data transferred to the server

[0373] Step 3:

[0374] Data reception and structuring by the server

[0375] The server receives the text data, parses it, and structures it. It checks the character encoding of the received data, normalizes line breaks and delimiters, and removes unnecessary whitespace. The structured data is then converted into a format such as JSON.

[0376] input:

[0377] Text data transferred to the server

[0378] output:

[0379] Structured data (e.g., JSON format)

[0380] Specific behavior:

[0381] Check the character encoding

[0382] Line break / delimiter normalization

[0383] Removing unnecessary whitespace

[0384] Conversion to JSON format

[0385] Step 4:

[0386] Emotion analysis by server

[0387] The server uses a sentiment analysis engine to analyze emotions from text data. For example, it extracts "positive emotion" from the phrase "nice cafe." It uses a sentiment classification API for sentiment analysis.

[0388] input:

[0389] Structured data

[0390] output:

[0391] Sentiment analysis results (e.g., "positive")

[0392] Specific behavior:

[0393] Call the sentiment classification API to perform sentiment analysis.

[0394] Add sentiment analysis results to structured data

[0395] Step 5:

[0396] AI analysis and story generation by server

[0397] The server uses natural language processing technology (e.g., the BERT model) to analyze the text data for themes, character traits, and scenes. Based on the analysis results, it sets the mood and tone of the story and generates a plot. The generation algorithm uses a language model (e.g., GPT-3, GPT-4).

[0398] input:

[0399] Structured data and sentiment analysis results

[0400] output:

[0401] Generated plot and narrative tone setting

[0402] Specific behavior:

[0403] Theme, character traits, and scene analysis

[0404] Setting the mood and tone

[0405] Plot generation using language models

[0406] Step 6:

[0407] Server-based first draft creation

[0408] The server uses a plot generation engine to automatically generate a first draft based on the created plot, including detailed scene descriptions and dialogue, and generates sentences using a language model (e.g., GPT-3 or GPT-4).

[0409] input:

[0410] Generated plot and narrative tone setting

[0411] output:

[0412] First draft text data

[0413] Specific behavior:

[0414] Start the plot generation engine

[0415] Automatically generate detailed scene descriptions and dialogue using language models

[0416] Step 7:

[0417] Server sends first draft and requests confirmation

[0418] The server sends the first draft to the user for review, and sends the user a link to the first draft via email or in-app notification.

[0419] input:

[0420] First draft text data

[0421] output:

[0422] Link to the first draft sent to the user

[0423] Specific behavior:

[0424] Send the first draft text data via email or in-app notification

[0425] Step 8:

[0426] User Feedback

[0427] The user checks the content and inputs requests for corrections or additions, such as "Please be more specific in describing the appearance of strangers."

[0428] input:

[0429] User feedback text

[0430] output:

[0431] Feedback text data

[0432] Specific behavior:

[0433] Enter feedback text through the application

[0434] Step 9:

[0435] Sending feedback via device

[0436] The device sends the feedback text to the server using an HTTP POST request or WebSocket.

[0437] input:

[0438] Feedback Text

[0439] output:

[0440] Feedback text forwarded to the server

[0441] Specific behavior:

[0442] Send data via HTTP POST request or WebSocket

[0443] Step 10:

[0444] Server Regeneration

[0445] The server receives and analyzes the feedback, and generates a new story using a regeneration algorithm. The emotion engine analyzes the intent of the feedback.

[0446] input:

[0447] Feedback and existing story data

[0448] output:

[0449] Regenerated narrative text

[0450] Specific behavior:

[0451] Analyzing feedback content

[0452] Analyze intent using an emotion engine

[0453] Regenerating stories using regeneration algorithms

[0454] Step 11:

[0455] Final draft creation and conversion to publication format by the server

[0456] The server creates a final draft based on the regenerated story and converts it into a publishing format (PDF or ePub), adjusting the font size and page layout.

[0457] input:

[0458] Regenerated narrative text

[0459] output:

[0460] Publication format of the final manuscript (PDF or ePub)

[0461] Specific behavior:

[0462] Creating the final draft

[0463] Adjusting font size and page layout

[0464] Conversion to publishing format

[0465] Step 12:

[0466] Server provides publishing procedure information

[0467] The server provides users with all the necessary information, such as how to obtain an ISBN, how to register copyright, and how to upload to a publishing platform.

[0468] input:

[0469] Publication-formatted final manuscript

[0470] output:

[0471] Providing information necessary for publication procedures

[0472] Specific behavior:

[0473] Information on how to obtain an ISBN

[0474] Providing information on copyright registration procedures

[0475] Guidance on how to upload to publishing platforms

[0476] (Application example 2)

[0477] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0478] This invention relates to a system that automatically analyzes and structures the thoughts and story fragments entered by users on a daily basis to generate high-quality stories. In particular, by incorporating an emotion analysis function, the system can adjust the mood and tone of the story and regenerate it based on user feedback, enabling the provision of more personalized stories. Furthermore, by providing a function to share the generated stories on content distribution services, the system aims to promote the dissemination of creative content.

[0479] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for a user to input fragments of thoughts or stories using an input device, a means for receiving data transmitted from the input device, and a means for automatically analyzing the data received by the server means and converting it into structured data. This enables automatic analysis and structuring of text data.

[0480] The system further includes an artificial intelligence means for automatically generating a story plot and detailed text based on the structured data, a means for converting the generated story into a publishing format, a means for providing information required for publishing procedures using the publishing format, a means for receiving user feedback and regenerating the story while reflecting the feedback, a means for extracting emotional information from the user's input data using an emotion analysis engine and reflecting the emotional information in the story's mood and tone, and a means for sharing the generated story or essay on a content distribution service. This makes it possible to adjust the mood and tone through emotion analysis, regenerate the story while reflecting the feedback, and share content.

[0481] An "input device" is a device that allows a user to input text data, and includes smartphones, personal computers, etc.

[0482] The "server means" is a computer system for receiving and processing data sent from an input device.

[0483] "Structured data" is a data format that is created by analyzing input text data and organizing it based on certain rules.

[0484] "Artificial intelligence means" refers to technology that uses machine learning and natural language processing techniques to automatically generate story plots and detailed sentences.

[0485] A "publishing format" is a format in which the generated story is converted into a form that can be published in print or as an e-book.

[0486] An "emotion analysis engine" is a technology that extracts emotional information from text data entered by the user and reflects that emotional information in the tone and atmosphere of the story.

[0487] A "content distribution service" is a platform for sharing generated stories and essays with other users via the Internet.

[0488] "Feedback" refers to comments and suggestions that users give to the generated story for correction or improvement.

[0489] "Regeneration" is the process of regenerating an existing story based on feedback from users.

[0490] This invention is a system that automatically analyzes and structures the thoughts and story fragments entered by users on a daily basis, generating high-quality stories. In particular, it has an emotion analysis function that adjusts the mood and tone of the story and regenerates it based on user feedback. The system also has a function to share the generated stories and essays on a content distribution service.

[0491] First, a user inputs their thoughts or fragments of a story using an input device (such as a smartphone or PC). The text data sent from the input device is then sent over the Internet to a server. The server receives this data, automatically parses it, and converts it into structured data. This process includes checking character encoding, normalizing line breaks and delimiters, and removing unnecessary whitespace.

[0492] Based on the structured data, the server uses artificial intelligence means (e.g., a generative AI model such as GPT-4) to automatically generate a story plot and detailed text. The server then uses a sentiment analysis engine (e.g., IBM Watson® sentiment analysis API) to extract emotional information from the text data and reflect it in the mood and tone of the story. The generated story is then converted into a publishing format (e.g., PDF or ePub format).

[0493] The generated story can be shared on a content distribution service. The user reviews the generated story and sends feedback to the server for corrections and improvements as needed. The server then uses the sentiment analysis engine again to regenerate the story, incorporating the feedback. This process allows the user to complete a work that is more in line with their own intentions and emotions.

[0494] As a concrete example, consider the case where a user enters a note saying, "Today, the sunset was so beautiful, and I can't forget the moment I talked with my friend in the park." The server receives this text and provides the following prompt to the generative AI model:

[0495] "User input text: 'Today the sunset was so beautiful, and I can't forget the moment I talked with my friend in the park.' Based on this text, generate a short story with a positive tone. The themes are 'friendship' and 'beauty of nature.' Also, reflect the emotion 'happiness' as the result of the sentiment analysis engine."

[0496] Based on this prompt, the generative AI model automatically generates a story with detailed scene descriptions and dialogue, and then receives user feedback to regenerate the content, providing a more satisfying story.

[0497] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0498] Step 1:

[0499] The user inputs fragments of thoughts or stories using an input device (such as a smartphone or PC), for example, by entering text data such as "Today, the sunset was so beautiful, and I can't forget the moment I talked with my friend in the park."

[0500] Step 2:

[0501] The terminal (input device) sends the input text data to the server via the Internet. The input data is sent in JSON format, and the server receives this data.

[0502] Step 3:

[0503] The server converts the received text data into structured data, which includes checking character encoding, normalizing line breaks and delimiters, and removing unnecessary whitespace. The resulting structured data is then ready for further analysis.

[0504] Step 4:

[0505] The server uses a sentiment analysis engine to extract emotional information from the input text. For example, it uses IBM Watson's sentiment analysis API to extract "positive" emotions from keywords such as "beautiful," "spoke," and "unforgettable." The emotional information is reflected in the next AI analysis.

[0506] Step 5:

[0507] The server uses a generative AI model (e.g., GPT-4) to analyze the input data for theme, plot, and character settings. Based on the previously extracted emotional information, the server generates a story with the appropriate mood and tone. The generated text is saved as a first draft. Specifically, the generative AI model is given the following prompt:

[0508] "User input text: 'Today the sunset was so beautiful, and I can't forget the moment I talked with my friend in the park.' Based on this text, generate a short story with a positive tone. The themes are 'friendship' and 'beauty of nature.' Also, reflect the emotion 'happiness' as the result of the sentiment analysis engine."

[0509] Step 6:

[0510] The server sends the generated draft to the user for confirmation. The user receives the draft via email or in-app notification and checks the text.

[0511] Step 7:

[0512] The user checks the content and, if necessary, enters feedback for corrections or additions through the terminal. For example, the user can enter specific feedback such as "Please be more specific in describing the appearance of strangers."

[0513] Step 8:

[0514] The device sends the feedback text to the server. The feedback data is also sent in JSON format, and the server receives this data.

[0515] Step 9:

[0516] The server analyzes the feedback and uses an emotion engine to analyze the intent of the feedback. Based on the analysis results, the generative AI model is used again to regenerate the story. The newly generated story reflects the feedback.

[0517] Step 10:

[0518] The server converts the final generated story into a publishing format, adjusting the font size and page layout, and finally converting it into PDF or ePub format.

[0519] Step 11:

[0520] The server shares the generated stories and essays on a content distribution service, allowing users to share their stories with other users via the Internet.

[0521] Step 12:

[0522] By publishing the stories created by users on content distribution services and having them viewed by many users, the spread of creative content will be promoted.

[0523] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0524] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0525] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0526] [Second embodiment]

[0527] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0528] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0529] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0530] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0531] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0532] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0533] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0534] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0535] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0536] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0537] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0538] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0539] This invention is a system that allows users to input fragments of their own thoughts or stories and automatically compiles them into a book. The system includes the following main components:

[0540] 1. The user uses an input device (such as a smartphone or PC) to input fragments of everyday thoughts or stories. For example, the user might input something like, "I had a dream this morning. It was about a huge castle and mysterious creatures..." into a dedicated smartphone application.

[0541] 2. The terminal sends the entered text data to the server. Each time the user completes input, the text data is sent to the server via the Internet.

[0542] 3. The server converts the received text data into structured data and organizes it into the appropriate format, including checking character encoding and handling line breaks and delimiters.

[0543] 4. The server analyzes the content of the input text using AI algorithms, specifically natural language processing (NLP) techniques, to extract themes, themes, character traits, and scenes from the text.

[0544] 5. The server applies an automatic generation algorithm based on the analysis results to create a first draft. Using a plot generation engine, the server generates a story outline based on the extracted themes and character settings, and then uses a language model to automatically generate text including detailed scene descriptions and dialogue.

[0545] 6. The server sends the created draft to the user for review. The user can review the draft via email or in-app notification and make specific corrections or additions.

[0546] 7. The user checks the content of the first draft and makes any necessary corrections or additions via the terminal. For example, the user sends feedback such as, "I would like Character A to be portrayed as more courageous."

[0547] 8. The device sends the feedback text to the server.

[0548] 9. The server regenerates the story based on the feedback. The server detects the corrections and regenerates the text based on the user's requests to create the final version.

[0549] 10. The server converts the final draft into book format. The server adjusts the font size and page layout and converts it into the final PDF or ePub format.

[0550] 11. The server organizes the information necessary for the publishing process and provides it to the user. The server provides the user with specific instructions, such as how to obtain an ISBN, how to register copyright, and how to upload to a publishing platform.

[0551] Specific examples

[0552] Usage example: A user writes down story ideas in a diary format

[0553] 1. A user uses a terminal to type a note such as, "Today, I talked to a stranger at a nice cafe."

[0554] 2. The device sends this memo to the server.

[0555] 3. The server analyzes keywords such as "cafe" and "stranger" to extract themes and settings.

[0556] 4. Based on the extracted information, the server generates a plot in which the conversation between the protagonist and a stranger he meets by chance in a cafe becomes the key to the story.

[0557] 5. The server creates a first draft including detailed scene descriptions based on the generated plot and sends it to the user.

[0558] 6. The user submits a correction request asking for more specific descriptions of strangers' appearances.

[0559] 7. The server adds detailed descriptions of the appearance based on the requests and creates the final draft.

[0560] 8. The server organizes the final draft, converts it into book format, and guides the user through procedures such as obtaining an ISBN and registering copyright.

[0561] This allows users to easily publish their ideas in book form, even if they do not have any special knowledge or skills.

[0562] The processing flow will be explained below.

[0563] Step 1:

[0564] The user inputs thoughts or story fragments using an input device.

[0565] Specifically, the user enters text such as "I had a dream this morning. A huge castle and mysterious creatures..." into a dedicated application on their smartphone or computer.

[0566] Step 2:

[0567] The terminal transmits the input text data to the server.

[0568] Each time the user completes an input, the terminal transmits the text data to a server over the Internet in real time.

[0569] Step 3:

[0570] The server converts the received text data into structured data and organizes it into an appropriate format.

[0571] This includes checking character encoding, normalizing line breaks and delimiters, and removing unnecessary whitespace.

[0572] Step 4:

[0573] The server analyzes the content of the text entered using AI algorithms.

[0574] The server uses natural language processing (NLP) technology to extract elements such as themes, subject matter, character traits, and scenes from the text.

[0575] Step 5:

[0576] The server applies an automatic generation algorithm based on the analysis results to create a first draft.

[0577] The server uses a plot generation engine to generate a story outline based on the extracted themes and character settings, and automatically generates text including detailed scene descriptions and dialogue using language models such as Generative Pretrained Transformers (GPT).

[0578] Step 6:

[0579] The server sends the generated draft to the user for confirmation.

[0580] The server will send a request to the user to review the first draft via email or in-app notification, allowing the user to review it.

[0581] Step 7:

[0582] The user checks the content and requests corrections or additions via the terminal.

[0583] Users read the first draft and enter corrections and additional feedback, such as "I would like Character A to be portrayed as more courageous."

[0584] Step 8:

[0585] The terminal sends the feedback text to the server.

[0586] Any corrections or additions requested by the user are sent from the terminal to the server.

[0587] Step 9:

[0588] The server then recreates the story, incorporating the feedback.

[0589] The server detects the corrections and regenerates the text based on the user's feedback.

[0590] Step 10:

[0591] The server converts the regenerated final draft into book format.

[0592] The server adjusts the font size and page layout, and finally converts it into PDF or ePub format.

[0593] Step 11:

[0594] The server organizes the information necessary for the publishing procedure and provides it to the user.

[0595] The server provides users with detailed instructions on how to obtain an ISBN, how to register copyright, and how to upload to a publishing platform.

[0596] Example 1

[0597] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0598] Until now, there has been a lack of efficient and easy ways for users to turn their thoughts and story fragments into books. Manually organizing and editing text is time-consuming, labor-intensive, and requires specialized knowledge, making it a significant barrier for many users. Therefore, there is a need for an efficient and automated system that allows users to easily turn their ideas into books and publish them.

[0599] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0600] In this invention, the server includes means for automatically analyzing text data and converting it into structured data, artificial intelligence means for automatically generating a story plot and detailed text based on the structured data, and means for receiving feedback from users and regenerating the story by reflecting that feedback. This allows users to efficiently turn their ideas into books and easily publish them without specialized knowledge.

[0601] An "input device" is a device that allows a user to input thoughts or fragments of a story, and specifically includes terminals such as smartphones and personal computers.

[0602] The term "server means" refers to a computer system for receiving and processing data sent from an input device.

[0603] The "means for converting into structured data" is a system that has the function of analyzing the text data received by the server means, checking the character encoding, processing line breaks and delimiters, and organizing the data.

[0604] "Artificial intelligence means" refers to AI algorithms and models that automatically generate story plots and detailed text based on structured data.

[0605] The "means for converting into a publishing format" is a system that has the function of adjusting the font size and page layout and converting the generated story into PDF or ePub format in order to publish it as a book.

[0606] "Means for providing information necessary for publishing procedures" refers to a function that guides users through specific steps such as how to obtain an ISBN, copyright registration procedures, and how to upload to a publishing platform.

[0607] "Natural language processing technology" refers to technology for analyzing input data for themes, structure, and character settings, and includes the use of NLP libraries and algorithms.

[0608] "Means for regenerating a story based on feedback" refers to the function of the server means to receive feedback from users and modify and regenerate the text of the story based on the content of that feedback.

[0609] MODE FOR CARRYING OUT THE INVENTION

[0610] This invention is a system that allows users to input fragments of their own thoughts or stories and automatically compiles them into a book. The system includes the following main components:

[0611] System Components

[0612] 1. Input Devices

[0613] The user uses an input device such as a smartphone or PC to input fragments of everyday thoughts or stories. Specifically, a dedicated application or web interface is used. For example, the user might input, "I had a dream this morning. It was about a huge castle and mysterious creatures..."

[0614] 2. Server Means

[0615] The terminal receives the text data entered by the user and sends it to the server via the Internet using an HTTP POST request.

[0616] The server converts the received text data into structured data by first checking the character encoding, then processing line breaks and delimiters, converting it to JSON format, and escaping special characters.

[0617] The server analyzes the text content using natural language processing (NLP) techniques, such as SpaCy and NLTK, to extract themes, themes, character traits, and scenes from the text.

[0618] Based on the analysis results, the server applies an automated generation algorithm to create a first draft. It uses a plot generation engine to generate a story outline based on the extracted themes and character settings. It then uses a language model (such as OpenAI's GPT-3) to generate sentences with detailed scene descriptions and dialogue.

[0619] The server sends the created draft to the user, who receives it via email or in-app notification.

[0620] The server receives feedback from the user, regenerates the story based on that feedback, and creates a final draft from the regenerated text.

[0621] The server converts the final manuscript into a publication format, adjusting the font size and page layout, and converting it into the final PDF or ePub format.

[0622] The server organizes and provides users with the information necessary for the publishing process, including how to obtain an ISBN, how to register copyright, and how to upload to a publishing platform.

[0623] Specific examples

[0624] Usage example: A user writes down story ideas in a diary format

[0625] 1. A user uses a terminal to type a note such as, "Today, I talked to a stranger at a nice cafe."

[0626] 2. The device sends this memo to the server.

[0627] 3. The server converts the received text into JSON format and escapes special characters.

[0628] 4. The server uses natural language processing technology to analyze the keywords "cafe" and "stranger" and extract themes and settings.

[0629] 5. Based on the extracted information, the server generates a plot, such as "The conversation between the protagonist and a stranger he meets by chance in a cafe will be key to the story." Using a generative AI model, the server creates a first draft that includes detailed scene descriptions and dialogue.

[0630] 6. The server sends the first draft to the user.

[0631] 7. The user reads the first draft and inputs a request for revisions on the terminal, saying, "Please be more specific in describing the stranger's appearance."

[0632] 8. The terminal resends the correction request to the server.

[0633] 9. The server analyzes the corrections and creates a final draft with the regenerated text.

[0634] 10. The server converts the final draft into PDF or ePub format and provides the book format to the user.

[0635] 11. The server guides the user on how to obtain an ISBN and register copyright.

[0636] In this way, the system allows users to easily turn their ideas into books and publish them without any special knowledge or skills.

[0637] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0638] Step 1:

[0639] The user inputs everyday thoughts or fragments of stories using an input device such as a smartphone or PC. The input data is in text format, such as "I had a dream this morning. A huge castle and mysterious creatures..." This input data is saved in text format on the device.

[0640] Step 2:

[0641] The terminal receives the input text data and sends it to the server via the Internet. Specifically, it sends the input text to the server using an HTTP POST request. The input of this step is the text data entered by the user, and the output is the text data sent to the server.

[0642] Step 3:

[0643] The server converts the received text data into structured data. First, the server checks the character encoding of the text data, then processes line breaks and delimiters. Specifically, it converts the text data into JSON format and escapes special characters. The input of this step is the text data received by the server, and the output is structured data converted into JSON format.

[0644] Step 4:

[0645] The server uses structured data to analyze the content of the text using natural language processing (NLP) techniques. The server uses an NLP library (e.g., SpaCy or NLTK) to extract themes, themes, character traits, and scenes from the text. The input for this step is structured data, and the output is the analysis results.

[0646] Step 5:

[0647] The server applies an automated generation algorithm based on the analysis results to create a first draft. It uses a plot generation engine to generate a story outline based on the extracted themes and character settings. It then uses a language model (e.g., OpenAI's GPT-3) to generate text with detailed scene descriptions and dialogue. The input for this step is the analysis results, and the output is the first draft text.

[0648] Step 6:

[0649] The server sends the created draft to the user. The server sends the draft to the user via email or in-app notification. The input of this step is the draft text, and the output is the draft sent to the user.

[0650] Step 7:

[0651] The user checks the content of the first draft and inputs any necessary corrections or requests for additions via the terminal. For example, the user may input feedback such as "I would like Character A to be portrayed as more courageous." The input for this step is the first draft that the user checked, and the resulting feedback. The output is the feedback text saved on the terminal.

[0652] Step 8:

[0653] The terminal sends the feedback text to the server. Specifically, it uses an HTTP POST request to send the feedback text requesting correction to the server. The input of this step is the feedback text received from the user, and the output is the feedback text sent to the server.

[0654] Step 9:

[0655] The server regenerates the story based on the feedback. The server detects corrections, regenerates the text based on the user's requests, and creates the final draft. The input to this step is the feedback text received by the server, and the output is the regenerated final draft text.

[0656] Step 10:

[0657] The server converts the final draft into book format, adjusting font size and page layout, and converting it into PDF or ePub format. The input for this step is the final draft text, and the output is the converted data in book format.

[0658] Step 11:

[0659] The server organizes the information necessary for the publishing process and provides it to the user. Specifically, it provides information on how to obtain an ISBN, how to register copyright, how to upload to a publishing platform, etc. The input for this step is data converted into book format, and the output is information on the publishing process.

[0660] (Application example 1)

[0661] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0662] Conventional story creation systems lack the functionality to automatically publish a book simply by inputting users' thoughts or fragments of a story. They also lack the ability to reflect feedback on the stories they create and regenerate them, or the ability to easily post them to content distribution platforms. Furthermore, they lack the information provided to simplify the publishing process, making it difficult for individuals to easily publish their own stories.

[0663] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0664] In this invention, the server includes: a means for a user to input thoughts or story fragments using an input device; a means for receiving data transmitted from the input device; a means for automatically analyzing the data received by the server means and converting it into structured data; an artificial intelligence means for automatically generating a story plot and detailed text based on the structured data; a means for converting the generated story into a publishing format; a means for providing information necessary for the publishing procedure using the publishing format; and a means for automatically posting the generated story to a content distribution platform. This allows users to easily input their own stories, which are then automatically analyzed, generated, regenerated, and distributed. Furthermore, the information necessary for the publishing procedure is also provided, allowing individuals to easily publish their own stories.

[0665] "User" refers to the person who uses the system to input their thoughts and story fragments.

[0666] An "input device" refers to a device such as a smartphone or computer that allows users to input text data.

[0667] "Data receiving means" refers to a mechanism by which the server receives text data sent from the input device.

[0668] "Server" refers to a computing device that processes input data and executes each means of the system.

[0669] "Structured data" refers to text data analyzed by a server that has been organized and systematized into a specific format.

[0670] "Artificial intelligence means" refers to technology that uses natural language processing technology and generative AI models to automatically generate story plots and detailed sentences.

[0671] "Publishing format" refers to the data format used to convert the generated story into a suitable format for a book or e-book.

[0672] "Information required for publishing procedures" refers to the specific information required to publish a book, such as how to obtain an ISBN, copyright registration procedures, and how to upload to a publishing platform.

[0673] "Content distribution platform" refers to an online service or website for publishing generated stories.

[0674] "Feedback" refers to corrections or requests for additions that a user provides to a generated story.

[0675] This invention relates to a system that allows users to easily input fragments of their own thoughts or stories and automatically compile them into a book. The system mainly consists of the following components:

[0676] Users use input devices such as smartphones and computers to input their daily events and ideas. For example, they can enter a note into a dedicated smartphone application saying, "Today, I talked to a stranger at a nice cafe."

[0677] The terminal sends the entered text data to the server, where it is securely transmitted over the Internet.

[0678] The server converts the received text data into structured data, which includes checking character encoding, handling line breaks and delimiters, etc.

[0679] The server uses natural language processing (NLP) to analyze the text and extract themes, characters, and scenes. For example, it extracts key elements of a story from keywords like "cafe" and "stranger."

[0680] The server uses a generative AI model based on the analysis results to automatically generate the plot and detailed sentences of the story. At this time, a generative AI model (e.g., GPT-3) is used to generate prompt sentences. For example, a plot such as "A conversation with a stranger you meet by chance in a cafe is the key to the story" is generated.

[0681] The first draft of the story is sent to the user, who can review it via email or in-app notifications, with any necessary corrections or additions requested. For example, feedback such as "be more specific in describing the stranger's appearance" is provided.

[0682] The server then recreates the story based on the feedback and creates a final draft, again using generative AI models to add specific dialogue and scenes.

[0683] The server converts the final draft into a publishing format and automatically posts it to a content distribution platform, where the resulting story is immediately made available online.

[0684] The server also provides information necessary for the publishing process, including specific steps on how to obtain an ISBN, register copyright, and upload to a publishing platform.

[0685] As a concrete example, consider a user writing down story ideas in a diary format:

[0686] 1. Input sentence: "Today, I talked to a stranger at a nice cafe."

[0687] 2. Prompt after analysis: "The conversation with a stranger you meet by chance in a cafe is the key to the story."

[0688] In this way, users can easily publish their ideas and share them on the content distribution platform, even if they do not have special knowledge or skills. This system provides a new means for individuals to easily generate and distribute creative content.

[0689] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0690] Step 1:

[0691] Users input fragments of thoughts or stories using an input device such as a smartphone or computer.

[0692] Input: Text data entered by the user (e.g., "Today, I spoke with a stranger at a nice cafe.")

[0693] Output: The input text data

[0694] Step 2:

[0695] The terminal transmits the input text data to the server.

[0696] Input: Text data entered in step 1

[0697] Output: Text data sent to the server (sent over the internet)

[0698] Step 3:

[0699] The server converts the received text data into structured data.

[0700] Input: Text data sent from the terminal

[0701] Data processing: Check character encoding, process line breaks and delimiters

[0702] Output: Structured data (e.g., JSON format data)

[0703] Step 4:

[0704] The server analyzes the text using natural language processing (NLP) technology to extract themes, characters, and scenes.

[0705] Input: Structured data from step 3

[0706] Data Computation: Using NLP techniques to analyze text and extract themes, characters, and scenes

[0707] Output: Analysis results (e.g., theme "cafe", character "stranger")

[0708] Step 5:

[0709] The server uses a generative AI model based on the analysis results to automatically generate the story plot and detailed text.

[0710] Input: Analysis results obtained in step 4

[0711] Data computation: Generative AI models (e.g., GPT-3) are used to generate prompts and create story outlines and detailed sentences.

[0712] Output: First draft of the story (e.g., "A conversation with a stranger I met by chance in a cafe is key to the story.")

[0713] Step 6:

[0714] The server sends the generated first draft of the story to the user.

[0715] Input: First draft of the story generated in step 5

[0716] Output: The first draft sent to the user (via email or in-app notification)

[0717] Step 7:

[0718] The user checks the first draft and sends any necessary corrections or requests for additions as feedback via the terminal.

[0719] Input: User feedback (e.g., "Please be more specific in describing strangers' appearances")

[0720] Output: Feedback text

[0721] Step 8:

[0722] The terminal sends the feedback text to the server.

[0723] Input: User feedback text

[0724] Output: Feedback text sent to the server (sent over the internet)

[0725] Step 9:

[0726] The server then recreates the story, incorporating the feedback.

[0727] Input: Feedback text and first draft

[0728] Data Computation: Using Generative AI Models to Regenerate Narratives Reflecting Feedback

[0729] Output: Final draft (e.g. "The story now includes a description of the stranger's appearance")

[0730] Step 10:

[0731] The server converts the final draft into a publishing format and automatically posts it to a content distribution platform.

[0732] Input: Final draft

[0733] Data processing: converting to publishing formats (e.g. PDF, ePub) and posting to content distribution platforms

[0734] Output: Final version posted to distribution platform

[0735] Step 11:

[0736] The server provides the information necessary for the publishing process.

[0737] Input: Final manuscript converted to publication format

[0738] Output: Specific information on how to obtain an ISBN, copyright registration procedures, and how to upload to publishing platforms.

[0739] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0740] This invention is a system in which users input fragments of their own thoughts or stories, and the text data is automatically analyzed, structured, and published as a book. The system also incorporates an emotion engine that analyzes the user's emotions, which also influences the mood and tone of the story. The specific configuration of the system and the program processing are explained below.

[0741] 1. User input:

[0742] A user inputs a thought or fragment of a story using an input device (such as a smartphone or PC). Specifically, the user inputs a note into the application, such as "Today, I talked to a stranger at a nice cafe."

[0743] 2. Data transmission by the device:

[0744] The terminal transmits the input text data to the server. Each time the user completes input, the terminal sends the text data to the server via the Internet.

[0745] 3. Data reception and structuring by the server:

[0746] The server converts the received text data into structured data and organizes it into the appropriate format, which includes checking character encoding, normalizing line breaks and delimiters, and removing unnecessary whitespace.

[0747] 4. Server-based sentiment analysis:

[0748] The server uses an emotion engine to analyze the user's emotion from the input text, for example, extracting "positive" emotion from the text "nice cafe."

[0749] 5. AI analysis and story generation by the server:

[0750] The server uses AI algorithms (including NLP technology) to analyze the input data for themes, themes, character traits, and scenes, and generates a story with the appropriate mood and tone based on the emotional information obtained from the emotion engine.

[0751] 6. Server-generated first draft:

[0752] The server uses a plot generation engine to create a story outline based on the analysis results, and automatically generates text including detailed scene descriptions and dialogue using a language model (e.g., GPT).

[0753] 7. Server sends first draft and requests confirmation:

[0754] The server sends the created draft to the user for confirmation. The user receives a request to review the draft via email or in-app notification and confirms the content.

[0755] 8. User Feedback:

[0756] The user checks the content and requests corrections or additions via the terminal. For example, they input feedback such as "I want Character A to be portrayed as more courageous."

[0757] 9. Sending Feedback via Device:

[0758] The terminal sends the feedback text to the server.

[0759] 10. Server Regeneration:

[0760] The server analyzes the feedback and uses an emotion engine to parse the intent of the feedback and recreate the story.

[0761] 11. Final Draft by Server:

[0762] The server creates a final draft based on the regenerated story and converts it into a book format, adjusting the font size and page layout, and finally converting it into PDF or ePub format.

[0763] 12. Server Providing Publishing Information:

[0764] The server organizes and provides users with information necessary for publishing, such as how to obtain an ISBN, copyright registration procedures, and how to upload to a publishing platform.

[0765] Specific examples

[0766] Usage example: A user writes down story ideas in a diary format

[0767] 1. A user uses a terminal to type a note such as, "Today, I talked to a stranger at a nice cafe."

[0768] 2. The device sends this memo to the server.

[0769] 3. The server analyzes the keywords "cafe" and "stranger" and extracts themes and settings.

[0770] 4. The server extracts "positive emotions" based on the analysis results and the emotion engine, and sets the tone of the story.

[0771] 5. The server generates a plot that states, "The conversation between the protagonist and a stranger he meets by chance in a cafe is the key to the story."

[0772] 6. The server creates a first draft including detailed scene descriptions based on the generated plot and sends it to the user.

[0773] 7. A user submits a correction request asking for more specific descriptions of strangers' appearances.

[0774] 8. The server uses an emotion engine to analyze the intent of the feedback, add detailed descriptions of the appearance, and create the final draft.

[0775] 9. The server organizes the final draft, converts it into book format, and then guides the user through procedures such as obtaining an ISBN and registering copyright.

[0776] This allows users to easily turn their ideas into books, even if they do not have any special knowledge or skills. The introduction of an emotion engine also optimizes the tone and atmosphere of the story, resulting in a more unique book.

[0777] The processing flow will be explained below.

[0778] Step 1:

[0779] The user inputs thoughts or story fragments using an input device.

[0780] Specifically, the user enters the text "Today, I talked to a stranger at a nice cafe" into a dedicated application on their smartphone or computer.

[0781] Step 2:

[0782] The terminal transmits the input text data to the server.

[0783] Once the user has completed the input, the device transmits the text data to a server in real time via the Internet.

[0784] Step 3:

[0785] The server converts the received text data into structured data and organizes it into an appropriate format.

[0786] The server checks the character encoding, normalizes line breaks and delimiters, removes unnecessary whitespace, etc.

[0787] Step 4:

[0788] The server uses an emotion engine to analyze the user's emotion from the input text.

[0789] For example, extract positive emotions from the phrase "nice cafe."

[0790] Step 5:

[0791] The server analyzes the text content using AI algorithms.

[0792] The server uses natural language processing (NLP) techniques to extract themes, themes, character traits, and scenes from the text.

[0793] Step 6:

[0794] The server generates a story plot and detailed text based on the analysis results and emotional information.

[0795] The server uses a plot generation engine to create a story outline based on the extracted themes and character settings, and then automatically generates detailed scene descriptions and dialogue using a language model (e.g., GPT).

[0796] Step 7:

[0797] The server sends the created draft to the user for confirmation.

[0798] The server will send the user a request to review the first draft via email or in-app notification.

[0799] Step 8:

[0800] The user checks the content and requests corrections or additions via the terminal.

[0801] Users read the first draft and enter feedback such as "I would like Character A to be portrayed as more courageous."

[0802] Step 9:

[0803] The terminal sends the feedback text to the server.

[0804] Any corrections or additions requested by the user are sent from the terminal to the server.

[0805] Step 10:

[0806] The server analyzes the feedback and uses an emotion engine to parse the intent of the feedback.

[0807] The server performs sentiment analysis on the feedback text to understand exactly what changes the user wants to make.

[0808] Step 11:

[0809] The server then recreates the story, incorporating the feedback.

[0810] The server detects the corrections and regenerates the text based on the feedback to produce the final draft.

[0811] Step 12:

[0812] The server converts the final draft into book format.

[0813] The server adjusts the font size and page layout, and finally converts it into PDF or ePub format.

[0814] Step 13:

[0815] The server organizes the information necessary for the publishing procedure and provides it to the user.

[0816] The server compiles information such as how to obtain an ISBN, copyright registration procedures, and how to upload to a publishing platform and presents it to the user.

[0817] Example 2

[0818] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0819] In today's world, many users are seeking an easy way to publish their thoughts and stories, but conventional systems require specialized knowledge and skills, making this difficult. Furthermore, there was a lack of means to properly optimize the atmosphere and tone of a story, making it difficult to reflect the user's intended emotions. Furthermore, the lack of flexibility in regenerating the story based on user feedback made it difficult to create a highly polished work.

[0820] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for automatically analyzing received data and converting it into structured data, artificial intelligence means for automatically generating a story plot and detailed sentences based on the structured data, means for optimizing the mood and tone of the generated story using a sentiment analysis engine, and means for receiving feedback from users and analyzing the feedback to regenerate the story. This makes it possible for users without specialized knowledge or skills to easily publish their own ideas and stories, optimize the mood and tone using a sentiment analysis engine, and regenerate a story that reflects the feedback.

[0821] "User" refers to an individual who uses the system to input their thoughts and story fragments.

[0822] "Input device" refers to a device such as a smartphone, PC, or tablet that allows a user to input text data.

[0823] A "server" refers to a computer system that receives data sent from an input device and performs processes such as analysis, data conversion, and story generation.

[0824] "Structured data" refers to data that has been converted from received text data into a regular format, and is organized to make it easier to analyze and process.

[0825] "Artificial intelligence means" refers to algorithms and technologies for automatically generating story plots and detailed text based on received structured data.

[0826] "Publishing format" refers to the standards and templates for converting the generated story into a format that can be published as a book (e.g., PDF or ePub format).

[0827] An "emotion analysis engine" refers to technology or software that analyzes emotions from text data and optimizes the mood and tone of the data.

[0828] "Feedback" refers to opinions such as requests for corrections or additions made by users to the generated story.

[0829] "Regeneration" refers to the process of reflecting on the feedback received and regenerating the story.

[0830] "Plot" refers to the outline or structure of a story, and is an outline that includes the overall flow of the story and major events.

[0831] This invention is a system that allows users to input fragments of their own thoughts or stories, analyzes and structures the text data, and then publishes it as a book. The system also includes an emotion engine that analyzes the user's emotions and a function that regenerates the story based on the feedback. The specific configuration of the system and the program processing are described below.

[0832] Hardware and Software

[0833] Hardware used: smartphone, PC, tablet (user input device), server (computing device for data analysis and story generation)

[0834] Software used: Application software (UI for user input), server software (data reception, analysis, generation, and transmission), sentiment analysis engine, AI algorithm (including natural language processing technology)

[0835] Program processing flow

[0836] User input: A user uses a smartphone or computer application to input a thought or fragment of a story, for example, "Today I spoke with a stranger in a nice cafe."

[0837] Sending data from the device: The device sends the text data entered to the server using an HTTP POST request.

[0838] Server data reception and structuring: The text data received by the server is taken and structured in JSON format. Character encoding is checked, line breaks and delimiters are normalized, and unnecessary whitespace is removed.

[0839] Sentiment analysis: The server uses an emotion engine to analyze emotions from text data. For example, it extracts "positive emotions" from the phrase "nice cafe." Sentiment analysis uses an emotion classification API.

[0840] AI analysis and story generation: The server uses natural language processing technology (such as the BERT model) to analyze the text data for themes, character traits, and scenes. Based on the analysis results, it sets the mood and tone of the story and generates a plot.

[0841] First Draft Creation: The server uses a plot generation engine to automatically generate a first draft including detailed scene descriptions and dialogue. It also uses a language model (e.g., GPT-3 or GPT-4) to generate sentences.

[0842] Sending and reviewing the first draft: The server sends the first draft to the user for review. The user receives a link to the first draft via email or in-app notification.

[0843] User feedback: The user reviews the content and inputs requests for corrections or additions. For example, they can send feedback such as, "Please be more specific in describing the stranger's appearance."

[0844] Regeneration: The server receives and analyzes the feedback, and generates a new story using the regeneration algorithm. The emotion engine analyzes the intent of the feedback.

[0845] Final drafting and conversion to publication format: The server creates a final draft based on the regenerated story and converts it to a publication format (PDF or ePub). It then cleans up the final draft and adjusts the font size and page layout.

[0846] Providing information on publishing procedures: The server provides users with the necessary information, such as how to obtain an ISBN, how to register copyright, and how to upload to a publishing platform.

[0847] Examples and prompts

[0848] Usage example: A user writes down story ideas in diary format

[0849] 1. A user uses a terminal to type a note such as, "Today, I talked to a stranger at a nice cafe."

[0850] 2. The device sends this memo to the server.

[0851] 3. The server analyzes the keywords "cafe" and "stranger" and extracts themes and settings.

[0852] 4. The server extracts "positive emotions" based on the analysis results and the emotion engine, and sets the tone of the story.

[0853] 5. The server generates a plot in which the conversation between the protagonist and a stranger he meets by chance in a cafe becomes the key to the story.

[0854] 6. The server creates a first draft based on the generated plot, including detailed scene descriptions, and sends it to the user.

[0855] 7. A user submits a correction request asking for more specific descriptions of strangers' appearances.

[0856] 8. The server uses an emotion engine to analyze the intent of the feedback, add detailed descriptions of the appearance, and create the final draft.

[0857] 9. The server organizes the final draft, converts it into book format, and then guides the user through procedures such as obtaining an ISBN and registering copyright.

[0858] Example prompt sentence:

[0859] "Today I had a conversation with a stranger at a nice cafe. Please generate a story that includes emotions."

[0860] This invention allows users to easily turn their ideas into books without any special knowledge or skills. By introducing an emotion engine, the tone and atmosphere of the story are optimized, resulting in a more unique book.

[0861] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0862] Step 1:

[0863] User-entered text

[0864] A user uses a smartphone or PC application to input a thought or fragment of a story, for example, "Today I spoke with a stranger at a nice cafe." The input data is sent from the device's input device to the application software.

[0865] input:

[0866] User-entered text (e.g., "I talked to a stranger at a nice cafe today.")

[0867] output:

[0868] Input text data stored on the device

[0869] Step 2:

[0870] Data transmission by the terminal

[0871] The device sends the text data entered by the user to the server. The device sends the data to the server over the Internet using an HTTP POST request.

[0872] input:

[0873] Input text data stored on the device

[0874] output:

[0875] Text data transferred to the server

[0876] Step 3:

[0877] Data reception and structuring by the server

[0878] The server receives the text data, parses it, and structures it. It checks the character encoding of the received data, normalizes line breaks and delimiters, and removes unnecessary whitespace. The structured data is then converted into a format such as JSON.

[0879] input:

[0880] Text data transferred to the server

[0881] output:

[0882] Structured data (e.g., JSON format)

[0883] Specific behavior:

[0884] Check the character encoding

[0885] Line break / delimiter normalization

[0886] Removing unnecessary whitespace

[0887] Conversion to JSON format

[0888] Step 4:

[0889] Emotion analysis by server

[0890] The server uses a sentiment analysis engine to analyze emotions from text data. For example, it extracts "positive emotion" from the phrase "nice cafe." It uses a sentiment classification API for sentiment analysis.

[0891] input:

[0892] Structured data

[0893] output:

[0894] Sentiment analysis results (e.g., "positive")

[0895] Specific behavior:

[0896] Call the sentiment classification API to perform sentiment analysis.

[0897] Add sentiment analysis results to structured data

[0898] Step 5:

[0899] AI analysis and story generation by server

[0900] The server uses natural language processing technology (e.g., the BERT model) to analyze the text data for themes, character traits, and scenes. Based on the analysis results, it sets the mood and tone of the story and generates a plot. The generation algorithm uses a language model (e.g., GPT-3, GPT-4).

[0901] input:

[0902] Structured data and sentiment analysis results

[0903] output:

[0904] Generated plot and narrative tone setting

[0905] Specific behavior:

[0906] Theme, character traits, and scene analysis

[0907] Setting the mood and tone

[0908] Plot generation using language models

[0909] Step 6:

[0910] Server-based first draft creation

[0911] The server uses a plot generation engine to automatically generate a first draft based on the created plot, including detailed scene descriptions and dialogue, and generates sentences using a language model (e.g., GPT-3 or GPT-4).

[0912] input:

[0913] Generated plot and narrative tone setting

[0914] output:

[0915] First draft text data

[0916] Specific behavior:

[0917] Start the plot generation engine

[0918] Automatically generate detailed scene descriptions and dialogue using language models

[0919] Step 7:

[0920] Server sends first draft and requests confirmation

[0921] The server sends the first draft to the user for review, and sends the user a link to the first draft via email or in-app notification.

[0922] input:

[0923] First draft text data

[0924] output:

[0925] Link to the first draft sent to the user

[0926] Specific behavior:

[0927] Send the first draft text data via email or in-app notification

[0928] Step 8:

[0929] User Feedback

[0930] The user checks the content and inputs requests for corrections or additions, such as "Please be more specific in describing the appearance of strangers."

[0931] input:

[0932] User feedback text

[0933] output:

[0934] Feedback text data

[0935] Specific behavior:

[0936] Enter feedback text through the application

[0937] Step 9:

[0938] Sending feedback via device

[0939] The device sends the feedback text to the server using an HTTP POST request or WebSocket.

[0940] input:

[0941] Feedback Text

[0942] output:

[0943] Feedback text forwarded to the server

[0944] Specific behavior:

[0945] Send data via HTTP POST request or WebSocket

[0946] Step 10:

[0947] Server Regeneration

[0948] The server receives and analyzes the feedback, and generates a new story using a regeneration algorithm. The emotion engine analyzes the intent of the feedback.

[0949] input:

[0950] Feedback and existing story data

[0951] output:

[0952] Regenerated narrative text

[0953] Specific behavior:

[0954] Analyzing feedback content

[0955] Analyze intent using an emotion engine

[0956] Regenerating stories using regeneration algorithms

[0957] Step 11:

[0958] Final draft creation and conversion to publication format by the server

[0959] The server creates a final draft based on the regenerated story and converts it into a publishing format (PDF or ePub), adjusting the font size and page layout.

[0960] input:

[0961] Regenerated narrative text

[0962] output:

[0963] Publication format of the final manuscript (PDF or ePub)

[0964] Specific behavior:

[0965] Creating the final draft

[0966] Adjusting font size and page layout

[0967] Conversion to publishing format

[0968] Step 12:

[0969] Server provides publishing procedure information

[0970] The server provides users with all the necessary information, such as how to obtain an ISBN, how to register copyright, and how to upload to a publishing platform.

[0971] input:

[0972] Publication-formatted final manuscript

[0973] output:

[0974] Providing information necessary for publication procedures

[0975] Specific behavior:

[0976] Information on how to obtain an ISBN

[0977] Providing information on copyright registration procedures

[0978] Guidance on how to upload to publishing platforms

[0979] (Application example 2)

[0980] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0981] This invention relates to a system that automatically analyzes and structures the thoughts and story fragments entered by users on a daily basis to generate high-quality stories. In particular, by incorporating an emotion analysis function, the system can adjust the mood and tone of the story and regenerate it based on user feedback, enabling the provision of more personalized stories. Furthermore, by providing a function to share the generated stories on content distribution services, the system aims to promote the dissemination of creative content.

[0982] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for a user to input fragments of thoughts or stories using an input device, a means for receiving data transmitted from the input device, and a means for automatically analyzing the data received by the server means and converting it into structured data. This enables automatic analysis and structuring of text data.

[0983] The system further includes an artificial intelligence means for automatically generating a story plot and detailed text based on the structured data, a means for converting the generated story into a publishing format, a means for providing information required for publishing procedures using the publishing format, a means for receiving user feedback and regenerating the story while reflecting the feedback, a means for extracting emotional information from the user's input data using an emotion analysis engine and reflecting the emotional information in the story's mood and tone, and a means for sharing the generated story or essay on a content distribution service. This makes it possible to adjust the mood and tone through emotion analysis, regenerate the story while reflecting the feedback, and share content.

[0984] An "input device" is a device that allows a user to input text data, and includes smartphones, personal computers, etc.

[0985] The "server means" is a computer system for receiving and processing data sent from an input device.

[0986] "Structured data" is a data format that is created by analyzing input text data and organizing it based on certain rules.

[0987] "Artificial intelligence means" refers to technology that uses machine learning and natural language processing techniques to automatically generate story plots and detailed sentences.

[0988] A "publishing format" is a format in which the generated story is converted into a form that can be published in print or as an e-book.

[0989] An "emotion analysis engine" is a technology that extracts emotional information from text data entered by the user and reflects that emotional information in the tone and atmosphere of the story.

[0990] A "content distribution service" is a platform for sharing generated stories and essays with other users via the Internet.

[0991] "Feedback" refers to comments and suggestions that users give to the generated story for correction or improvement.

[0992] "Regeneration" is the process of regenerating an existing story based on feedback from users.

[0993] This invention is a system that automatically analyzes and structures the thoughts and story fragments entered by users on a daily basis, generating high-quality stories. In particular, it has an emotion analysis function that adjusts the mood and tone of the story and regenerates it based on user feedback. The system also has a function to share the generated stories and essays on a content distribution service.

[0994] First, a user inputs their thoughts or fragments of a story using an input device (such as a smartphone or PC). The text data sent from the input device is then sent over the Internet to a server. The server receives this data, automatically parses it, and converts it into structured data. This process includes checking character encoding, normalizing line breaks and delimiters, and removing unnecessary whitespace.

[0995] Based on the structured data, the server uses artificial intelligence tools (e.g., a generative AI model such as GPT-4) to automatically generate the plot and detailed text of the story. The server then uses a sentiment analysis engine (e.g., IBM Watson's sentiment analysis API) to extract emotional information from the text data and reflect it in the mood and tone of the story. The generated story is then converted into a publishing format (e.g., PDF or ePub).

[0996] The generated story can be shared on a content distribution service. The user reviews the generated story and sends feedback to the server for corrections and improvements as needed. The server then uses the sentiment analysis engine again to regenerate the story, incorporating the feedback. This process allows the user to complete a work that is more in line with their own intentions and emotions.

[0997] As a concrete example, consider the case where a user enters a note saying, "Today, the sunset was so beautiful, and I can't forget the moment I talked with my friend in the park." The server receives this text and provides the following prompt to the generative AI model:

[0998] "User input text: 'Today the sunset was so beautiful, and I can't forget the moment I talked with my friend in the park.' Based on this text, generate a short story with a positive tone. The themes are 'friendship' and 'beauty of nature.' Also, reflect the emotion 'happiness' as the result of the sentiment analysis engine."

[0999] Based on this prompt, the generative AI model automatically generates a story with detailed scene descriptions and dialogue, and then receives user feedback to regenerate the content, providing a more satisfying story.

[1000] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1001] Step 1:

[1002] The user inputs fragments of thoughts or stories using an input device (such as a smartphone or PC), for example, by entering text data such as "Today, the sunset was so beautiful, and I can't forget the moment I talked with my friend in the park."

[1003] Step 2:

[1004] The terminal (input device) sends the input text data to the server via the Internet. The input data is sent in JSON format, and the server receives this data.

[1005] Step 3:

[1006] The server converts the received text data into structured data, which includes checking character encoding, normalizing line breaks and delimiters, and removing unnecessary whitespace. The resulting structured data is then ready for further analysis.

[1007] Step 4:

[1008] The server uses a sentiment analysis engine to extract emotional information from the input text. For example, it uses IBM Watson's sentiment analysis API to extract "positive" emotions from keywords such as "beautiful," "spoke," and "unforgettable." The emotional information is reflected in the next AI analysis.

[1009] Step 5:

[1010] The server uses a generative AI model (e.g., GPT-4) to analyze the input data for theme, plot, and character settings. Based on the previously extracted emotional information, the server generates a story with the appropriate mood and tone. The generated text is saved as a first draft. Specifically, the generative AI model is given the following prompt:

[1011] "User input text: 'Today the sunset was so beautiful, and I can't forget the moment I talked with my friend in the park.' Based on this text, generate a short story with a positive tone. The themes are 'friendship' and 'beauty of nature.' Also, reflect the emotion 'happiness' as the result of the sentiment analysis engine."

[1012] Step 6:

[1013] The server sends the generated draft to the user for confirmation. The user receives the draft via email or in-app notification and checks the text.

[1014] Step 7:

[1015] The user checks the content and, if necessary, enters feedback for corrections or additions through the terminal. For example, the user can enter specific feedback such as "Please be more specific in describing the appearance of strangers."

[1016] Step 8:

[1017] The device sends the feedback text to the server. The feedback data is also sent in JSON format, and the server receives this data.

[1018] Step 9:

[1019] The server analyzes the feedback and uses an emotion engine to analyze the intent of the feedback. Based on the analysis results, the generative AI model is used again to regenerate the story. The newly generated story reflects the feedback.

[1020] Step 10:

[1021] The server converts the final generated story into a publishing format, adjusting the font size and page layout, and finally converting it into PDF or ePub format.

[1022] Step 11:

[1023] The server shares the generated stories and essays on a content distribution service, allowing users to share their stories with other users via the Internet.

[1024] Step 12:

[1025] By publishing the stories created by users on content distribution services and having them viewed by many users, the spread of creative content will be promoted.

[1026] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1027] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1028] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1029] [Third embodiment]

[1030] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1031] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1032] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1033] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1034] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1035] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1036] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1037] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1038] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1039] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1040] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1041] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1042] This invention is a system that allows users to input fragments of their own thoughts or stories and automatically compiles them into a book. The system includes the following main components:

[1043] 1. The user uses an input device (such as a smartphone or PC) to input fragments of everyday thoughts or stories. For example, the user might input something like, "I had a dream this morning. It was about a huge castle and mysterious creatures..." into a dedicated smartphone application.

[1044] 2. The terminal sends the entered text data to the server. Each time the user completes input, the text data is sent to the server via the Internet.

[1045] 3. The server converts the received text data into structured data and organizes it into the appropriate format, including checking character encoding and handling line breaks and delimiters.

[1046] 4. The server analyzes the content of the input text using AI algorithms, specifically natural language processing (NLP) techniques, to extract themes, themes, character traits, and scenes from the text.

[1047] 5. The server applies an automatic generation algorithm based on the analysis results to create a first draft. Using a plot generation engine, the server generates a story outline based on the extracted themes and character settings, and then uses a language model to automatically generate text including detailed scene descriptions and dialogue.

[1048] 6. The server sends the created draft to the user for review. The user can review the draft via email or in-app notification and make specific corrections or additions.

[1049] 7. The user checks the content of the first draft and makes any necessary corrections or additions via the terminal. For example, the user sends feedback such as, "I would like Character A to be portrayed as more courageous."

[1050] 8. The device sends the feedback text to the server.

[1051] 9. The server regenerates the story based on the feedback. The server detects the corrections and regenerates the text based on the user's requests to create the final version.

[1052] 10. The server converts the final draft into book format. The server adjusts the font size and page layout and converts it into the final PDF or ePub format.

[1053] 11. The server organizes the information necessary for the publishing process and provides it to the user. The server provides the user with specific instructions, such as how to obtain an ISBN, how to register copyright, and how to upload to a publishing platform.

[1054] Specific examples

[1055] Usage example: A user writes down story ideas in a diary format

[1056] 1. A user uses a terminal to type a note such as, "Today, I talked to a stranger at a nice cafe."

[1057] 2. The device sends this memo to the server.

[1058] 3. The server analyzes keywords such as "cafe" and "stranger" to extract themes and settings.

[1059] 4. Based on the extracted information, the server generates a plot in which the conversation between the protagonist and a stranger he meets by chance in a cafe becomes the key to the story.

[1060] 5. The server creates a first draft including detailed scene descriptions based on the generated plot and sends it to the user.

[1061] 6. The user submits a correction request asking for more specific descriptions of strangers' appearances.

[1062] 7. The server adds detailed descriptions of the appearance based on the requests and creates the final draft.

[1063] 8. The server organizes the final draft, converts it into book format, and guides the user through procedures such as obtaining an ISBN and registering copyright.

[1064] This allows users to easily publish their ideas in book form, even if they do not have any special knowledge or skills.

[1065] The processing flow will be explained below.

[1066] Step 1:

[1067] The user inputs thoughts or story fragments using an input device.

[1068] Specifically, the user enters text such as "I had a dream this morning. A huge castle and mysterious creatures..." into a dedicated application on their smartphone or computer.

[1069] Step 2:

[1070] The terminal transmits the input text data to the server.

[1071] Each time the user completes an input, the terminal transmits the text data to a server over the Internet in real time.

[1072] Step 3:

[1073] The server converts the received text data into structured data and organizes it into an appropriate format.

[1074] This includes checking character encoding, normalizing line breaks and delimiters, and removing unnecessary whitespace.

[1075] Step 4:

[1076] The server analyzes the content of the text entered using AI algorithms.

[1077] The server uses natural language processing (NLP) technology to extract elements such as themes, subject matter, character traits, and scenes from the text.

[1078] Step 5:

[1079] The server applies an automatic generation algorithm based on the analysis results to create a first draft.

[1080] The server uses a plot generation engine to generate a story outline based on the extracted themes and character settings, and automatically generates text including detailed scene descriptions and dialogue using language models such as Generative Pretrained Transformers (GPT).

[1081] Step 6:

[1082] The server sends the generated draft to the user for confirmation.

[1083] The server will send a request to the user to review the first draft via email or in-app notification, allowing the user to review it.

[1084] Step 7:

[1085] The user checks the content and requests corrections or additions via the terminal.

[1086] Users read the first draft and enter corrections and additional feedback, such as "I would like Character A to be portrayed as more courageous."

[1087] Step 8:

[1088] The terminal sends the feedback text to the server.

[1089] Any corrections or additions requested by the user are sent from the terminal to the server.

[1090] Step 9:

[1091] The server then recreates the story, incorporating the feedback.

[1092] The server detects the corrections and regenerates the text based on the user's feedback.

[1093] Step 10:

[1094] The server converts the regenerated final draft into book format.

[1095] The server adjusts the font size and page layout, and finally converts it into PDF or ePub format.

[1096] Step 11:

[1097] The server organizes the information necessary for the publishing procedure and provides it to the user.

[1098] The server provides users with detailed instructions on how to obtain an ISBN, how to register copyright, and how to upload to a publishing platform.

[1099] Example 1

[1100] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1101] Until now, there has been a lack of efficient and easy ways for users to turn their thoughts and story fragments into books. Manually organizing and editing text is time-consuming, labor-intensive, and requires specialized knowledge, making it a significant barrier for many users. Therefore, there is a need for an efficient and automated system that allows users to easily turn their ideas into books and publish them.

[1102] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1103] In this invention, the server includes means for automatically analyzing text data and converting it into structured data, artificial intelligence means for automatically generating a story plot and detailed text based on the structured data, and means for receiving feedback from users and regenerating the story by reflecting that feedback. This allows users to efficiently turn their ideas into books and easily publish them without specialized knowledge.

[1104] An "input device" is a device that allows a user to input thoughts or fragments of a story, and specifically includes terminals such as smartphones and personal computers.

[1105] The term "server means" refers to a computer system for receiving and processing data sent from an input device.

[1106] The "means for converting into structured data" is a system that has the function of analyzing the text data received by the server means, checking the character encoding, processing line breaks and delimiters, and organizing the data.

[1107] "Artificial intelligence means" refers to AI algorithms and models that automatically generate story plots and detailed text based on structured data.

[1108] The "means for converting into a publishing format" is a system that has the function of adjusting the font size and page layout and converting the generated story into PDF or ePub format in order to publish it as a book.

[1109] "Means for providing information necessary for publishing procedures" refers to a function that guides users through specific steps such as how to obtain an ISBN, copyright registration procedures, and how to upload to a publishing platform.

[1110] "Natural language processing technology" refers to technology for analyzing input data for themes, structure, and character settings, and includes the use of NLP libraries and algorithms.

[1111] "Means for regenerating a story based on feedback" refers to the function of the server means to receive feedback from users and modify and regenerate the text of the story based on the content of that feedback.

[1112] MODE FOR CARRYING OUT THE INVENTION

[1113] This invention is a system that allows users to input fragments of their own thoughts or stories and automatically compiles them into a book. The system includes the following main components:

[1114] System Components

[1115] 1. Input Devices

[1116] The user uses an input device such as a smartphone or PC to input fragments of everyday thoughts or stories. Specifically, a dedicated application or web interface is used. For example, the user might input, "I had a dream this morning. It was about a huge castle and mysterious creatures..."

[1117] 2. Server Means

[1118] The terminal receives the text data entered by the user and sends it to the server via the Internet using an HTTP POST request.

[1119] The server converts the received text data into structured data by first checking the character encoding, then processing line breaks and delimiters, converting it to JSON format, and escaping special characters.

[1120] The server analyzes the text content using natural language processing (NLP) techniques, such as SpaCy and NLTK, to extract themes, themes, character traits, and scenes from the text.

[1121] Based on the analysis results, the server applies an automated generation algorithm to create a first draft. It uses a plot generation engine to generate a story outline based on the extracted themes and character settings. It then uses a language model (such as OpenAI's GPT-3) to generate sentences with detailed scene descriptions and dialogue.

[1122] The server sends the created draft to the user, who receives it via email or in-app notification.

[1123] The server receives feedback from the user, regenerates the story based on that feedback, and creates a final draft from the regenerated text.

[1124] The server converts the final manuscript into a publication format, adjusting the font size and page layout, and converting it into the final PDF or ePub format.

[1125] The server organizes and provides users with the information necessary for the publishing process, including how to obtain an ISBN, how to register copyright, and how to upload to a publishing platform.

[1126] Specific examples

[1127] Usage example: A user writes down story ideas in a diary format

[1128] 1. A user uses a terminal to type a note such as, "Today, I talked to a stranger at a nice cafe."

[1129] 2. The device sends this memo to the server.

[1130] 3. The server converts the received text into JSON format and escapes special characters.

[1131] 4. The server uses natural language processing technology to analyze the keywords "cafe" and "stranger" and extract themes and settings.

[1132] 5. Based on the extracted information, the server generates a plot, such as "The conversation between the protagonist and a stranger he meets by chance in a cafe will be key to the story." Using a generative AI model, the server creates a first draft that includes detailed scene descriptions and dialogue.

[1133] 6. The server sends the first draft to the user.

[1134] 7. The user reads the first draft and inputs a request for revisions on the terminal, saying, "Please be more specific in describing the stranger's appearance."

[1135] 8. The terminal resends the correction request to the server.

[1136] 9. The server analyzes the corrections and creates a final draft with the regenerated text.

[1137] 10. The server converts the final draft into PDF or ePub format and provides the book format to the user.

[1138] 11. The server guides the user on how to obtain an ISBN and register copyright.

[1139] In this way, the system allows users to easily turn their ideas into books and publish them without any special knowledge or skills.

[1140] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1141] Step 1:

[1142] The user inputs everyday thoughts or fragments of stories using an input device such as a smartphone or PC. The input data is in text format, such as "I had a dream this morning. A huge castle and mysterious creatures..." This input data is saved in text format on the device.

[1143] Step 2:

[1144] The terminal receives the input text data and sends it to the server via the Internet. Specifically, it sends the input text to the server using an HTTP POST request. The input of this step is the text data entered by the user, and the output is the text data sent to the server.

[1145] Step 3:

[1146] The server converts the received text data into structured data. First, the server checks the character encoding of the text data, then processes line breaks and delimiters. Specifically, it converts the text data into JSON format and escapes special characters. The input of this step is the text data received by the server, and the output is structured data converted into JSON format.

[1147] Step 4:

[1148] The server uses structured data to analyze the content of the text using natural language processing (NLP) techniques. The server uses an NLP library (e.g., SpaCy or NLTK) to extract themes, themes, character traits, and scenes from the text. The input for this step is structured data, and the output is the analysis results.

[1149] Step 5:

[1150] The server applies an automated generation algorithm based on the analysis results to create a first draft. It uses a plot generation engine to generate a story outline based on the extracted themes and character settings. It then uses a language model (e.g., OpenAI's GPT-3) to generate text with detailed scene descriptions and dialogue. The input for this step is the analysis results, and the output is the first draft text.

[1151] Step 6:

[1152] The server sends the created draft to the user. The server sends the draft to the user via email or in-app notification. The input of this step is the draft text, and the output is the draft sent to the user.

[1153] Step 7:

[1154] The user checks the content of the first draft and inputs any necessary corrections or requests for additions via the terminal. For example, the user may input feedback such as "I would like Character A to be portrayed as more courageous." The input for this step is the first draft that the user checked, and the resulting feedback. The output is the feedback text saved on the terminal.

[1155] Step 8:

[1156] The terminal sends the feedback text to the server. Specifically, it uses an HTTP POST request to send the feedback text requesting correction to the server. The input of this step is the feedback text received from the user, and the output is the feedback text sent to the server.

[1157] Step 9:

[1158] The server regenerates the story based on the feedback. The server detects corrections, regenerates the text based on the user's requests, and creates the final draft. The input to this step is the feedback text received by the server, and the output is the regenerated final draft text.

[1159] Step 10:

[1160] The server converts the final draft into book format, adjusting font size and page layout, and converting it into PDF or ePub format. The input for this step is the final draft text, and the output is the converted data in book format.

[1161] Step 11:

[1162] The server organizes the information necessary for the publishing process and provides it to the user. Specifically, it provides information on how to obtain an ISBN, how to register copyright, how to upload to a publishing platform, etc. The input for this step is data converted into book format, and the output is information on the publishing process.

[1163] (Application example 1)

[1164] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1165] Conventional story creation systems lack the functionality to automatically publish a book simply by inputting users' thoughts or fragments of a story. They also lack the ability to reflect feedback on the stories they create and regenerate them, or the ability to easily post them to content distribution platforms. Furthermore, they lack the information provided to simplify the publishing process, making it difficult for individuals to easily publish their own stories.

[1166] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1167] In this invention, the server includes: a means for a user to input thoughts or story fragments using an input device; a means for receiving data transmitted from the input device; a means for automatically analyzing the data received by the server means and converting it into structured data; an artificial intelligence means for automatically generating a story plot and detailed text based on the structured data; a means for converting the generated story into a publishing format; a means for providing information necessary for the publishing procedure using the publishing format; and a means for automatically posting the generated story to a content distribution platform. This allows users to easily input their own stories, which are then automatically analyzed, generated, regenerated, and distributed. Furthermore, the information necessary for the publishing procedure is also provided, allowing individuals to easily publish their own stories.

[1168] "User" refers to the person who uses the system to input their thoughts and story fragments.

[1169] An "input device" refers to a device such as a smartphone or computer that allows users to input text data.

[1170] "Data receiving means" refers to a mechanism by which the server receives text data sent from the input device.

[1171] "Server" refers to a computing device that processes input data and executes each means of the system.

[1172] "Structured data" refers to text data analyzed by a server that has been organized and systematized into a specific format.

[1173] "Artificial intelligence means" refers to technology that uses natural language processing technology and generative AI models to automatically generate story plots and detailed sentences.

[1174] "Publishing format" refers to the data format used to convert the generated story into a suitable format for a book or e-book.

[1175] "Information required for publishing procedures" refers to the specific information required to publish a book, such as how to obtain an ISBN, copyright registration procedures, and how to upload to a publishing platform.

[1176] "Content distribution platform" refers to an online service or website for publishing generated stories.

[1177] "Feedback" refers to corrections or requests for additions that a user provides to a generated story.

[1178] This invention relates to a system that allows users to easily input fragments of their own thoughts or stories and automatically compile them into a book. The system mainly consists of the following components:

[1179] Users use input devices such as smartphones and computers to input their daily events and ideas. For example, they can enter a note into a dedicated smartphone application saying, "Today, I talked to a stranger at a nice cafe."

[1180] The terminal sends the entered text data to the server, where it is securely transmitted over the Internet.

[1181] The server converts the received text data into structured data, which includes checking character encoding, handling line breaks and delimiters, etc.

[1182] The server uses natural language processing (NLP) to analyze the text and extract themes, characters, and scenes. For example, it extracts key elements of a story from keywords like "cafe" and "stranger."

[1183] The server uses a generative AI model based on the analysis results to automatically generate the plot and detailed sentences of the story. At this time, a generative AI model (e.g., GPT-3) is used to generate prompt sentences. For example, a plot such as "A conversation with a stranger you meet by chance in a cafe is the key to the story" is generated.

[1184] The first draft of the story is sent to the user, who can review it via email or in-app notifications, with any necessary corrections or additions requested. For example, feedback such as "be more specific in describing the stranger's appearance" is provided.

[1185] The server then recreates the story based on the feedback and creates a final draft, again using generative AI models to add specific dialogue and scenes.

[1186] The server converts the final draft into a publishing format and automatically posts it to a content distribution platform, where the resulting story is immediately made available online.

[1187] The server also provides information necessary for the publishing process, including specific steps on how to obtain an ISBN, register copyright, and upload to a publishing platform.

[1188] As a concrete example, consider a user writing down story ideas in a diary format:

[1189] 1. Input sentence: "Today, I talked to a stranger at a nice cafe."

[1190] 2. Prompt after analysis: "The conversation with a stranger you meet by chance in a cafe is the key to the story."

[1191] In this way, users can easily publish their ideas and share them on the content distribution platform, even if they do not have special knowledge or skills. This system provides a new means for individuals to easily generate and distribute creative content.

[1192] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1193] Step 1:

[1194] Users input fragments of thoughts or stories using an input device such as a smartphone or computer.

[1195] Input: Text data entered by the user (e.g., "Today, I spoke with a stranger at a nice cafe.")

[1196] Output: The input text data

[1197] Step 2:

[1198] The terminal transmits the input text data to the server.

[1199] Input: Text data entered in step 1

[1200] Output: Text data sent to the server (sent over the internet)

[1201] Step 3:

[1202] The server converts the received text data into structured data.

[1203] Input: Text data sent from the terminal

[1204] Data processing: Check character encoding, process line breaks and delimiters

[1205] Output: Structured data (e.g., JSON format data)

[1206] Step 4:

[1207] The server analyzes the text using natural language processing (NLP) technology to extract themes, characters, and scenes.

[1208] Input: Structured data from step 3

[1209] Data Computation: Using NLP techniques to analyze text and extract themes, characters, and scenes

[1210] Output: Analysis results (e.g., theme "cafe", character "stranger")

[1211] Step 5:

[1212] The server uses a generative AI model based on the analysis results to automatically generate the story plot and detailed text.

[1213] Input: Analysis results obtained in step 4

[1214] Data computation: Generative AI models (e.g., GPT-3) are used to generate prompts and create story outlines and detailed sentences.

[1215] Output: First draft of the story (e.g., "A conversation with a stranger I met by chance in a cafe is key to the story.")

[1216] Step 6:

[1217] The server sends the generated first draft of the story to the user.

[1218] Input: First draft of the story generated in step 5

[1219] Output: The first draft sent to the user (via email or in-app notification)

[1220] Step 7:

[1221] The user checks the first draft and sends any necessary corrections or requests for additions as feedback via the terminal.

[1222] Input: User feedback (e.g., "Please be more specific in describing strangers' appearances")

[1223] Output: Feedback text

[1224] Step 8:

[1225] The terminal sends the feedback text to the server.

[1226] Input: User feedback text

[1227] Output: Feedback text sent to the server (sent over the internet)

[1228] Step 9:

[1229] The server then recreates the story, incorporating the feedback.

[1230] Input: Feedback text and first draft

[1231] Data Computation: Using Generative AI Models to Regenerate Narratives Reflecting Feedback

[1232] Output: Final draft (e.g. "The story now includes a description of the stranger's appearance")

[1233] Step 10:

[1234] The server converts the final draft into a publishing format and automatically posts it to a content distribution platform.

[1235] Input: Final draft

[1236] Data processing: converting to publishing formats (e.g. PDF, ePub) and posting to content distribution platforms

[1237] Output: Final version posted to distribution platform

[1238] Step 11:

[1239] The server provides the information necessary for the publishing process.

[1240] Input: Final manuscript converted to publication format

[1241] Output: Specific information on how to obtain an ISBN, copyright registration procedures, and how to upload to publishing platforms.

[1242] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1243] This invention is a system in which users input fragments of their own thoughts or stories, and the text data is automatically analyzed, structured, and published as a book. The system also incorporates an emotion engine that analyzes the user's emotions, which also influences the mood and tone of the story. The specific configuration of the system and the program processing are explained below.

[1244] 1. User input:

[1245] A user inputs a thought or fragment of a story using an input device (such as a smartphone or PC). Specifically, the user inputs a note into the application, such as "Today, I talked to a stranger at a nice cafe."

[1246] 2. Data transmission by the device:

[1247] The terminal transmits the input text data to the server. Each time the user completes input, the terminal sends the text data to the server via the Internet.

[1248] 3. Data reception and structuring by the server:

[1249] The server converts the received text data into structured data and organizes it into the appropriate format, which includes checking character encoding, normalizing line breaks and delimiters, and removing unnecessary whitespace.

[1250] 4. Server-based sentiment analysis:

[1251] The server uses an emotion engine to analyze the user's emotion from the input text, for example, extracting "positive" emotion from the text "nice cafe."

[1252] 5. AI analysis and story generation by the server:

[1253] The server uses AI algorithms (including NLP technology) to analyze the input data for themes, themes, character traits, and scenes, and generates a story with the appropriate mood and tone based on the emotional information obtained from the emotion engine.

[1254] 6. Server-generated first draft:

[1255] The server uses a plot generation engine to create a story outline based on the analysis results, and automatically generates text including detailed scene descriptions and dialogue using a language model (e.g., GPT).

[1256] 7. Server sends first draft and requests confirmation:

[1257] The server sends the created draft to the user for confirmation. The user receives a request to review the draft via email or in-app notification and confirms the content.

[1258] 8. User Feedback:

[1259] The user checks the content and requests corrections or additions via the terminal. For example, they input feedback such as "I want Character A to be portrayed as more courageous."

[1260] 9. Sending Feedback via Device:

[1261] The terminal sends the feedback text to the server.

[1262] 10. Server Regeneration:

[1263] The server analyzes the feedback and uses an emotion engine to parse the intent of the feedback and recreate the story.

[1264] 11. Final Draft by Server:

[1265] The server creates a final draft based on the regenerated story and converts it into a book format, adjusting the font size and page layout, and finally converting it into PDF or ePub format.

[1266] 12. Server Providing Publishing Information:

[1267] The server organizes and provides users with information necessary for publishing, such as how to obtain an ISBN, copyright registration procedures, and how to upload to a publishing platform.

[1268] Specific examples

[1269] Usage example: A user writes down story ideas in a diary format

[1270] 1. A user uses a terminal to type a note such as, "Today, I talked to a stranger at a nice cafe."

[1271] 2. The device sends this memo to the server.

[1272] 3. The server analyzes the keywords "cafe" and "stranger" and extracts themes and settings.

[1273] 4. The server extracts "positive emotions" based on the analysis results and the emotion engine, and sets the tone of the story.

[1274] 5. The server generates a plot that states, "The conversation between the protagonist and a stranger he meets by chance in a cafe is the key to the story."

[1275] 6. The server creates a first draft including detailed scene descriptions based on the generated plot and sends it to the user.

[1276] 7. A user submits a correction request asking for more specific descriptions of strangers' appearances.

[1277] 8. The server uses an emotion engine to analyze the intent of the feedback, add detailed descriptions of the appearance, and create the final draft.

[1278] 9. The server organizes the final draft, converts it into book format, and then guides the user through procedures such as obtaining an ISBN and registering copyright.

[1279] This allows users to easily turn their ideas into books, even if they do not have any special knowledge or skills. The introduction of an emotion engine also optimizes the tone and atmosphere of the story, resulting in a more unique book.

[1280] The processing flow will be explained below.

[1281] Step 1:

[1282] The user inputs thoughts or story fragments using an input device.

[1283] Specifically, the user enters the text "Today, I talked to a stranger at a nice cafe" into a dedicated application on their smartphone or computer.

[1284] Step 2:

[1285] The terminal transmits the input text data to the server.

[1286] Once the user has completed the input, the device transmits the text data to a server in real time via the Internet.

[1287] Step 3:

[1288] The server converts the received text data into structured data and organizes it into an appropriate format.

[1289] The server checks the character encoding, normalizes line breaks and delimiters, removes unnecessary whitespace, etc.

[1290] Step 4:

[1291] The server uses an emotion engine to analyze the user's emotion from the input text.

[1292] For example, extract positive emotions from the phrase "nice cafe."

[1293] Step 5:

[1294] The server analyzes the text content using AI algorithms.

[1295] The server uses natural language processing (NLP) techniques to extract themes, themes, character traits, and scenes from the text.

[1296] Step 6:

[1297] The server generates a story plot and detailed text based on the analysis results and emotional information.

[1298] The server uses a plot generation engine to create a story outline based on the extracted themes and character settings, and then automatically generates detailed scene descriptions and dialogue using a language model (e.g., GPT).

[1299] Step 7:

[1300] The server sends the created draft to the user for confirmation.

[1301] The server will send the user a request to review the first draft via email or in-app notification.

[1302] Step 8:

[1303] The user checks the content and requests corrections or additions via the terminal.

[1304] Users read the first draft and enter feedback such as "I would like Character A to be portrayed as more courageous."

[1305] Step 9:

[1306] The terminal sends the feedback text to the server.

[1307] Any corrections or additions requested by the user are sent from the terminal to the server.

[1308] Step 10:

[1309] The server analyzes the feedback and uses an emotion engine to parse the intent of the feedback.

[1310] The server performs sentiment analysis on the feedback text to understand exactly what changes the user wants to make.

[1311] Step 11:

[1312] The server then recreates the story, incorporating the feedback.

[1313] The server detects the corrections and regenerates the text based on the feedback to produce the final draft.

[1314] Step 12:

[1315] The server converts the final draft into book format.

[1316] The server adjusts the font size and page layout, and finally converts it into PDF or ePub format.

[1317] Step 13:

[1318] The server organizes the information necessary for the publishing procedure and provides it to the user.

[1319] The server compiles information such as how to obtain an ISBN, copyright registration procedures, and how to upload to a publishing platform and presents it to the user.

[1320] Example 2

[1321] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1322] In today's world, many users are seeking an easy way to publish their thoughts and stories, but conventional systems require specialized knowledge and skills, making this difficult. Furthermore, there was a lack of means to properly optimize the atmosphere and tone of a story, making it difficult to reflect the user's intended emotions. Furthermore, the lack of flexibility in regenerating the story based on user feedback made it difficult to create a highly polished work.

[1323] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for automatically analyzing received data and converting it into structured data, artificial intelligence means for automatically generating a story plot and detailed sentences based on the structured data, means for optimizing the mood and tone of the generated story using a sentiment analysis engine, and means for receiving feedback from users and analyzing the feedback to regenerate the story. This makes it possible for users without specialized knowledge or skills to easily publish their own ideas and stories, optimize the mood and tone using a sentiment analysis engine, and regenerate a story that reflects the feedback.

[1324] "User" refers to an individual who uses the system to input their thoughts and story fragments.

[1325] "Input device" refers to a device such as a smartphone, PC, or tablet that allows a user to input text data.

[1326] A "server" refers to a computer system that receives data sent from an input device and performs processes such as analysis, data conversion, and story generation.

[1327] "Structured data" refers to data that has been converted from received text data into a regular format, and is organized to make it easier to analyze and process.

[1328] "Artificial intelligence means" refers to algorithms and technologies for automatically generating story plots and detailed text based on received structured data.

[1329] "Publishing format" refers to the standards and templates for converting the generated story into a format that can be published as a book (e.g., PDF or ePub format).

[1330] An "emotion analysis engine" refers to technology or software that analyzes emotions from text data and optimizes the mood and tone of the data.

[1331] "Feedback" refers to opinions such as requests for corrections or additions made by users to the generated story.

[1332] "Regeneration" refers to the process of reflecting on the feedback received and regenerating the story.

[1333] "Plot" refers to the outline or structure of a story, and is an outline that includes the overall flow of the story and major events.

[1334] This invention is a system that allows users to input fragments of their own thoughts or stories, analyzes and structures the text data, and then publishes it as a book. The system also includes an emotion engine that analyzes the user's emotions and a function that regenerates the story based on the feedback. The specific configuration of the system and the program processing are described below.

[1335] Hardware and Software

[1336] Hardware used: smartphone, PC, tablet (user input device), server (computing device for data analysis and story generation)

[1337] Software used: Application software (UI for user input), server software (data reception, analysis, generation, and transmission), sentiment analysis engine, AI algorithm (including natural language processing technology)

[1338] Program processing flow

[1339] User input: A user uses a smartphone or computer application to input a thought or fragment of a story, for example, "Today I spoke with a stranger in a nice cafe."

[1340] Sending data from the device: The device sends the text data entered to the server using an HTTP POST request.

[1341] Server data reception and structuring: The text data received by the server is taken and structured in JSON format. Character encoding is checked, line breaks and delimiters are normalized, and unnecessary whitespace is removed.

[1342] Sentiment analysis: The server uses an emotion engine to analyze emotions from text data. For example, it extracts "positive emotions" from the phrase "nice cafe." Sentiment analysis uses an emotion classification API.

[1343] AI analysis and story generation: The server uses natural language processing technology (such as the BERT model) to analyze the text data for themes, character traits, and scenes. Based on the analysis results, it sets the mood and tone of the story and generates a plot.

[1344] First Draft Creation: The server uses a plot generation engine to automatically generate a first draft including detailed scene descriptions and dialogue. It also uses a language model (e.g., GPT-3 or GPT-4) to generate sentences.

[1345] Sending and reviewing the first draft: The server sends the first draft to the user for review. The user receives a link to the first draft via email or in-app notification.

[1346] User feedback: The user reviews the content and inputs requests for corrections or additions. For example, they can send feedback such as, "Please be more specific in describing the stranger's appearance."

[1347] Regeneration: The server receives and analyzes the feedback, and generates a new story using the regeneration algorithm. The emotion engine analyzes the intent of the feedback.

[1348] Final drafting and conversion to publication format: The server creates a final draft based on the regenerated story and converts it to a publication format (PDF or ePub). It then cleans up the final draft and adjusts the font size and page layout.

[1349] Providing information on publishing procedures: The server provides users with the necessary information, such as how to obtain an ISBN, how to register copyright, and how to upload to a publishing platform.

[1350] Examples and prompts

[1351] Usage example: A user writes down story ideas in diary format

[1352] 1. A user uses a terminal to type a note such as, "Today, I talked to a stranger at a nice cafe."

[1353] 2. The device sends this memo to the server.

[1354] 3. The server analyzes the keywords "cafe" and "stranger" and extracts themes and settings.

[1355] 4. The server extracts "positive emotions" based on the analysis results and the emotion engine, and sets the tone of the story.

[1356] 5. The server generates a plot in which the conversation between the protagonist and a stranger he meets by chance in a cafe becomes the key to the story.

[1357] 6. The server creates a first draft based on the generated plot, including detailed scene descriptions, and sends it to the user.

[1358] 7. A user submits a correction request asking for more specific descriptions of strangers' appearances.

[1359] 8. The server uses an emotion engine to analyze the intent of the feedback, add detailed descriptions of the appearance, and create the final draft.

[1360] 9. The server organizes the final draft, converts it into book format, and then guides the user through procedures such as obtaining an ISBN and registering copyright.

[1361] Example prompt sentence:

[1362] "Today I had a conversation with a stranger at a nice cafe. Please generate a story that includes emotions."

[1363] This invention allows users to easily turn their ideas into books without any special knowledge or skills. By introducing an emotion engine, the tone and atmosphere of the story are optimized, resulting in a more unique book.

[1364] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1365] Step 1:

[1366] User-entered text

[1367] A user uses a smartphone or PC application to input a thought or fragment of a story, for example, "Today I spoke with a stranger at a nice cafe." The input data is sent from the device's input device to the application software.

[1368] input:

[1369] User-entered text (e.g., "I talked to a stranger at a nice cafe today.")

[1370] output:

[1371] Input text data stored on the device

[1372] Step 2:

[1373] Data transmission by the terminal

[1374] The device sends the text data entered by the user to the server. The device sends the data to the server over the Internet using an HTTP POST request.

[1375] input:

[1376] Input text data stored on the device

[1377] output:

[1378] Text data transferred to the server

[1379] Step 3:

[1380] Data reception and structuring by the server

[1381] The server receives the text data, parses it, and structures it. It checks the character encoding of the received data, normalizes line breaks and delimiters, and removes unnecessary whitespace. The structured data is then converted into a format such as JSON.

[1382] input:

[1383] Text data transferred to the server

[1384] output:

[1385] Structured data (e.g., JSON format)

[1386] Specific behavior:

[1387] Check the character encoding

[1388] Line break / delimiter normalization

[1389] Removing unnecessary whitespace

[1390] Conversion to JSON format

[1391] Step 4:

[1392] Emotion analysis by server

[1393] The server uses a sentiment analysis engine to analyze emotions from text data. For example, it extracts "positive emotion" from the phrase "nice cafe." It uses a sentiment classification API for sentiment analysis.

[1394] input:

[1395] Structured data

[1396] output:

[1397] Sentiment analysis results (e.g., "positive")

[1398] Specific behavior:

[1399] Call the sentiment classification API to perform sentiment analysis.

[1400] Add sentiment analysis results to structured data

[1401] Step 5:

[1402] AI analysis and story generation by server

[1403] The server uses natural language processing technology (e.g., the BERT model) to analyze the text data for themes, character traits, and scenes. Based on the analysis results, it sets the mood and tone of the story and generates a plot. The generation algorithm uses a language model (e.g., GPT-3, GPT-4).

[1404] input:

[1405] Structured data and sentiment analysis results

[1406] output:

[1407] Generated plot and narrative tone setting

[1408] Specific behavior:

[1409] Theme, character traits, and scene analysis

[1410] Setting the mood and tone

[1411] Plot generation using language models

[1412] Step 6:

[1413] Server-based first draft creation

[1414] The server uses a plot generation engine to automatically generate a first draft based on the created plot, including detailed scene descriptions and dialogue, and generates sentences using a language model (e.g., GPT-3 or GPT-4).

[1415] input:

[1416] Generated plot and narrative tone setting

[1417] output:

[1418] First draft text data

[1419] Specific behavior:

[1420] Start the plot generation engine

[1421] Automatically generate detailed scene descriptions and dialogue using language models

[1422] Step 7:

[1423] Server sends first draft and requests confirmation

[1424] The server sends the first draft to the user for review, and sends the user a link to the first draft via email or in-app notification.

[1425] input:

[1426] First draft text data

[1427] output:

[1428] Link to the first draft sent to the user

[1429] Specific behavior:

[1430] Send the first draft text data via email or in-app notification

[1431] Step 8:

[1432] User Feedback

[1433] The user checks the content and inputs requests for corrections or additions, such as "Please be more specific in describing the appearance of strangers."

[1434] input:

[1435] User feedback text

[1436] output:

[1437] Feedback text data

[1438] Specific behavior:

[1439] Enter feedback text through the application

[1440] Step 9:

[1441] Sending feedback via device

[1442] The device sends the feedback text to the server using an HTTP POST request or WebSocket.

[1443] input:

[1444] Feedback Text

[1445] output:

[1446] Feedback text forwarded to the server

[1447] Specific behavior:

[1448] Send data via HTTP POST request or WebSocket

[1449] Step 10:

[1450] Server Regeneration

[1451] The server receives and analyzes the feedback, and generates a new story using a regeneration algorithm. The emotion engine analyzes the intent of the feedback.

[1452] input:

[1453] Feedback and existing story data

[1454] output:

[1455] Regenerated narrative text

[1456] Specific behavior:

[1457] Analyzing feedback content

[1458] Analyze intent using an emotion engine

[1459] Regenerating stories using regeneration algorithms

[1460] Step 11:

[1461] Final draft creation and conversion to publication format by the server

[1462] The server creates a final draft based on the regenerated story and converts it into a publishing format (PDF or ePub), adjusting the font size and page layout.

[1463] input:

[1464] Regenerated narrative text

[1465] output:

[1466] Publication format of the final manuscript (PDF or ePub)

[1467] Specific behavior:

[1468] Creating the final draft

[1469] Adjusting font size and page layout

[1470] Conversion to publishing format

[1471] Step 12:

[1472] Server provides publishing procedure information

[1473] The server provides users with all the necessary information, such as how to obtain an ISBN, how to register copyright, and how to upload to a publishing platform.

[1474] input:

[1475] Publication-formatted final manuscript

[1476] output:

[1477] Providing information necessary for publication procedures

[1478] Specific behavior:

[1479] Information on how to obtain an ISBN

[1480] Providing information on copyright registration procedures

[1481] Guidance on how to upload to publishing platforms

[1482] (Application example 2)

[1483] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1484] This invention relates to a system that automatically analyzes and structures the thoughts and story fragments entered by users on a daily basis to generate high-quality stories. In particular, by incorporating an emotion analysis function, the system can adjust the mood and tone of the story and regenerate it based on user feedback, enabling the provision of more personalized stories. Furthermore, by providing a function to share the generated stories on content distribution services, the system aims to promote the dissemination of creative content.

[1485] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for a user to input fragments of thoughts or stories using an input device, a means for receiving data transmitted from the input device, and a means for automatically analyzing the data received by the server means and converting it into structured data. This enables automatic analysis and structuring of text data.

[1486] The system further includes an artificial intelligence means for automatically generating a story plot and detailed text based on the structured data, a means for converting the generated story into a publishing format, a means for providing information required for publishing procedures using the publishing format, a means for receiving user feedback and regenerating the story while reflecting the feedback, a means for extracting emotional information from the user's input data using an emotion analysis engine and reflecting the emotional information in the story's mood and tone, and a means for sharing the generated story or essay on a content distribution service. This makes it possible to adjust the mood and tone through emotion analysis, regenerate the story while reflecting the feedback, and share content.

[1487] An "input device" is a device that allows a user to input text data, and includes smartphones, personal computers, etc.

[1488] The "server means" is a computer system for receiving and processing data sent from an input device.

[1489] "Structured data" is a data format that is created by analyzing input text data and organizing it based on certain rules.

[1490] "Artificial intelligence means" refers to technology that uses machine learning and natural language processing techniques to automatically generate story plots and detailed sentences.

[1491] A "publishing format" is a format in which the generated story is converted into a form that can be published in print or as an e-book.

[1492] An "emotion analysis engine" is a technology that extracts emotional information from text data entered by the user and reflects that emotional information in the tone and atmosphere of the story.

[1493] A "content distribution service" is a platform for sharing generated stories and essays with other users via the Internet.

[1494] "Feedback" refers to comments and suggestions that users give to the generated story for correction or improvement.

[1495] "Regeneration" is the process of regenerating an existing story based on feedback from users.

[1496] This invention is a system that automatically analyzes and structures the thoughts and story fragments entered by users on a daily basis, generating high-quality stories. In particular, it has an emotion analysis function that adjusts the mood and tone of the story and regenerates it based on user feedback. The system also has a function to share the generated stories and essays on a content distribution service.

[1497] First, a user inputs their thoughts or fragments of a story using an input device (such as a smartphone or PC). The text data sent from the input device is then sent over the Internet to a server. The server receives this data, automatically parses it, and converts it into structured data. This process includes checking character encoding, normalizing line breaks and delimiters, and removing unnecessary whitespace.

[1498] Based on the structured data, the server uses artificial intelligence tools (e.g., a generative AI model such as GPT-4) to automatically generate the plot and detailed text of the story. The server then uses a sentiment analysis engine (e.g., IBM Watson's sentiment analysis API) to extract emotional information from the text data and reflect it in the mood and tone of the story. The generated story is then converted into a publishing format (e.g., PDF or ePub).

[1499] The generated story can be shared on a content distribution service. The user reviews the generated story and sends feedback to the server for corrections and improvements as needed. The server then uses the sentiment analysis engine again to regenerate the story, incorporating the feedback. This process allows the user to complete a work that is more in line with their own intentions and emotions.

[1500] As a concrete example, consider the case where a user enters a note saying, "Today, the sunset was so beautiful, and I can't forget the moment I talked with my friend in the park." The server receives this text and provides the following prompt to the generative AI model:

[1501] "User input text: 'Today the sunset was so beautiful, and I can't forget the moment I talked with my friend in the park.' Based on this text, generate a short story with a positive tone. The themes are 'friendship' and 'beauty of nature.' Also, reflect the emotion 'happiness' as the result of the sentiment analysis engine."

[1502] Based on this prompt, the generative AI model automatically generates a story with detailed scene descriptions and dialogue, and then receives user feedback to regenerate the content, providing a more satisfying story.

[1503] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1504] Step 1:

[1505] The user inputs fragments of thoughts or stories using an input device (such as a smartphone or PC), for example, by entering text data such as "Today, the sunset was so beautiful, and I can't forget the moment I talked with my friend in the park."

[1506] Step 2:

[1507] The terminal (input device) sends the input text data to the server via the Internet. The input data is sent in JSON format, and the server receives this data.

[1508] Step 3:

[1509] The server converts the received text data into structured data, which includes checking character encoding, normalizing line breaks and delimiters, and removing unnecessary whitespace. The resulting structured data is then ready for further analysis.

[1510] Step 4:

[1511] The server uses a sentiment analysis engine to extract emotional information from the input text. For example, it uses IBM Watson's sentiment analysis API to extract "positive" emotions from keywords such as "beautiful," "spoke," and "unforgettable." The emotional information is reflected in the next AI analysis.

[1512] Step 5:

[1513] The server uses a generative AI model (e.g., GPT-4) to analyze the input data for theme, plot, and character settings. Based on the previously extracted emotional information, the server generates a story with the appropriate mood and tone. The generated text is saved as a first draft. Specifically, the generative AI model is given the following prompt:

[1514] "User input text: 'Today the sunset was so beautiful, and I can't forget the moment I talked with my friend in the park.' Based on this text, generate a short story with a positive tone. The themes are 'friendship' and 'beauty of nature.' Also, reflect the emotion 'happiness' as the result of the sentiment analysis engine."

[1515] Step 6:

[1516] The server sends the generated draft to the user for confirmation. The user receives the draft via email or in-app notification and checks the text.

[1517] Step 7:

[1518] The user checks the content and, if necessary, enters feedback for corrections or additions through the terminal. For example, the user can enter specific feedback such as "Please be more specific in describing the appearance of strangers."

[1519] Step 8:

[1520] The device sends the feedback text to the server. The feedback data is also sent in JSON format, and the server receives this data.

[1521] Step 9:

[1522] The server analyzes the feedback and uses an emotion engine to analyze the intent of the feedback. Based on the analysis results, the generative AI model is used again to regenerate the story. The newly generated story reflects the feedback.

[1523] Step 10:

[1524] The server converts the final generated story into a publishing format, adjusting the font size and page layout, and finally converting it into PDF or ePub format.

[1525] Step 11:

[1526] The server shares the generated stories and essays on a content distribution service, allowing users to share their stories with other users via the Internet.

[1527] Step 12:

[1528] By publishing the stories created by users on content distribution services and having them viewed by many users, the spread of creative content will be promoted.

[1529] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1530] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1531] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1532] [Fourth embodiment]

[1533] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1534] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1535] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1536] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1537] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1538] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1539] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1540] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1541] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1542] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1543] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1544] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1545] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1546] This invention is a system that allows users to input fragments of their own thoughts or stories and automatically compiles them into a book. The system includes the following main components:

[1547] 1. The user uses an input device (such as a smartphone or PC) to input fragments of everyday thoughts or stories. For example, the user might input something like, "I had a dream this morning. It was about a huge castle and mysterious creatures..." into a dedicated smartphone application.

[1548] 2. The terminal sends the entered text data to the server. Each time the user completes input, the text data is sent to the server via the Internet.

[1549] 3. The server converts the received text data into structured data and organizes it into the appropriate format, including checking character encoding and handling line breaks and delimiters.

[1550] 4. The server analyzes the content of the input text using AI algorithms, specifically natural language processing (NLP) techniques, to extract themes, themes, character traits, and scenes from the text.

[1551] 5. The server applies an automatic generation algorithm based on the analysis results to create a first draft. Using a plot generation engine, the server generates a story outline based on the extracted themes and character settings, and then uses a language model to automatically generate text including detailed scene descriptions and dialogue.

[1552] 6. The server sends the created draft to the user for review. The user can review the draft via email or in-app notification and make specific corrections or additions.

[1553] 7. The user checks the content of the first draft and makes any necessary corrections or additions via the terminal. For example, the user sends feedback such as, "I would like Character A to be portrayed as more courageous."

[1554] 8. The device sends the feedback text to the server.

[1555] 9. The server regenerates the story based on the feedback. The server detects the corrections and regenerates the text based on the user's requests to create the final version.

[1556] 10. The server converts the final draft into book format. The server adjusts the font size and page layout and converts it into the final PDF or ePub format.

[1557] 11. The server organizes the information necessary for the publishing process and provides it to the user. The server provides the user with specific instructions, such as how to obtain an ISBN, how to register copyright, and how to upload to a publishing platform.

[1558] Specific examples

[1559] Usage example: A user writes down story ideas in a diary format

[1560] 1. A user uses a terminal to type a note such as, "Today, I talked to a stranger at a nice cafe."

[1561] 2. The device sends this memo to the server.

[1562] 3. The server analyzes keywords such as "cafe" and "stranger" to extract themes and settings.

[1563] 4. Based on the extracted information, the server generates a plot in which the conversation between the protagonist and a stranger he meets by chance in a cafe becomes the key to the story.

[1564] 5. The server creates a first draft including detailed scene descriptions based on the generated plot and sends it to the user.

[1565] 6. The user submits a correction request asking for more specific descriptions of strangers' appearances.

[1566] 7. The server adds detailed descriptions of the appearance based on the requests and creates the final draft.

[1567] 8. The server organizes the final draft, converts it into book format, and guides the user through procedures such as obtaining an ISBN and registering copyright.

[1568] This allows users to easily publish their ideas in book form, even if they do not have any special knowledge or skills.

[1569] The processing flow will be explained below.

[1570] Step 1:

[1571] The user inputs thoughts or story fragments using an input device.

[1572] Specifically, the user enters text such as "I had a dream this morning. A huge castle and mysterious creatures..." into a dedicated application on their smartphone or computer.

[1573] Step 2:

[1574] The terminal transmits the input text data to the server.

[1575] Each time the user completes an input, the terminal transmits the text data to a server over the Internet in real time.

[1576] Step 3:

[1577] The server converts the received text data into structured data and organizes it into an appropriate format.

[1578] This includes checking character encoding, normalizing line breaks and delimiters, and removing unnecessary whitespace.

[1579] Step 4:

[1580] The server analyzes the content of the text entered using AI algorithms.

[1581] The server uses natural language processing (NLP) technology to extract elements such as themes, subject matter, character traits, and scenes from the text.

[1582] Step 5:

[1583] The server applies an automatic generation algorithm based on the analysis results to create a first draft.

[1584] The server uses a plot generation engine to generate a story outline based on the extracted themes and character settings, and automatically generates text including detailed scene descriptions and dialogue using language models such as Generative Pretrained Transformers (GPT).

[1585] Step 6:

[1586] The server sends the generated draft to the user for confirmation.

[1587] The server will send a request to the user to review the first draft via email or in-app notification, allowing the user to review it.

[1588] Step 7:

[1589] The user checks the content and requests corrections or additions via the terminal.

[1590] Users read the first draft and enter corrections and additional feedback, such as "I would like Character A to be portrayed as more courageous."

[1591] Step 8:

[1592] The terminal sends the feedback text to the server.

[1593] Any corrections or additions requested by the user are sent from the terminal to the server.

[1594] Step 9:

[1595] The server then recreates the story, incorporating the feedback.

[1596] The server detects the corrections and regenerates the text based on the user's feedback.

[1597] Step 10:

[1598] The server converts the regenerated final draft into book format.

[1599] The server adjusts the font size and page layout, and finally converts it into PDF or ePub format.

[1600] Step 11:

[1601] The server organizes the information necessary for the publishing procedure and provides it to the user.

[1602] The server provides users with detailed instructions on how to obtain an ISBN, how to register copyright, and how to upload to a publishing platform.

[1603] Example 1

[1604] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1605] Until now, there has been a lack of efficient and easy ways for users to turn their thoughts and story fragments into books. Manually organizing and editing text is time-consuming, labor-intensive, and requires specialized knowledge, making it a significant barrier for many users. Therefore, there is a need for an efficient and automated system that allows users to easily turn their ideas into books and publish them.

[1606] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1607] In this invention, the server includes means for automatically analyzing text data and converting it into structured data, artificial intelligence means for automatically generating a story plot and detailed text based on the structured data, and means for receiving feedback from users and regenerating the story by reflecting that feedback. This allows users to efficiently turn their ideas into books and easily publish them without specialized knowledge.

[1608] An "input device" is a device that allows a user to input thoughts or fragments of a story, and specifically includes terminals such as smartphones and personal computers.

[1609] The term "server means" refers to a computer system for receiving and processing data sent from an input device.

[1610] The "means for converting into structured data" is a system that has the function of analyzing the text data received by the server means, checking the character encoding, processing line breaks and delimiters, and organizing the data.

[1611] "Artificial intelligence means" refers to AI algorithms and models that automatically generate story plots and detailed text based on structured data.

[1612] The "means for converting into a publishing format" is a system that has the function of adjusting the font size and page layout and converting the generated story into PDF or ePub format in order to publish it as a book.

[1613] "Means for providing information necessary for publishing procedures" refers to a function that guides users through specific steps such as how to obtain an ISBN, copyright registration procedures, and how to upload to a publishing platform.

[1614] "Natural language processing technology" refers to technology for analyzing input data for themes, structure, and character settings, and includes the use of NLP libraries and algorithms.

[1615] "Means for regenerating a story based on feedback" refers to the function of the server means to receive feedback from users and modify and regenerate the text of the story based on the content of that feedback.

[1616] MODE FOR CARRYING OUT THE INVENTION

[1617] This invention is a system that allows users to input fragments of their own thoughts or stories and automatically compiles them into a book. The system includes the following main components:

[1618] System Components

[1619] 1. Input Devices

[1620] The user uses an input device such as a smartphone or PC to input fragments of everyday thoughts or stories. Specifically, a dedicated application or web interface is used. For example, the user might input, "I had a dream this morning. It was about a huge castle and mysterious creatures..."

[1621] 2. Server Means

[1622] The terminal receives the text data entered by the user and sends it to the server via the Internet using an HTTP POST request.

[1623] The server converts the received text data into structured data by first checking the character encoding, then processing line breaks and delimiters, converting it to JSON format, and escaping special characters.

[1624] The server analyzes the text content using natural language processing (NLP) techniques, such as SpaCy and NLTK, to extract themes, themes, character traits, and scenes from the text.

[1625] Based on the analysis results, the server applies an automated generation algorithm to create a first draft. It uses a plot generation engine to generate a story outline based on the extracted themes and character settings. It then uses a language model (such as OpenAI's GPT-3) to generate sentences with detailed scene descriptions and dialogue.

[1626] The server sends the created draft to the user, who receives it via email or in-app notification.

[1627] The server receives feedback from the user, regenerates the story based on that feedback, and creates a final draft from the regenerated text.

[1628] The server converts the final manuscript into a publication format, adjusting the font size and page layout, and converting it into the final PDF or ePub format.

[1629] The server organizes and provides users with the information necessary for the publishing process, including how to obtain an ISBN, how to register copyright, and how to upload to a publishing platform.

[1630] Specific examples

[1631] Usage example: A user writes down story ideas in a diary format

[1632] 1. A user uses a terminal to type a note such as, "Today, I talked to a stranger at a nice cafe."

[1633] 2. The device sends this memo to the server.

[1634] 3. The server converts the received text into JSON format and escapes special characters.

[1635] 4. The server uses natural language processing technology to analyze the keywords "cafe" and "stranger" and extract themes and settings.

[1636] 5. Based on the extracted information, the server generates a plot, such as "The conversation between the protagonist and a stranger he meets by chance in a cafe will be key to the story." Using a generative AI model, the server creates a first draft that includes detailed scene descriptions and dialogue.

[1637] 6. The server sends the first draft to the user.

[1638] 7. The user reads the first draft and inputs a request for revisions on the terminal, saying, "Please be more specific in describing the stranger's appearance."

[1639] 8. The terminal resends the correction request to the server.

[1640] 9. The server analyzes the corrections and creates a final draft with the regenerated text.

[1641] 10. The server converts the final draft into PDF or ePub format and provides the book format to the user.

[1642] 11. The server guides the user on how to obtain an ISBN and register copyright.

[1643] In this way, the system allows users to easily turn their ideas into books and publish them without any special knowledge or skills.

[1644] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1645] Step 1:

[1646] The user inputs everyday thoughts or fragments of stories using an input device such as a smartphone or PC. The input data is in text format, such as "I had a dream this morning. A huge castle and mysterious creatures..." This input data is saved in text format on the device.

[1647] Step 2:

[1648] The terminal receives the input text data and sends it to the server via the Internet. Specifically, it sends the input text to the server using an HTTP POST request. The input of this step is the text data entered by the user, and the output is the text data sent to the server.

[1649] Step 3:

[1650] The server converts the received text data into structured data. First, the server checks the character encoding of the text data, then processes line breaks and delimiters. Specifically, it converts the text data into JSON format and escapes special characters. The input of this step is the text data received by the server, and the output is structured data converted into JSON format.

[1651] Step 4:

[1652] The server uses structured data to analyze the content of the text using natural language processing (NLP) techniques. The server uses an NLP library (e.g., SpaCy or NLTK) to extract themes, themes, character traits, and scenes from the text. The input for this step is structured data, and the output is the analysis results.

[1653] Step 5:

[1654] The server applies an automated generation algorithm based on the analysis results to create a first draft. It uses a plot generation engine to generate a story outline based on the extracted themes and character settings. It then uses a language model (e.g., OpenAI's GPT-3) to generate text with detailed scene descriptions and dialogue. The input for this step is the analysis results, and the output is the first draft text.

[1655] Step 6:

[1656] The server sends the created draft to the user. The server sends the draft to the user via email or in-app notification. The input of this step is the draft text, and the output is the draft sent to the user.

[1657] Step 7:

[1658] The user checks the content of the first draft and inputs any necessary corrections or requests for additions via the terminal. For example, the user may input feedback such as "I would like Character A to be portrayed as more courageous." The input for this step is the first draft that the user checked, and the resulting feedback. The output is the feedback text saved on the terminal.

[1659] Step 8:

[1660] The terminal sends the feedback text to the server. Specifically, it uses an HTTP POST request to send the feedback text requesting correction to the server. The input of this step is the feedback text received from the user, and the output is the feedback text sent to the server.

[1661] Step 9:

[1662] The server regenerates the story based on the feedback. The server detects corrections, regenerates the text based on the user's requests, and creates the final draft. The input to this step is the feedback text received by the server, and the output is the regenerated final draft text.

[1663] Step 10:

[1664] The server converts the final draft into book format, adjusting font size and page layout, and converting it into PDF or ePub format. The input for this step is the final draft text, and the output is the converted data in book format.

[1665] Step 11:

[1666] The server organizes the information necessary for the publishing process and provides it to the user. Specifically, it provides information on how to obtain an ISBN, how to register copyright, how to upload to a publishing platform, etc. The input for this step is data converted into book format, and the output is information on the publishing process.

[1667] (Application example 1)

[1668] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1669] Conventional story creation systems lack the functionality to automatically publish a book simply by inputting users' thoughts or fragments of a story. They also lack the ability to reflect feedback on the stories they create and regenerate them, or the ability to easily post them to content distribution platforms. Furthermore, they lack the information provided to simplify the publishing process, making it difficult for individuals to easily publish their own stories.

[1670] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1671] In this invention, the server includes: a means for a user to input thoughts or story fragments using an input device; a means for receiving data transmitted from the input device; a means for automatically analyzing the data received by the server means and converting it into structured data; an artificial intelligence means for automatically generating a story plot and detailed text based on the structured data; a means for converting the generated story into a publishing format; a means for providing information necessary for the publishing procedure using the publishing format; and a means for automatically posting the generated story to a content distribution platform. This allows users to easily input their own stories, which are then automatically analyzed, generated, regenerated, and distributed. Furthermore, the information necessary for the publishing procedure is also provided, allowing individuals to easily publish their own stories.

[1672] "User" refers to the person who uses the system to input their thoughts and story fragments.

[1673] An "input device" refers to a device such as a smartphone or computer that allows users to input text data.

[1674] "Data receiving means" refers to a mechanism by which the server receives text data sent from the input device.

[1675] "Server" refers to a computing device that processes input data and executes each means of the system.

[1676] "Structured data" refers to text data analyzed by a server that has been organized and systematized into a specific format.

[1677] "Artificial intelligence means" refers to technology that uses natural language processing technology and generative AI models to automatically generate story plots and detailed sentences.

[1678] "Publishing format" refers to the data format used to convert the generated story into a suitable format for a book or e-book.

[1679] "Information required for publishing procedures" refers to the specific information required to publish a book, such as how to obtain an ISBN, copyright registration procedures, and how to upload to a publishing platform.

[1680] "Content distribution platform" refers to an online service or website for publishing generated stories.

[1681] "Feedback" refers to corrections or requests for additions that a user provides to a generated story.

[1682] This invention relates to a system that allows users to easily input fragments of their own thoughts or stories and automatically compile them into a book. The system mainly consists of the following components:

[1683] Users use input devices such as smartphones and computers to input their daily events and ideas. For example, they can enter a note into a dedicated smartphone application saying, "Today, I talked to a stranger at a nice cafe."

[1684] The terminal sends the entered text data to the server, where it is securely transmitted over the Internet.

[1685] The server converts the received text data into structured data, which includes checking character encoding, handling line breaks and delimiters, etc.

[1686] The server uses natural language processing (NLP) to analyze the text and extract themes, characters, and scenes. For example, it extracts key elements of a story from keywords like "cafe" and "stranger."

[1687] The server uses a generative AI model based on the analysis results to automatically generate the plot and detailed sentences of the story. At this time, a generative AI model (e.g., GPT-3) is used to generate prompt sentences. For example, a plot such as "A conversation with a stranger you meet by chance in a cafe is the key to the story" is generated.

[1688] The first draft of the story is sent to the user, who can review it via email or in-app notifications, with any necessary corrections or additions requested. For example, feedback such as "be more specific in describing the stranger's appearance" is provided.

[1689] The server then recreates the story based on the feedback and creates a final draft, again using generative AI models to add specific dialogue and scenes.

[1690] The server converts the final draft into a publishing format and automatically posts it to a content distribution platform, where the resulting story is immediately made available online.

[1691] The server also provides information necessary for the publishing process, including specific steps on how to obtain an ISBN, register copyright, and upload to a publishing platform.

[1692] As a concrete example, consider a user writing down story ideas in a diary format:

[1693] 1. Input sentence: "Today, I talked to a stranger at a nice cafe."

[1694] 2. Prompt after analysis: "The conversation with a stranger you meet by chance in a cafe is the key to the story."

[1695] In this way, users can easily publish their ideas and share them on the content distribution platform, even if they do not have special knowledge or skills. This system provides a new means for individuals to easily generate and distribute creative content.

[1696] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1697] Step 1:

[1698] Users input fragments of thoughts or stories using an input device such as a smartphone or computer.

[1699] Input: Text data entered by the user (e.g., "Today, I spoke with a stranger at a nice cafe.")

[1700] Output: The input text data

[1701] Step 2:

[1702] The terminal transmits the input text data to the server.

[1703] Input: Text data entered in step 1

[1704] Output: Text data sent to the server (sent over the internet)

[1705] Step 3:

[1706] The server converts the received text data into structured data.

[1707] Input: Text data sent from the terminal

[1708] Data processing: Check character encoding, process line breaks and delimiters

[1709] Output: Structured data (e.g., JSON format data)

[1710] Step 4:

[1711] The server analyzes the text using natural language processing (NLP) technology to extract themes, characters, and scenes.

[1712] Input: Structured data from step 3

[1713] Data Computation: Using NLP techniques to analyze text and extract themes, characters, and scenes

[1714] Output: Analysis results (e.g., theme "cafe", character "stranger")

[1715] Step 5:

[1716] The server uses a generative AI model based on the analysis results to automatically generate the story plot and detailed text.

[1717] Input: Analysis results obtained in step 4

[1718] Data computation: Generative AI models (e.g., GPT-3) are used to generate prompts and create story outlines and detailed sentences.

[1719] Output: First draft of the story (e.g., "A conversation with a stranger I met by chance in a cafe is key to the story.")

[1720] Step 6:

[1721] The server sends the generated first draft of the story to the user.

[1722] Input: First draft of the story generated in step 5

[1723] Output: The first draft sent to the user (via email or in-app notification)

[1724] Step 7:

[1725] The user checks the first draft and sends any necessary corrections or requests for additions as feedback via the terminal.

[1726] Input: User feedback (e.g., "Please be more specific in describing strangers' appearances")

[1727] Output: Feedback text

[1728] Step 8:

[1729] The terminal sends the feedback text to the server.

[1730] Input: User feedback text

[1731] Output: Feedback text sent to the server (sent over the internet)

[1732] Step 9:

[1733] The server then recreates the story, incorporating the feedback.

[1734] Input: Feedback text and first draft

[1735] Data Computation: Using Generative AI Models to Regenerate Narratives Reflecting Feedback

[1736] Output: Final draft (e.g. "The story now includes a description of the stranger's appearance")

[1737] Step 10:

[1738] The server converts the final draft into a publishing format and automatically posts it to a content distribution platform.

[1739] Input: Final draft

[1740] Data processing: converting to publishing formats (e.g. PDF, ePub) and posting to content distribution platforms

[1741] Output: Final version posted to distribution platform

[1742] Step 11:

[1743] The server provides the information necessary for the publishing process.

[1744] Input: Final manuscript converted to publication format

[1745] Output: Specific information on how to obtain an ISBN, copyright registration procedures, and how to upload to publishing platforms.

[1746] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1747] This invention is a system in which users input fragments of their own thoughts or stories, and the text data is automatically analyzed, structured, and published as a book. The system also incorporates an emotion engine that analyzes the user's emotions, which also influences the mood and tone of the story. The specific configuration of the system and the program processing are explained below.

[1748] 1. User input:

[1749] A user inputs a thought or fragment of a story using an input device (such as a smartphone or PC). Specifically, the user inputs a note into the application, such as "Today, I talked to a stranger at a nice cafe."

[1750] 2. Data transmission by the device:

[1751] The terminal transmits the input text data to the server. Each time the user completes input, the terminal sends the text data to the server via the Internet.

[1752] 3. Data reception and structuring by the server:

[1753] The server converts the received text data into structured data and organizes it into the appropriate format, which includes checking character encoding, normalizing line breaks and delimiters, and removing unnecessary whitespace.

[1754] 4. Server-based sentiment analysis:

[1755] The server uses an emotion engine to analyze the user's emotion from the input text, for example, extracting "positive" emotion from the text "nice cafe."

[1756] 5. AI analysis and story generation by the server:

[1757] The server uses AI algorithms (including NLP technology) to analyze the input data for themes, themes, character traits, and scenes, and generates a story with the appropriate mood and tone based on the emotional information obtained from the emotion engine.

[1758] 6. Server-generated first draft:

[1759] The server uses a plot generation engine to create a story outline based on the analysis results, and automatically generates text including detailed scene descriptions and dialogue using a language model (e.g., GPT).

[1760] 7. Server sends first draft and requests confirmation:

[1761] The server sends the created draft to the user for confirmation. The user receives a request to review the draft via email or in-app notification and confirms the content.

[1762] 8. User Feedback:

[1763] The user checks the content and requests corrections or additions via the terminal. For example, they input feedback such as "I want Character A to be portrayed as more courageous."

[1764] 9. Sending Feedback via Device:

[1765] The terminal sends the feedback text to the server.

[1766] 10. Server Regeneration:

[1767] The server analyzes the feedback and uses an emotion engine to parse the intent of the feedback and recreate the story.

[1768] 11. Final Draft by Server:

[1769] The server creates a final draft based on the regenerated story and converts it into a book format, adjusting the font size and page layout, and finally converting it into PDF or ePub format.

[1770] 12. Server Providing Publishing Information:

[1771] The server organizes and provides users with information necessary for publishing, such as how to obtain an ISBN, copyright registration procedures, and how to upload to a publishing platform.

[1772] Specific examples

[1773] Usage example: A user writes down story ideas in a diary format

[1774] 1. A user uses a terminal to type a note such as, "Today, I talked to a stranger at a nice cafe."

[1775] 2. The device sends this memo to the server.

[1776] 3. The server analyzes the keywords "cafe" and "stranger" and extracts themes and settings.

[1777] 4. The server extracts "positive emotions" based on the analysis results and the emotion engine, and sets the tone of the story.

[1778] 5. The server generates a plot that states, "The conversation between the protagonist and a stranger he meets by chance in a cafe is the key to the story."

[1779] 6. The server creates a first draft including detailed scene descriptions based on the generated plot and sends it to the user.

[1780] 7. A user submits a correction request asking for more specific descriptions of strangers' appearances.

[1781] 8. The server uses an emotion engine to analyze the intent of the feedback, add detailed descriptions of the appearance, and create the final draft.

[1782] 9. The server organizes the final draft, converts it into book format, and then guides the user through procedures such as obtaining an ISBN and registering copyright.

[1783] This allows users to easily turn their ideas into books, even if they do not have any special knowledge or skills. The introduction of an emotion engine also optimizes the tone and atmosphere of the story, resulting in a more unique book.

[1784] The processing flow will be explained below.

[1785] Step 1:

[1786] The user inputs thoughts or story fragments using an input device.

[1787] Specifically, the user enters the text "Today, I talked to a stranger at a nice cafe" into a dedicated application on their smartphone or computer.

[1788] Step 2:

[1789] The terminal transmits the input text data to the server.

[1790] Once the user has completed the input, the device transmits the text data to a server in real time via the Internet.

[1791] Step 3:

[1792] The server converts the received text data into structured data and organizes it into an appropriate format.

[1793] The server checks the character encoding, normalizes line breaks and delimiters, removes unnecessary whitespace, etc.

[1794] Step 4:

[1795] The server uses an emotion engine to analyze the user's emotion from the input text.

[1796] For example, extract positive emotions from the phrase "nice cafe."

[1797] Step 5:

[1798] The server analyzes the text content using AI algorithms.

[1799] The server uses natural language processing (NLP) techniques to extract themes, themes, character traits, and scenes from the text.

[1800] Step 6:

[1801] The server generates a story plot and detailed text based on the analysis results and emotional information.

[1802] The server uses a plot generation engine to create a story outline based on the extracted themes and character settings, and then automatically generates detailed scene descriptions and dialogue using a language model (e.g., GPT).

[1803] Step 7:

[1804] The server sends the created draft to the user for confirmation.

[1805] The server will send the user a request to review the first draft via email or in-app notification.

[1806] Step 8:

[1807] The user checks the content and requests corrections or additions via the terminal.

[1808] Users read the first draft and enter feedback such as "I would like Character A to be portrayed as more courageous."

[1809] Step 9:

[1810] The terminal sends the feedback text to the server.

[1811] Any corrections or additions requested by the user are sent from the terminal to the server.

[1812] Step 10:

[1813] The server analyzes the feedback and uses an emotion engine to parse the intent of the feedback.

[1814] The server performs sentiment analysis on the feedback text to understand exactly what changes the user wants to make.

[1815] Step 11:

[1816] The server then recreates the story, incorporating the feedback.

[1817] The server detects the corrections and regenerates the text based on the feedback to produce the final draft.

[1818] Step 12:

[1819] The server converts the final draft into book format.

[1820] The server adjusts the font size and page layout, and finally converts it into PDF or ePub format.

[1821] Step 13:

[1822] The server organizes the information necessary for the publishing procedure and provides it to the user.

[1823] The server compiles information such as how to obtain an ISBN, copyright registration procedures, and how to upload to a publishing platform and presents it to the user.

[1824] Example 2

[1825] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1826] In today's world, many users are seeking an easy way to publish their thoughts and stories, but conventional systems require specialized knowledge and skills, making this difficult. Furthermore, there was a lack of means to properly optimize the atmosphere and tone of a story, making it difficult to reflect the user's intended emotions. Furthermore, the lack of flexibility in regenerating the story based on user feedback made it difficult to create a highly polished work.

[1827] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for automatically analyzing received data and converting it into structured data, artificial intelligence means for automatically generating a story plot and detailed sentences based on the structured data, means for optimizing the mood and tone of the generated story using a sentiment analysis engine, and means for receiving feedback from users and analyzing the feedback to regenerate the story. This makes it possible for users without specialized knowledge or skills to easily publish their own ideas and stories, optimize the mood and tone using a sentiment analysis engine, and regenerate a story that reflects the feedback.

[1828] "User" refers to an individual who uses the system to input their thoughts and story fragments.

[1829] "Input device" refers to a device such as a smartphone, PC, or tablet that allows a user to input text data.

[1830] A "server" refers to a computer system that receives data sent from an input device and performs processes such as analysis, data conversion, and story generation.

[1831] "Structured data" refers to data that has been converted from received text data into a regular format, and is organized to make it easier to analyze and process.

[1832] "Artificial intelligence means" refers to algorithms and technologies for automatically generating story plots and detailed text based on received structured data.

[1833] "Publishing format" refers to the standards and templates for converting the generated story into a format that can be published as a book (e.g., PDF or ePub format).

[1834] An "emotion analysis engine" refers to technology or software that analyzes emotions from text data and optimizes the mood and tone of the data.

[1835] "Feedback" refers to opinions such as requests for corrections or additions made by users to the generated story.

[1836] "Regeneration" refers to the process of reflecting on the feedback received and regenerating the story.

[1837] "Plot" refers to the outline or structure of a story, and is an outline that includes the overall flow of the story and major events.

[1838] This invention is a system that allows users to input fragments of their own thoughts or stories, analyzes and structures the text data, and then publishes it as a book. The system also includes an emotion engine that analyzes the user's emotions and a function that regenerates the story based on the feedback. The specific configuration of the system and the program processing are described below.

[1839] Hardware and Software

[1840] Hardware used: smartphone, PC, tablet (user input device), server (computing device for data analysis and story generation)

[1841] Software used: Application software (UI for user input), server software (data reception, analysis, generation, and transmission), sentiment analysis engine, AI algorithm (including natural language processing technology)

[1842] Program processing flow

[1843] User input: A user uses a smartphone or computer application to input a thought or fragment of a story, for example, "Today I spoke with a stranger in a nice cafe."

[1844] Sending data from the device: The device sends the text data entered to the server using an HTTP POST request.

[1845] Server data reception and structuring: The text data received by the server is taken and structured in JSON format. Character encoding is checked, line breaks and delimiters are normalized, and unnecessary whitespace is removed.

[1846] Sentiment analysis: The server uses an emotion engine to analyze emotions from text data. For example, it extracts "positive emotions" from the phrase "nice cafe." Sentiment analysis uses an emotion classification API.

[1847] AI analysis and story generation: The server uses natural language processing technology (such as the BERT model) to analyze the text data for themes, character traits, and scenes. Based on the analysis results, it sets the mood and tone of the story and generates a plot.

[1848] First Draft Creation: The server uses a plot generation engine to automatically generate a first draft including detailed scene descriptions and dialogue. It also uses a language model (e.g., GPT-3 or GPT-4) to generate sentences.

[1849] Sending and reviewing the first draft: The server sends the first draft to the user for review. The user receives a link to the first draft via email or in-app notification.

[1850] User feedback: The user reviews the content and inputs requests for corrections or additions. For example, they can send feedback such as, "Please be more specific in describing the stranger's appearance."

[1851] Regeneration: The server receives and analyzes the feedback, and generates a new story using the regeneration algorithm. The emotion engine analyzes the intent of the feedback.

[1852] Final drafting and conversion to publication format: The server creates a final draft based on the regenerated story and converts it to a publication format (PDF or ePub). It then cleans up the final draft and adjusts the font size and page layout.

[1853] Providing information on publishing procedures: The server provides users with the necessary information, such as how to obtain an ISBN, how to register copyright, and how to upload to a publishing platform.

[1854] Examples and prompts

[1855] Usage example: A user writes down story ideas in diary format

[1856] 1. A user uses a terminal to type a note such as, "Today, I talked to a stranger at a nice cafe."

[1857] 2. The device sends this memo to the server.

[1858] 3. The server analyzes the keywords "cafe" and "stranger" and extracts themes and settings.

[1859] 4. The server extracts "positive emotions" based on the analysis results and the emotion engine, and sets the tone of the story.

[1860] 5. The server generates a plot in which the conversation between the protagonist and a stranger he meets by chance in a cafe becomes the key to the story.

[1861] 6. The server creates a first draft based on the generated plot, including detailed scene descriptions, and sends it to the user.

[1862] 7. A user submits a correction request asking for more specific descriptions of strangers' appearances.

[1863] 8. The server uses an emotion engine to analyze the intent of the feedback, add detailed descriptions of the appearance, and create the final draft.

[1864] 9. The server organizes the final draft, converts it into book format, and then guides the user through procedures such as obtaining an ISBN and registering copyright.

[1865] Example prompt sentence:

[1866] "Today I had a conversation with a stranger at a nice cafe. Please generate a story that includes emotions."

[1867] This invention allows users to easily turn their ideas into books without any special knowledge or skills. By introducing an emotion engine, the tone and atmosphere of the story are optimized, resulting in a more unique book.

[1868] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1869] Step 1:

[1870] User-entered text

[1871] A user uses a smartphone or PC application to input a thought or fragment of a story, for example, "Today I spoke with a stranger at a nice cafe." The input data is sent from the device's input device to the application software.

[1872] input:

[1873] User-entered text (e.g., "I talked to a stranger at a nice cafe today.")

[1874] output:

[1875] Input text data stored on the device

[1876] Step 2:

[1877] Data transmission by the terminal

[1878] The device sends the text data entered by the user to the server. The device sends the data to the server over the Internet using an HTTP POST request.

[1879] input:

[1880] Input text data stored on the device

[1881] output:

[1882] Text data transferred to the server

[1883] Step 3:

[1884] Data reception and structuring by the server

[1885] The server receives the text data, parses it, and structures it. It checks the character encoding of the received data, normalizes line breaks and delimiters, and removes unnecessary whitespace. The structured data is then converted into a format such as JSON.

[1886] input:

[1887] Text data transferred to the server

[1888] output:

[1889] Structured data (e.g., JSON format)

[1890] Specific behavior:

[1891] Check the character encoding

[1892] Line break / delimiter normalization

[1893] Removing unnecessary whitespace

[1894] Conversion to JSON format

[1895] Step 4:

[1896] Emotion analysis by server

[1897] The server uses a sentiment analysis engine to analyze emotions from text data. For example, it extracts "positive emotion" from the phrase "nice cafe." It uses a sentiment classification API for sentiment analysis.

[1898] input:

[1899] Structured data

[1900] output:

[1901] Sentiment analysis results (e.g., "positive")

[1902] Specific behavior:

[1903] Call the sentiment classification API to perform sentiment analysis.

[1904] Add sentiment analysis results to structured data

[1905] Step 5:

[1906] AI analysis and story generation by server

[1907] The server uses natural language processing technology (e.g., the BERT model) to analyze the text data for themes, character traits, and scenes. Based on the analysis results, it sets the mood and tone of the story and generates a plot. The generation algorithm uses a language model (e.g., GPT-3, GPT-4).

[1908] input:

[1909] Structured data and sentiment analysis results

[1910] output:

[1911] Generated plot and narrative tone setting

[1912] Specific behavior:

[1913] Theme, character traits, and scene analysis

[1914] Setting the mood and tone

[1915] Plot generation using language models

[1916] Step 6:

[1917] Server-based first draft creation

[1918] The server uses a plot generation engine to automatically generate a first draft based on the created plot, including detailed scene descriptions and dialogue, and generates sentences using a language model (e.g., GPT-3 or GPT-4).

[1919] input:

[1920] Generated plot and narrative tone setting

[1921] output:

[1922] First draft text data

[1923] Specific behavior:

[1924] Start the plot generation engine

[1925] Automatically generate detailed scene descriptions and dialogue using language models

[1926] Step 7:

[1927] Server sends first draft and requests confirmation

[1928] The server sends the first draft to the user for review, and sends the user a link to the first draft via email or in-app notification.

[1929] input:

[1930] First draft text data

[1931] output:

[1932] Link to the first draft sent to the user

[1933] Specific behavior:

[1934] Send the first draft text data via email or in-app notification

[1935] Step 8:

[1936] User Feedback

[1937] The user checks the content and inputs requests for corrections or additions, such as "Please be more specific in describing the appearance of strangers."

[1938] input:

[1939] User feedback text

[1940] output:

[1941] Feedback text data

[1942] Specific behavior:

[1943] Enter feedback text through the application

[1944] Step 9:

[1945] Sending feedback via device

[1946] The device sends the feedback text to the server using an HTTP POST request or WebSocket.

[1947] input:

[1948] Feedback Text

[1949] output:

[1950] Feedback text forwarded to the server

[1951] Specific behavior:

[1952] Send data via HTTP POST request or WebSocket

[1953] Step 10:

[1954] Server Regeneration

[1955] The server receives and analyzes the feedback, and generates a new story using a regeneration algorithm. The emotion engine analyzes the intent of the feedback.

[1956] input:

[1957] Feedback and existing story data

[1958] output:

[1959] Regenerated narrative text

[1960] Specific behavior:

[1961] Analyzing feedback content

[1962] Analyze intent using an emotion engine

[1963] Regenerating stories using regeneration algorithms

[1964] Step 11:

[1965] Final draft creation and conversion to publication format by the server

[1966] The server creates a final draft based on the regenerated story and converts it into a publishing format (PDF or ePub), adjusting the font size and page layout.

[1967] input:

[1968] Regenerated narrative text

[1969] output:

[1970] Publication format of the final manuscript (PDF or ePub)

[1971] Specific behavior:

[1972] Creating the final draft

[1973] Adjusting font size and page layout

[1974] Conversion to publishing format

[1975] Step 12:

[1976] Server provides publishing procedure information

[1977] The server provides users with all the necessary information, such as how to obtain an ISBN, how to register copyright, and how to upload to a publishing platform.

[1978] input:

[1979] Publication-formatted final manuscript

[1980] output:

[1981] Providing information necessary for publication procedures

[1982] Specific behavior:

[1983] Information on how to obtain an ISBN

[1984] Providing information on copyright registration procedures

[1985] Guidance on how to upload to publishing platforms

[1986] (Application example 2)

[1987] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1988] This invention relates to a system that automatically analyzes and structures the thoughts and story fragments entered by users on a daily basis to generate high-quality stories. In particular, by incorporating an emotion analysis function, the system can adjust the mood and tone of the story and regenerate it based on user feedback, enabling the provision of more personalized stories. Furthermore, by providing a function to share the generated stories on content distribution services, the system aims to promote the dissemination of creative content.

[1989] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for a user to input fragments of thoughts or stories using an input device, a means for receiving data transmitted from the input device, and a means for automatically analyzing the data received by the server means and converting it into structured data. This enables automatic analysis and structuring of text data.

[1990] The system further includes an artificial intelligence means for automatically generating a story plot and detailed text based on the structured data, a means for converting the generated story into a publishing format, a means for providing information required for publishing procedures using the publishing format, a means for receiving user feedback and regenerating the story while reflecting the feedback, a means for extracting emotional information from the user's input data using an emotion analysis engine and reflecting the emotional information in the story's mood and tone, and a means for sharing the generated story or essay on a content distribution service. This makes it possible to adjust the mood and tone through emotion analysis, regenerate the story while reflecting the feedback, and share content.

[1991] An "input device" is a device that allows a user to input text data, and includes smartphones, personal computers, etc.

[1992] The "server means" is a computer system for receiving and processing data sent from an input device.

[1993] "Structured data" is a data format that is created by analyzing input text data and organizing it based on certain rules.

[1994] "Artificial intelligence means" refers to technology that uses machine learning and natural language processing techniques to automatically generate story plots and detailed sentences.

[1995] A "publishing format" is a format in which the generated story is converted into a form that can be published in print or as an e-book.

[1996] An "emotion analysis engine" is a technology that extracts emotional information from text data entered by the user and reflects that emotional information in the tone and atmosphere of the story.

[1997] A "content distribution service" is a platform for sharing generated stories and essays with other users via the Internet.

[1998] "Feedback" refers to comments and suggestions that users give to the generated story for correction or improvement.

[1999] "Regeneration" is the process of regenerating an existing story based on feedback from users.

[2000] This invention is a system that automatically analyzes and structures the thoughts and story fragments entered by users on a daily basis, generating high-quality stories. In particular, it has an emotion analysis function that adjusts the mood and tone of the story and regenerates it based on user feedback. The system also has a function to share the generated stories and essays on a content distribution service.

[2001] First, a user inputs their thoughts or fragments of a story using an input device (such as a smartphone or PC). The text data sent from the input device is then sent over the Internet to a server. The server receives this data, automatically parses it, and converts it into structured data. This process includes checking character encoding, normalizing line breaks and delimiters, and removing unnecessary whitespace.

[2002] Based on the structured data, the server uses artificial intelligence tools (e.g., a generative AI model such as GPT-4) to automatically generate the plot and detailed text of the story. The server then uses a sentiment analysis engine (e.g., IBM Watson's sentiment analysis API) to extract emotional information from the text data and reflect it in the mood and tone of the story. The generated story is then converted into a publishing format (e.g., PDF or ePub).

[2003] The generated story can be shared on a content distribution service. The user reviews the generated story and sends feedback to the server for corrections and improvements as needed. The server then uses the sentiment analysis engine again to regenerate the story, incorporating the feedback. This process allows the user to complete a work that is more in line with their own intentions and emotions.

[2004] As a concrete example, consider the case where a user enters a note saying, "Today, the sunset was so beautiful, and I can't forget the moment I talked with my friend in the park." The server receives this text and provides the following prompt to the generative AI model:

[2005] "User input text: 'Today the sunset was so beautiful, and I can't forget the moment I talked with my friend in the park.' Based on this text, generate a short story with a positive tone. The themes are 'friendship' and 'beauty of nature.' Also, reflect the emotion 'happiness' as the result of the sentiment analysis engine."

[2006] Based on this prompt, the generative AI model automatically generates a story with detailed scene descriptions and dialogue, and then receives user feedback to regenerate the content, providing a more satisfying story.

[2007] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2008] Step 1:

[2009] The user inputs fragments of thoughts or stories using an input device (such as a smartphone or PC), for example, by entering text data such as "Today, the sunset was so beautiful, and I can't forget the moment I talked with my friend in the park."

[2010] Step 2:

[2011] The terminal (input device) sends the input text data to the server via the Internet. The input data is sent in JSON format, and the server receives this data.

[2012] Step 3:

[2013] The server converts the received text data into structured data, which includes checking character encoding, normalizing line breaks and delimiters, and removing unnecessary whitespace. The resulting structured data is then ready for further analysis.

[2014] Step 4:

[2015] The server uses a sentiment analysis engine to extract emotional information from the input text. For example, it uses IBM Watson's sentiment analysis API to extract "positive" emotions from keywords such as "beautiful," "spoke," and "unforgettable." The emotional information is reflected in the next AI analysis.

[2016] Step 5:

[2017] The server uses a generative AI model (e.g., GPT-4) to analyze the input data for theme, plot, and character settings. Based on the previously extracted emotional information, the server generates a story with the appropriate mood and tone. The generated text is saved as a first draft. Specifically, the generative AI model is given the following prompt:

[2018] "User input text: 'Today the sunset was so beautiful, and I can't forget the moment I talked with my friend in the park.' Based on this text, generate a short story with a positive tone. The themes are 'friendship' and 'beauty of nature.' Also, reflect the emotion 'happiness' as the result of the sentiment analysis engine."

[2019] Step 6:

[2020] The server sends the generated draft to the user for confirmation. The user receives the draft via email or in-app notification and checks the text.

[2021] Step 7:

[2022] The user checks the content and, if necessary, enters feedback for corrections or additions through the terminal. For example, the user can enter specific feedback such as "Please be more specific in describing the appearance of strangers."

[2023] Step 8:

[2024] The device sends the feedback text to the server. The feedback data is also sent in JSON format, and the server receives this data.

[2025] Step 9:

[2026] The server analyzes the feedback and uses an emotion engine to analyze the intent of the feedback. Based on the analysis results, the generative AI model is used again to regenerate the story. The newly generated story reflects the feedback.

[2027] Step 10:

[2028] The server converts the final generated story into a publishing format, adjusting the font size and page layout, and finally converting it into PDF or ePub format.

[2029] Step 11:

[2030] The server shares the generated stories and essays on a content distribution service, allowing users to share their stories with other users via the Internet.

[2031] Step 12:

[2032] By publishing the stories created by users on content distribution services and having them viewed by many users, the spread of creative content will be promoted.

[2033] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2034] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2035] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2036] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2037] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2038] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2039] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2040] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2041] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2042] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2043] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2044] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2045] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2046] 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.

[2047] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2048] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2049] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2050] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2051] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2052] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2053] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2054] The following is further disclosed regarding the above embodiment.

[2055] (Claim 1)

[2056] a means for a user to input thoughts or story fragments using an input device;

[2057] a server means for receiving data transmitted from the input device;

[2058] means for automatically analyzing the data received by the server means and converting it into structured data;

[2059] an artificial intelligence means for automatically generating a story plot and detailed sentences based on the structured data;

[2060] a means for converting the generated stories into a publishing format;

[2061] a means for providing information necessary for publishing procedures using the publishing format;

[2062] A system including:

[2063] (Claim 2)

[2064] 2. The system of claim 1, wherein the artificial intelligence means uses natural language processing techniques to analyze the input data for theme, structure, and character settings.

[2065] (Claim 3)

[2066] 2. The system of claim 1, wherein said server means includes means for receiving feedback from a user and regenerating a story to reflect said feedback.

[2067] "Example 1"

[2068] (Claim 1)

[2069] a means for a user to input thoughts or story fragments using an input device;

[2070] a server means for receiving data transmitted from the input device;

[2071] means for automatically analyzing the data received by the server means and converting it into structured data;

[2072] an artificial intelligence means for automatically generating a story plot and detailed sentences based on the structured data;

[2073] a means for converting the generated stories into a publishing format;

[2074] a means for providing information necessary for publishing procedures using the publishing format;

[2075] means for receiving feedback from a user and regenerating a story by reflecting the feedback;

[2076] A system including:

[2077] (Claim 2)

[2078] 2. The system of claim 1, wherein the artificial intelligence means uses natural language processing techniques to analyze the input data for theme, structure, and character settings.

[2079] (Claim 3)

[2080] 2. The system according to claim 1, wherein the server means includes means for checking character encoding, processing line breaks and delimiters, and converting input data into structured data.

[2081] "Application Example 1"

[2082] (Claim 1)

[2083] a means for a user to input thoughts or story fragments using an input device;

[2084] a server means for receiving data transmitted from the input device;

[2085] means for automatically analyzing the data received by the server means and converting it into structured data;

[2086] an artificial intelligence means for automatically generating a story plot and detailed sentences based on the structured data;

[2087] a means for converting the generated stories into a publishing format;

[2088] a means for providing information necessary for publishing procedures using the publishing format;

[2089] A means for automatically posting the generated stories to a content distribution platform;

[2090] A system including:

[2091] (Claim 2)

[2092] 2. The system of claim 1, wherein the artificial intelligence means uses natural language processing techniques to analyze the input data for theme, structure, and character settings.

[2093] (Claim 3)

[2094] 2. The system of claim 1, wherein said server means includes means for receiving feedback from a user and regenerating a story to reflect said feedback.

[2095] "Example 2: Combining Emotion Engines"

[2096] (Claim 1)

[2097] a means for a user to input thoughts or story fragments using an input device;

[2098] a server means for receiving data transmitted from the input device;

[2099] means for automatically analyzing the data received by the server means and converting it into structured data;

[2100] an artificial intelligence means for automatically generating a story plot and detailed sentences based on the structured data;

[2101] a means for converting the generated stories into a publishing format;

[2102] a means for providing information necessary for publishing procedures using the publishing format;

[2103] means for optimizing the mood and tone of the generated story using a sentiment analysis engine;

[2104] means for receiving feedback from a user and analyzing the feedback to recreate the story;

[2105] A system including:

[2106] (Claim 2)

[2107] 2. The system of claim 1, wherein the artificial intelligence means uses natural language processing techniques to analyze the input data for theme, structure, and character settings.

[2108] (Claim 3)

[2109] 2. The system of claim 1, wherein the server means includes means for receiving feedback from a user, regenerating a story to reflect the feedback, and optimizing the regenerated story using a sentiment analysis engine.

[2110] "Application example 2 when combining emotion engines"

[2111] (Claim 1)

[2112] a means for a user to input thoughts or story fragments using an input device;

[2113] a server means for receiving data transmitted from the input device;

[2114] means for automatically analyzing the data received by the server means and converting it into structured data;

[2115] an artificial intelligence means for automatically generating a story plot and detailed sentences based on the structured data;

[2116] a means for converting the generated stories into a publishing format;

[2117] a means for providing information necessary for publishing procedures using the publishing format;

[2118] means for receiving feedback from a user and regenerating the content in accordance with the feedback;

[2119] Using an emotion analysis engine, we extract emotional information from user input data and reflect it in the atmosphere and tone of the story.

[2120] A means to share the generated stories and essays on a content distribution service,

[2121] A system including:

[2122] (Claim 2)

[2123] The system of claim 1, wherein the artificial intelligence means uses natural language processing technology to analyze the theme, structure, and character settings of the input data, and further adjusts the tone and atmosphere based on emotional information extracted by an emotional analysis engine.

[2124] (Claim 3)

[2125] 2. The system of claim 1, wherein said server means includes means for receiving feedback from a user and regenerating a story to reflect said feedback. [Explanation of symbols]

[2126] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for a user to input thoughts or story fragments using an input device; a server means for receiving data transmitted from the input device; means for automatically analyzing the data received by the server means and converting it into structured data; an artificial intelligence means for automatically generating a story plot and detailed sentences based on the structured data; a means for converting the generated stories into a publishing format; a means for providing information necessary for publishing procedures using the publishing format; A system including:

2. 2. The system of claim 1, wherein said artificial intelligence means uses natural language processing techniques to analyze the input data for theme, structure, and character settings.

3. 2. The system of claim 1, wherein said server means includes means for receiving feedback from users and regenerating the story to reflect said feedback.

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