System

Abstract

A system is provided.SOLUTION: A system comprising: means for reading an existing literary work from an input device; means for analyzing text of the literary work read from the input device; means for generating a new literary work using text data extracted by the analyzing means based on a prompt provided by a user; and means for storing the new literary work generated by the generating means.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] When a famous author passes away, it is difficult to meet the demands of fans who want to read their new works. Conventional technologies have the problem of being unable to create new works from existing works, which means that they are unable to meet the expectations of fans. For this reason, there is a need for a system that uses artificial intelligence models to generate new works based on existing literary works. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including: means for reading an existing literary work from an input device; analysis means for analyzing the text of the literary work read from the input device; generation means for generating a new literary work using the text data extracted by the analysis means based on prompts provided by a user; and storage means for saving the new literary work generated by the generation means. Specifically, an existing work in PDF format is analyzed on a server, and an AI model generates a new work based on prompts specified by the user, and the generated work is saved, allowing users to enjoy works by new authors.

[0006] An "input device" is a device or software used to read an existing literary work.

[0007] A "reading means" is a method or process for obtaining data from an existing literary work from an input device.

[0008] "Analytical methods" are methods or processes for analyzing the text of a literary work and extracting meaning and structure.

[0009] The "generator" is a method or process that utilizes the text data extracted by the analyzer to generate a new literary work based on prompts provided by a user.

[0010] "Preservation means" are methods or processes for preserving new literary works generated by the generation means.

[0011] A "prompt" is an instruction or storyline outline that a user provides when generating a new literary work.

[0012] An "artificial intelligence model" is a machine learning algorithm or neural network used to generate new text based on existing text data.

[0013] A "literary work" is a created text such as a novel, poem, or essay.

[0014] "PDF format" is an abbreviation for Portable Document Format, a file format for displaying and printing documents. [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] The system of the present invention generates new literary works based on existing literary works. Below, we will generate a program for this system and explain its processing in natural language. We will also show how to put the invention into practice using concrete examples.

[0037] Program processing

[0038] 1. User uploads a PDF file

[0039] The user terminal specifies a PDF file of an existing literary work by the author they want to read and uploads it to the system. This PDF file is then sent to the server, which verifies the file's existence and reads its contents.

[0040] 2. The server extracts the text from the PDF file.

[0041] The server extracts text from the uploaded PDF file, which involves using an AI model to analyze and efficiently extract the text data within the PDF.

[0042] 3. User enters story prompt

[0043] The user then inputs the plot or storyline of a new literary work into the system as a prompt, which acts as a guideline for generating the new work.

[0044] 4. The server uses AI to generate new novels

[0045] The AI ​​model deployed on the server generates new literary works based on the extracted existing text data and the user's story prompts. In this process, the AI ​​generates new text by imitating the author's writing style and literary style.

[0046] 5. The server saves the new novel.

[0047] The server saves the generated new story as a text file, which is saved in a specified location for the user to access later.

[0048] Specific examples

[0049] As a concrete example, consider the case where a user wants to read a new work by "Author A." First, the user uploads "Author A's" existing work, "Existing Work.pdf," to the system. Next, the user enters "Sequel 10 Years Later" as a prompt for the new story. Based on this information, the server extracts text from "Existing Work.pdf" and uses an AI model to generate a new literary work. The generated new literary work is saved as "New Work.txt," which the user can download and read.

[0050] This system provides an environment where fans can enjoy new works by deceased authors, meeting their demands.

[0051] The processing flow will be explained below.

[0052] Step 1:

[0053] The user selects a PDF file of an existing literary work. The user terminal uploads the specified PDF file to the server. For example, the user selects "Existing Work.pdf" and uploads it to the system.

[0054] Step 2:

[0055] The server checks whether the uploaded PDF file exists. If the file does not exist, it returns an error message. If the file exists, the server reads the file contents. The server stores the read PDF file in memory in binary format.

[0056] Step 3:

[0057] The server extracts text from the PDF file. The server uses an AI model to analyze the PDF's content and efficiently extract the text data. For example, the AI ​​model extracts text from each page and summarizes it.

[0058] Step 4:

[0059] The user inputs a prompt outlining the storyline or plot of the new work, for example, "a sequel set 10 years later." The user terminal then sends this prompt to the server.

[0060] Step 5:

[0061] The server then passes the extracted text data and user-provided prompts to the AI ​​model to generate a new literary work. The AI ​​model then generates the new work while imitating the writing style of the existing work. For example, the AI ​​model may add new episodes or events related to the characters based on the prompts.

[0062] Step 6:

[0063] The server saves the new work as a text file. The location can be specified by the user. For example, the new work can be saved as "New Work.txt".

[0064] Step 7:

[0065] The user can download the newly created work from the specified save location. The user can download the "New Work.txt" file on their device and read its contents.

[0066] This series of steps allows users to enjoy new works by deceased authors.

[0067] Example 1

[0068] 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."

[0069] The process of generating new literary works based on existing works requires a lot of manual work and is inefficient. It is also difficult to automatically generate new works that faithfully imitate the style and writing style of existing works. Therefore, there is a need for a system that can easily generate new literary works with the content desired by users.

[0070] 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.

[0071] In this invention, the server includes a means for a user to upload a PDF file, a means for the server to check the existence of the PDF file and read its contents, a means for the server to extract text from the PDF file, a means for the user to input a story prompt, a means for the server to generate a new literary work using a generative AI model, and a means for the server to save the generated new literary work, thereby enabling a user to easily and efficiently generate a new literary work based on an existing literary work.

[0072] "User" means an individual or entity that utilizes the system to upload existing literary works and enter story prompts to generate new literary works.

[0073] A "PDF file" is a standard electronic document format in which existing literary works are stored.

[0074] The "server" is a computer system that receives data from users, processes PDF files, generates new literary works using generative AI models, and stores the results.

[0075] "Text extraction" is the process of extracting textual information from a PDF file and visualizing it.

[0076] A "story prompt" is a narrative guideline or setting that a user provides to generate a new literary work.

[0077] A "generative AI model" is an artificial intelligence algorithm that automatically generates new literary works based on given text data or prompts.

[0078] A "new literary work" is a document newly created by a generative AI model based on existing text data and story prompts.

[0079] "Preservation" is the process of recording new literary works in digital form on a storage device so that they can be accessed at a later time.

[0080] "OCR technology" refers to optical character recognition technology, a technology used to extract text data from scanned images and PDFs.

[0081] The system according to the present invention generates new literary works based on existing literary works. To implement this system, the following hardware and software are used.

[0082] Users access the system's web interface using their own devices and select PDF files of existing literary works by the author they want to read. Once the file selection is complete, they click the "Upload" button, and the device sends the PDF files to the server.

[0083] When a server receives a PDF file, it verifies its existence and reads its contents into memory. The server then uses AI models such as Tesseract OCR or Adobe PDF Extractor to extract text data from the PDF file. The extracted text data is then stored in storage for use in subsequent processing steps.

[0084] The user then enters a prompt into the system's interface describing the plot or storyline of the new literary work, such as "a sequel set 10 years later" or "the main characters embark on a new adventure," and clicks the "Submit" button. The prompt is then sent to the server.

[0085] Based on the submitted prompt and the extracted text data, a generative AI model (e.g., OpenAI's GPT-4) deployed on the server generates a new literary work. The generative AI model receives existing text data and the prompt as input and generates new content based on it.

[0086] The server saves the new literary work as a text file named "New Work.txt" in a designated directory accessible to the user. Once saved, the server notifies the user that a new work is available and allows them to download and read it.

[0087] As a concrete example, consider the case where a user wants to read a new work by "Author A." First, the user uploads "Author A's" existing work, "Existing Work.pdf," to the system. Next, the user enters "Sequel 10 Years Later" as a prompt for the new story. Based on this information, the server extracts text from "Existing Work.pdf" and generates a new literary work using a generative AI model. The generated new literary work is saved as "New Work.txt," which the user can download and read.

[0088] This system provides an environment where fans can enjoy new works by deceased authors, meeting their demands.

[0089] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0090] Step 1:

[0091] A user uploads a PDF file. The user accesses the system's web interface using a terminal, selects an existing PDF file of a literary work, and clicks the "Upload" button. This operation sends the PDF file from the terminal to the server. The input is a PDF file, and the output is a PDF file saved in a specified directory on the server.

[0092] Step 2:

[0093] The server checks whether the PDF file exists and reads its contents. The server checks whether the received PDF file is saved correctly in the specified directory. If the file exists, the server reads its contents into memory. The input is the PDF file saved in the server, and the output is the contents of the PDF file read into the server's memory.

[0094] Step 3:

[0095] The server extracts text from PDF files. The server uses AI models such as Tesseract OCR and Adobe PDF Extractor to extract text data. Specifically, it analyzes character information from scanned images of PDF files and extracts it as text. The input is the scanned image of the PDF file, and the output is the extracted text data.

[0096] Step 4:

[0097] User Enters Story Prompt: The user enters a prompt in the web interface that describes the plot or storyline of a new literary work, such as "A sequel set 10 years later" or "The main characters go on a new adventure," and clicks the "Submit" button. The input is the prompt entered by the user, and the output is the prompt sent to the server.

[0098] Step 5:

[0099] The server generates a new literary work using a generative AI model. The server generates a new literary work using a generative AI model (e.g., OpenAI's GPT-4) based on the extracted existing text data and the prompt sentence entered by the user. The input is the extracted text data and the prompt sentence, and the output is the text of the generated new literary work.

[0100] Step 6:

[0101] The server saves the generated new literary work. The server saves the generated new literary work as "New Work.txt" in a specified directory. The input is the text of the generated new literary work, and the output is a text file of the new literary work saved in the specified directory. Users can download and read the new literary work from this directory.

[0102] Through the above processing steps, the system can efficiently provide new literary works to the user.

[0103] (Application example 1)

[0104] 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."

[0105] In systems that generate new literary works based on existing works, a mechanism is required that allows users to easily view and download the generated works. Another challenge is to improve the user experience by promptly notifying users of the generated works.

[0106] 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.

[0107] In this invention, the server includes: means for reading an existing literary work from an input device; analysis means for analyzing the text of the literary work read from the input device; generation means for generating a new literary work using the text data extracted by the analysis means based on a prompt provided by a user; storage means for saving the new literary work generated by the generation means; and distribution means for notifying the user of the generated new literary work and enabling the user to view or download the generated work through a smartphone application. This allows the user to quickly receive the generated new literary work and easily view or download it.

[0108] An "input device" is a device that a user uses to provide existing literary works to the system.

[0109] The "reading means" is a means for executing a process of importing a literary work provided via an input device into the system.

[0110] "Analysis means" refers to a means for extracting text data from the literary work that has been read and analyzing its content.

[0111] The "generation means" is a means for generating a new literary work based on the text data extracted by the analysis means and prompts provided by the user.

[0112] The "storage means" is a means for storing within the system the new literary work generated by the generation means.

[0113] "Distribution means" refers to the means by which users are notified of new stored literary works and are able to view or download them through a smartphone application.

[0114] A "prompt" is a user-provided text that serves as a guideline or theme for the generation of a new literary work.

[0115] An "artificial intelligence model" is a machine learning algorithm used to generate new texts that mimic the writing style of existing literary works.

[0116] A "smartphone application" is application software for a mobile device that a user uses to view or download newly generated literary works.

[0117] A system for implementing the present invention is configured as follows.

[0118] First, the user uses the smartphone application to read an existing literary work from an input device (such as a smartphone or tablet), and can specify a PDF file and upload it to the system.

[0119] The server side checks for the existence of the uploaded PDF file, reads its contents, and uses analytical techniques to extract text from the PDF file. This involves using an AI model to efficiently parse and extract text data from the PDF. Specifically, it uses a library such as PyPDF2 to extract text from the PDF.

[0120] Next, the user enters a prompt into the smartphone application that will become the plot or storyline of the new literary work, which will set the theme and direction of the user's intended new work. For example, the user could enter the prompt "A sequel set 10 years later."

[0121] On the server side, a generator is used to generate new literary works based on the existing text data extracted by the analysis tool and the user's story prompts. This generator is performed using a generative AI model such as OpenAI's GPT-3. The AI ​​model imitates the writing style of existing works to generate new literary works.

[0122] The generated new literary work is saved as a text file in a specified location on the server, and then notified to the user through a distribution method. The user can then view or download the generated new work through a smartphone application.

[0123] As a specific example, the process is carried out as follows.

[0124] 1. User Input:

[0125] PDF: “existing_literature.pdf”

[0126] Prompt: "A sequel set 10 years later."

[0127] 2. PDF Text Extraction:

[0128] Text: "This is the content of an existing literary work..."

[0129] 3. AI model prompt:

[0130] existing_text + "\n\nSequel 10 years later"

[0131] The server generates, stores, and distributes new literary works based on the user-provided PDF files and prompts, making them readily accessible to users.

[0132] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0133] Step 1:

[0134] A user uploads a PDF file of an existing literary work to a server using a smartphone application.

[0135] Input: A PDF file of an existing literary work (e.g., "existing_literature.pdf")

[0136] How it works: A user uses the upload function of a smartphone application to select a PDF file and send it to a server.

[0137] Output: A PDF file is saved on the server.

[0138] Step 2:

[0139] The server checks for the existence of the uploaded PDF file and extracts the text.

[0140] Input: PDF file uploaded in Step 1

[0141] How it works: The server uses a PDF parsing library such as PyPDF2 to extract text data from a PDF file. It opens the PDF file and extracts the text from each page sequentially.

[0142] Output: Extracted text data (e.g., "This is the content of an existing literary work...")

[0143] Step 3:

[0144] A user enters a prompt for a new literary work through a smartphone application.

[0145] Input: The prompt text that the user enters (e.g., "The sequel 10 years later")

[0146] How it works: Through a smartphone application, the user enters a prompt statement that describes the plot or theme of a new work into the input field and presses the send button.

[0147] Output: The user's prompt is sent to the server.

[0148] Step 4:

[0149] The server generates new literary works based on the extracted text data and user prompts.

[0150] Input: Text data extracted in step 2, prompt sent in step 3

[0151] How it works: The server uses a generative AI model such as OpenAI's GPT-3 to generate a new literary work by integrating the extracted text data and the prompt sentence. It generates a complete_prompt sentence for the AI ​​model and executes the generation process by calling the API.

[0152] Output: The newly generated literary work (in text format)

[0153] Step 5:

[0154] The server saves the generated new literary work as a text file.

[0155] Input: The new literary work generated in step 4

[0156] Operation: The server saves the generated text data as a text file in the specified location.

[0157] Output: The path to the saved text file (e.g., " / path / to / generated_literature.txt")

[0158] Step 6:

[0159] The server notifies users of the existence of the generated literary works, allowing them to view and download them through a smartphone application.

[0160] Input: The file path of the new literary work saved in step 5

[0161] How it works: The server notifies the user that a new literary work has been created and delivers the path to the text file to the smartphone application. The user receives the notification and opens the application to view or download the work.

[0162] Output: The user can view or download the new literary work generated in a smartphone application.

[0163] 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.

[0164] The present invention is a system for recognizing a user's emotional state and generating new literary works based on that state. In addition to the basic function of reading existing literary works and analyzing them to generate new works, the system can also recognize the user's emotions using an emotion engine and adjust the tone and style of the resulting work. Below, we will generate a program for this system and explain its processing in natural language. We will also demonstrate how the invention can be put into practice using concrete examples.

[0165] Program processing

[0166] 1. User uploads a PDF file

[0167] The user terminal specifies a PDF file of an existing literary work by the author they want to read and uploads it to the system. This PDF file is then sent to the server, which verifies the file's existence and reads its contents.

[0168] 2. The server extracts the text from the PDF file.

[0169] The server extracts text from the uploaded PDF file, which involves using an AI model to analyze and efficiently extract the text data within the PDF.

[0170] 3. User enters story prompt

[0171] The user then inputs the plot or storyline of a new literary work into the system as a prompt, which acts as a guideline for generating the new work.

[0172] 4. The server recognizes the user's emotions

[0173] The server recognizes the user's emotional state using an emotion engine, which analyzes the user's facial expressions and voice input to determine the user's current emotional state.

[0174] 5. The server generates new literary works based on emotions.

[0175] The server uses an AI model to generate new literary works based on the user's perceived emotional state. The AI ​​model adjusts the tone and style of the work depending on the user's emotions. In this process, the AI ​​mimics the author's writing style and literary style to generate new text.

[0176] 6. The server saves the new novel.

[0177] The server saves the generated new novel as a text file. This file is saved in a specified location for the user to access later. For example, the generated new literary work is saved as "New Work.txt."

[0178] 7. User gets new novel

[0179] The user can download the new novel from the specified location and begin reading it. The user can then download the "New Work.txt" file to their device and enjoy the content.

[0180] Specific examples

[0181] For example, suppose a user uploads an existing literary work, "Existing Work.pdf," to the system and enters "Sequel 10 Years Later" as the new story prompt. The emotion engine then recognizes the user's current emotional state as "Joy." As a result, the system adjusts the tone and style of the work according to the user's emotion of "Joy" and generates a new literary work that is bright and hopeful. This new work is then saved as "New Work.txt" for the user to download and enjoy.

[0182] This invention not only allows users to enjoy new works by deceased authors, but also provides a special experience tailored to their own emotional state.

[0183] The processing flow will be explained below.

[0184] Step 1:

[0185] The user selects a PDF file of an existing literary work and uploads it to the system from the user terminal. For example, the user selects "Existing Work.pdf" and sends the file to the system.

[0186] Step 2:

[0187] The server checks whether the uploaded PDF file exists. If the file does not exist, it returns an error message. If the file exists, the server reads its contents. The server stores the read PDF file in memory in binary format.

[0188] Step 3:

[0189] The server extracts text from the uploaded PDF file. The server uses an AI model to analyze the PDF's content and efficiently extract the text data, for example, extracting the text from each page and summarizing it as a whole.

[0190] Step 4:

[0191] The user inputs a prompt outlining the storyline or plot of the new work, for example, "a sequel set 10 years later." The user terminal then sends this prompt to the server.

[0192] Step 5:

[0193] The server recognizes the user's emotional state using an emotion engine, which analyzes the user's facial expressions and voice input to determine the user's current emotion, for example, by using data from a camera or microphone to detect the user's emotion.

[0194] Step 6:

[0195] The server uses an AI model to generate new literary works based on the user's perceived emotional state. The AI ​​model uses existing text data and prompts to create new works with a tone and style that matches the user's emotions. For example, if the user is feeling "joy," the server generates text with an upbeat and hopeful feel.

[0196] Step 7:

[0197] The server saves the new novel as a text file. The user can specify the location. For example, it can be saved as "New Work.txt."

[0198] Step 8:

[0199] The user downloads the new novel from the specified location. The user can then download the file "New Work.txt" to their device and read the contents.

[0200] This series of steps allows users to not only enjoy new works by deceased authors, but also experience literary works tailored to their own emotional state.

[0201] Example 2

[0202] 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."

[0203] With conventional document generation systems, it was difficult for users to reflect their own emotions and current state when creating a new document. Furthermore, when creating a new work based on an existing document, it was difficult to accurately reproduce the tone and style unique to that document. These issues led to a decrease in user satisfaction.

[0204] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0205] In this invention, the server includes means for reading an existing document from an input device, means for analyzing the text of the document read from the input device, means for generating a new document using the text data extracted by the analysis means based on instructions provided by a user, means for recognizing the emotional state of the user, means for adjusting the tone and style of the generated document based on the recognized emotional state, means for saving the generated new document, and means for providing the new document to the user, thereby making it possible to generate a new document that reflects the emotional state of the user.

[0206] An "input device" is a device for inputting existing document data into a computer system.

[0207] "Analysis means" refers to a technical method for extracting text information from imported document data and understanding its content.

[0208] The "generation means" is a technical method for creating a new document based on the extracted text data and instructions from the user.

[0209] "Emotional state" refers to the psychological and emotional state of the user as recognized from their facial expressions and voice.

[0210] "Means of recognition" refers to the technical methods and algorithms used to analyze and determine the user's emotional state.

[0211] "Tone and style" refers to the mood and writing characteristics of a document, which change depending on the emotional state.

[0212] "Storage means" refers to the technical method for storing the newly generated document as data and making it accessible later.

[0213] The "means of provision" refers to the technical method used to distribute the stored document data in a form that allows users to view and download it.

[0214] The present invention is a system for recognizing a user's emotional state and generating new literary works based on that. In addition to the basic function of reading an existing document from an input device and analyzing its content to generate a new work, the system can also recognize the user's emotions using an emotion engine and adjust the tone and style of the generated work.

[0215] Hardware and software used

[0216] Hardware

[0217] Server: The central processing device that analyzes documents, recognizes emotions, and generates new documents.

[0218] User device: A device that uploads documents and provides emotion data. This includes PCs, smartphones, tablets, etc.

[0219] Input device: A device for uploading PDF files. Typically, this corresponds to the file input function of a PC or smartphone.

[0220] software

[0221] Document analysis software: Used to extract text from PDFs, for example, Tesseract OCR.

[0222] Emotion recognition engine: Used to analyze user emotions. For example, Microsoft Azure's Emotion API is used.

[0223] Generative AI models: used to generate new documents, such as OpenAI's GPT-4.

[0224] Process Overview

[0225] 1. User uploads a PDF file

[0226] A user terminal uploads a PDF file containing an existing literary work by a particular author to the system, which then sends the uploaded PDF file to the server, which verifies the file's existence and reads its contents.

[0227] 2. The server extracts the text from the PDF file.

[0228] The server extracts text from the uploaded PDF file using document analysis software such as Tesseract OCR, which efficiently extracts the text data from the PDF.

[0229] 3. User enters story prompt

[0230] The user inputs the plot or storyline of a new literary work into the system as a prompt, providing a specific prompt such as "a sequel set 10 years later."

[0231] 4. The server recognizes the user's emotions

[0232] The server recognizes the user's emotional state using an emotion recognition engine that analyzes the user's facial expressions and voice to determine the user's current emotional state.

[0233] 5. The server generates a new document based on the emotion.

[0234] The server uses a generative AI model (e.g., GPT-4) to generate new literary works based on the user's emotional state. The AI ​​model mimics the author's writing style and adjusts the tone and style of the work depending on the user's emotions.

[0235] 6. The server saves the new document.

[0236] The server saves the generated new literary work as a text file called "New Work.txt" in a specified location for later access by the user.

[0237] 7. User retrieves new document

[0238] The user can download the generated new literary work from the specified location and begin reading it. The user can then download the file "New Work.txt" to their device and enjoy its contents.

[0239] Specific examples

[0240] For example, suppose a user uploads an existing literary work by a particular author ("Existing Work.pdf") to the system and enters "A Sequel 10 Years Later" as the new story prompt. The server uses an emotion engine to recognize the user's current emotional state as "Joy." As a result, the system adjusts the tone and style of the work according to the user's emotion of "Joy," generating a new literary work that is bright and hopeful. This new work is saved as "New Work.txt," and the user can download and enjoy it.

[0241] The above is a detailed description of the embodiment of the present invention.

[0242] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0243] Step 1:

[0244] User uploads a PDF file

[0245] A user terminal uploads a PDF file, which is an existing literary work by a specific author, to the system. This operation is performed by clicking the file selection button on the user interface and selecting the specified PDF file. As input, the PDF file selected by the user is given and sent to the server. The output is the PDF file received by the server.

[0246] Step 2:

[0247] The server extracts the text from the PDF file.

[0248] The server extracts text from the uploaded PDF file. Specifically, it uses document analysis software such as Tesseract OCR. The PDF file stored on the server is given as input, and text data is extracted from the PDF file. The output is the extracted text data. This text extraction operation is performed using text recognition processing using computer vision technology.

[0249] Step 3:

[0250] User enters story prompt

[0251] A user inputs the plot or storyline of a new literary work by entering text into input fields on a Web form and clicking a "Submit" button. The input is a text prompt provided by the user. The output is the prompt received by the server.

[0252] Step 4:

[0253] The server recognizes the user's emotions

[0254] The server uses an emotion recognition engine to recognize the user's emotional state. The server receives and analyzes facial and voice data sent from the user's device to determine the user's emotional state. The user's facial and voice data are given as input, and the emotion engine identifies the user's emotional state based on this. The output is the recognized user's emotional state.

[0255] Step 5:

[0256] The server generates new documents based on the emotion.

[0257] The server uses a generative AI model (e.g., GPT-4) to generate a new literary work based on the user's emotional state. The extracted text data, user-provided prompts, and the recognized emotional state are given as input. The generative AI model takes these data as input and generates the text of the new literary work. The output is the generated text of the new literary work. In this generation process, the generative AI model mimics the author's writing style, incorporating tone and style according to the emotion.

[0258] Step 6:

[0259] The server saves the new generated document.

[0260] The server saves the generated new literary work as a text file. As input, it takes the text of the generated new document and performs an operation to save it in a specific location. The output is a saved text file (e.g., "New Work.txt") in a directory that can be accessed by the user at a later date.

[0261] Step 7:

[0262] User gets new document

[0263] The user downloads the generated new literary work from the specified location and begins reading it. As input, the user is provided with the path to the saved text file and performs an operation to download it from the user's device. The output is the new document downloaded to the user's device. This operation is performed by clicking the download link on the user interface.

[0264] (Application example 2)

[0265] 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."

[0266] Conventional literary production generation systems have difficulty automatically generating works that reflect the user's emotional state. Furthermore, because they do not adjust the tone or style according to the user's emotions, the works provided often do not meet the user's expectations. This limits the quality of the user experience.

[0267] 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: means for reading an existing literary work from an input device; analysis means for analyzing the text of the literary work read from the input device; generation means for generating a new literary work using the text data extracted by the analysis means based on a prompt provided by the user; storage means for saving the new literary work generated by the generation means; and emotion recognition means for recognizing the emotional state of the user and adjusting the generation means based on the emotional state. This makes it possible to automatically generate a new literary work with a tone and style that matches the emotional state of the user.

[0268] "Existing literary works" are literary content that already exists, including books, stories, poems, etc.

[0269] An "input device" is a device that allows a user to supply data to a system, such as a PC, smartphone, or tablet.

[0270] The "reading means" has the function of receiving data from an input device and converting it into a format that can be used within the system.

[0271] The "analysis means" has the function of analyzing input text data and extracting necessary information.

[0272] A "prompt" is a user-provided guideline or instruction that indicates the theme or storyline for generating a new literary work.

[0273] A "generator" is a device that has the function of generating a new literary work based on the provided prompt and the analyzed text data.

[0274] "Storage means" are those that have the function of preserving the new literary works produced in digital form.

[0275] The "emotion recognition means" has a function for analyzing and determining the user's emotional state, and uses face recognition technology, voice analysis technology, and the like.

[0276] The invention is a system that recognizes a user's emotional state and generates new literary works based on it.

[0277] System Configuration

[0278] The system uses the following hardware and software components:

[0279] Hardware

[0280] Input device: The device through which the user uploads the PDF file, such as a computer, smartphone, or tablet.

[0281] Camera and microphone: A device for capturing a user's facial expressions and voice. For example, a camera and microphone built into a computer or smartphone can be used.

[0282] software

[0283] PDF Text Extraction Software: Software for extracting text data from PDF files, such as PDFMiner.

[0284] Emotion recognition software: Libraries such as EmotionRecognizer that analyze a user's facial expressions and voice to recognize emotions.

[0285] Generative AI models: AI models for generating new literary works based on the user's emotional state and prompts, such as OpenAI GPT-3.

[0286] Data processing and calculation

[0287] User operations

[0288] First, the user uploads a PDF file of an existing literary work by the author they want to read to the system using their input device, and then they need to enable the camera and microphone to recognize their emotional state.

[0289] Processing on the server

[0290] The server processes the data as follows:

[0291] 1. Extract PDF text:

[0292] The server uses PDFMiner to extract text data from uploaded PDF files, which later serves as the basis for generating new literary works.

[0293] 2. Enter and parse prompts:

[0294] Users input the plot or storyline of a new literary work as a prompt, which is parsed by a generative AI model and used as a guideline for generating the work.

[0295] 3. Emotion recognition:

[0296] The server uses EmotionRecognizer to recognize the user's current emotional state, specifically by using facial images captured by a camera and audio recorded by a microphone to identify the user's emotions.

[0297] 4. The creation of literary works:

[0298] The server uses a generative AI model (e.g., OpenAI GPT-3) to generate new literary works based on the user's perceived emotional state and the provided prompts. The generated works have a tone and style that correspond to the user's emotions.

[0299] 5. Saving Your Generated Work:

[0300] The generated new literary works are stored as text files by the server, so that users can download and read the new literary works at any time.

[0301] Specific examples

[0302] For example, suppose a user uploads "Existing Works.pdf" to the system and enters "A Future Detective Story" as the new story prompt. If the emotion recognition system then recognizes the user's emotional state as "Excited," the system will generate a new Future Detective Story with a tone and style that corresponds to the user's emotion of excitement.

[0303] Prompt example

[0304] Given the base text: The original text from the uploaded PDF, and the emotion: Excitement, generate a new literary work based on the prompt: A detective story set in the future.

[0305] In this way, new literary works tailored to the user's emotions are generated, providing a special reading experience.

[0306] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0307] Step 1:

[0308] The user uploads a PDF file of an existing literary work from an input device. The user provides the PDF file of the existing work to the system using a PC or smartphone. At this stage, the system checks the existence of the file and transfers it to the server.

[0309] Input: PDF files uploaded from a PC or smartphone

[0310] Output: PDF file saved on the server

[0311] Step 2:

[0312] The server uses PDFMiner to extract text data from the uploaded PDF file. The server parses the PDF file and extracts the text for each page and paragraph.

[0313] Input: PDF file stored on the server

[0314] Output: Extracted text data

[0315] Step 3:

[0316] The user inputs the plot or storyline of a new literary work into the system as a prompt, which the terminal sends to the system to use as a guideline for subsequent work generation.

[0317] Input: The prompt text entered by the user

[0318] Output: The prompt sent to the server

[0319] Step 4:

[0320] The server uses EmotionRecognizer to analyze the user's facial expressions and voice data acquired from the camera and microphone of the input device to recognize the user's emotional state. The server performs facial recognition and voiceprint analysis to identify the user's emotions in real time.

[0321] Input: User facial and voice data acquired from a camera and microphone

[0322] Output: Recognized emotional state of the user

[0323] Step 5:

[0324] The server generates new literary works using a generative AI model (e.g., OpenAI GPT-3) based on the recognized emotional state and prompt sentences. The generative AI model generates new text based on the input text data and prompts, adjusting the tone and style according to the user's emotions.

[0325] Input: Recognized emotional state, prompt sentence, extracted text data

[0326] Output: The new literary work generated

[0327] Step 6:

[0328] The server saves the new literary work as a text file, which the user can download and read later. The saved file is stored in a specified directory.

[0329] Input: A new literary work is generated.

[0330] Output: A text file saved on the server

[0331] Step 7:

[0332] A user uses a device to download a text file of a new literary work. The user can retrieve the file from the storage location and begin reading.

[0333] Input: A text file stored on the server

[0334] Output: A text file downloaded to the user's device.

[0335] 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.

[0336] 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.

[0337] 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.

[0338] [Second embodiment]

[0339] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0340] 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.

[0341] 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).

[0342] 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.

[0343] 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.

[0344] 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).

[0345] 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.

[0346] 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.

[0347] 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.

[0348] 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.

[0349] 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.

[0350] 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."

[0351] The system of the present invention generates new literary works based on existing literary works. Below, we will generate a program for this system and explain its processing in natural language. We will also show how to put the invention into practice using concrete examples.

[0352] Program processing

[0353] 1. User uploads a PDF file

[0354] The user terminal specifies a PDF file of an existing literary work by the author they want to read and uploads it to the system. This PDF file is then sent to the server, which verifies the file's existence and reads its contents.

[0355] 2. The server extracts the text from the PDF file.

[0356] The server extracts text from the uploaded PDF file, which involves using an AI model to analyze and efficiently extract the text data within the PDF.

[0357] 3. User enters story prompt

[0358] The user then inputs the plot or storyline of a new literary work into the system as a prompt, which acts as a guideline for generating the new work.

[0359] 4. The server uses AI to generate new novels

[0360] The AI ​​model deployed on the server generates new literary works based on the extracted existing text data and the user's story prompts. In this process, the AI ​​generates new text by imitating the author's writing style and literary style.

[0361] 5. The server saves the new novel.

[0362] The server saves the generated new story as a text file, which is saved in a specified location for the user to access later.

[0363] Specific examples

[0364] As a concrete example, consider the case where a user wants to read a new work by "Author A." First, the user uploads "Author A's" existing work, "Existing Work.pdf," to the system. Next, the user enters "Sequel 10 Years Later" as a prompt for the new story. Based on this information, the server extracts text from "Existing Work.pdf" and uses an AI model to generate a new literary work. The generated new literary work is saved as "New Work.txt," which the user can download and read.

[0365] This system provides an environment where fans can enjoy new works by deceased authors, meeting their demands.

[0366] The processing flow will be explained below.

[0367] Step 1:

[0368] The user selects a PDF file of an existing literary work. The user terminal uploads the specified PDF file to the server. For example, the user selects "Existing Work.pdf" and uploads it to the system.

[0369] Step 2:

[0370] The server checks whether the uploaded PDF file exists. If the file does not exist, it returns an error message. If the file exists, the server reads the file contents. The server stores the read PDF file in memory in binary format.

[0371] Step 3:

[0372] The server extracts text from the PDF file. The server uses an AI model to analyze the PDF's content and efficiently extract the text data. For example, the AI ​​model extracts text from each page and summarizes it.

[0373] Step 4:

[0374] The user inputs a prompt outlining the storyline or plot of the new work, for example, "a sequel set 10 years later." The user terminal then sends this prompt to the server.

[0375] Step 5:

[0376] The server then passes the extracted text data and user-provided prompts to the AI ​​model to generate a new literary work. The AI ​​model then generates the new work while imitating the writing style of the existing work. For example, the AI ​​model may add new episodes or events related to the characters based on the prompts.

[0377] Step 6:

[0378] The server saves the new work as a text file. The location can be specified by the user. For example, the new work can be saved as "New Work.txt".

[0379] Step 7:

[0380] The user can download the newly created work from the specified save location. The user can download the "New Work.txt" file on their device and read its contents.

[0381] This series of steps allows users to enjoy new works by deceased authors.

[0382] Example 1

[0383] 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."

[0384] The process of generating new literary works based on existing works requires a lot of manual work and is inefficient. It is also difficult to automatically generate new works that faithfully imitate the style and writing style of existing works. Therefore, there is a need for a system that can easily generate new literary works with the content desired by users.

[0385] 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.

[0386] In this invention, the server includes a means for a user to upload a PDF file, a means for the server to check the existence of the PDF file and read its contents, a means for the server to extract text from the PDF file, a means for the user to input a story prompt, a means for the server to generate a new literary work using a generative AI model, and a means for the server to save the generated new literary work, thereby enabling a user to easily and efficiently generate a new literary work based on an existing literary work.

[0387] "User" means an individual or entity that utilizes the system to upload existing literary works and enter story prompts to generate new literary works.

[0388] A "PDF file" is a standard electronic document format in which existing literary works are stored.

[0389] The "server" is a computer system that receives data from users, processes PDF files, generates new literary works using generative AI models, and stores the results.

[0390] "Text extraction" is the process of extracting textual information from a PDF file and visualizing it.

[0391] A "story prompt" is a narrative guideline or setting that a user provides to generate a new literary work.

[0392] A "generative AI model" is an artificial intelligence algorithm that automatically generates new literary works based on given text data or prompts.

[0393] A "new literary work" is a document newly created by a generative AI model based on existing text data and story prompts.

[0394] "Preservation" is the process of recording new literary works in digital form on a storage device so that they can be accessed at a later time.

[0395] "OCR technology" refers to optical character recognition technology, a technology used to extract text data from scanned images and PDFs.

[0396] The system according to the present invention generates new literary works based on existing literary works. To implement this system, the following hardware and software are used.

[0397] Users access the system's web interface using their own devices and select PDF files of existing literary works by the author they want to read. Once the file selection is complete, they click the "Upload" button, and the device sends the PDF files to the server.

[0398] When a server receives a PDF file, it verifies its existence and reads its contents into memory. The server then uses AI models such as Tesseract OCR or Adobe PDF Extractor to extract text data from the PDF file. The extracted text data is then stored in storage for use in subsequent processing steps.

[0399] The user then enters a prompt into the system's interface describing the plot or storyline of the new literary work, such as "a sequel set 10 years later" or "the main characters embark on a new adventure," and clicks the "Submit" button. The prompt is then sent to the server.

[0400] Based on the submitted prompt and the extracted text data, a generative AI model (e.g., OpenAI's GPT-4) deployed on the server generates a new literary work. The generative AI model receives existing text data and the prompt as input and generates new content based on it.

[0401] The server saves the new literary work as a text file named "New Work.txt" in a designated directory accessible to the user. Once saved, the server notifies the user that a new work is available and allows them to download and read it.

[0402] As a concrete example, consider the case where a user wants to read a new work by "Author A." First, the user uploads "Author A's" existing work, "Existing Work.pdf," to the system. Next, the user enters "Sequel 10 Years Later" as a prompt for the new story. Based on this information, the server extracts text from "Existing Work.pdf" and generates a new literary work using a generative AI model. The generated new literary work is saved as "New Work.txt," which the user can download and read.

[0403] This system provides an environment where fans can enjoy new works by deceased authors, meeting their demands.

[0404] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0405] Step 1:

[0406] A user uploads a PDF file. The user accesses the system's web interface using a terminal, selects an existing PDF file of a literary work, and clicks the "Upload" button. This operation sends the PDF file from the terminal to the server. The input is a PDF file, and the output is a PDF file saved in a specified directory on the server.

[0407] Step 2:

[0408] The server checks whether the PDF file exists and reads its contents. The server checks whether the received PDF file is saved correctly in the specified directory. If the file exists, the server reads its contents into memory. The input is the PDF file saved in the server, and the output is the contents of the PDF file read into the server's memory.

[0409] Step 3:

[0410] The server extracts text from PDF files. The server uses AI models such as Tesseract OCR and Adobe PDF Extractor to extract text data. Specifically, it analyzes character information from scanned images of PDF files and extracts it as text. The input is the scanned image of the PDF file, and the output is the extracted text data.

[0411] Step 4:

[0412] User Enters Story Prompt: The user enters a prompt in the web interface that describes the plot or storyline of a new literary work, such as "A sequel set 10 years later" or "The main characters go on a new adventure," and clicks the "Submit" button. The input is the prompt entered by the user, and the output is the prompt sent to the server.

[0413] Step 5:

[0414] The server generates a new literary work using a generative AI model. The server generates a new literary work using a generative AI model (e.g., OpenAI's GPT-4) based on the extracted existing text data and the prompt sentence entered by the user. The input is the extracted text data and the prompt sentence, and the output is the text of the generated new literary work.

[0415] Step 6:

[0416] The server saves the generated new literary work. The server saves the generated new literary work as "New Work.txt" in a specified directory. The input is the text of the generated new literary work, and the output is a text file of the new literary work saved in the specified directory. Users can download and read the new literary work from this directory.

[0417] Through the above processing steps, the system can efficiently provide new literary works to the user.

[0418] (Application example 1)

[0419] 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."

[0420] In systems that generate new literary works based on existing works, a mechanism is required that allows users to easily view and download the generated works. Another challenge is to improve the user experience by promptly notifying users of the generated works.

[0421] 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.

[0422] In this invention, the server includes: means for reading an existing literary work from an input device; analysis means for analyzing the text of the literary work read from the input device; generation means for generating a new literary work using the text data extracted by the analysis means based on a prompt provided by a user; storage means for saving the new literary work generated by the generation means; and distribution means for notifying the user of the generated new literary work and enabling the user to view or download the generated work through a smartphone application. This allows the user to quickly receive the generated new literary work and easily view or download it.

[0423] An "input device" is a device that a user uses to provide existing literary works to the system.

[0424] The "reading means" is a means for executing a process of importing a literary work provided via an input device into the system.

[0425] "Analysis means" refers to a means for extracting text data from the literary work that has been read and analyzing its content.

[0426] The "generation means" is a means for generating a new literary work based on the text data extracted by the analysis means and prompts provided by the user.

[0427] The "storage means" is a means for storing within the system the new literary work generated by the generation means.

[0428] "Distribution means" refers to the means by which users are notified of new stored literary works and are able to view or download them through a smartphone application.

[0429] A "prompt" is a user-provided text that serves as a guideline or theme for the generation of a new literary work.

[0430] An "artificial intelligence model" is a machine learning algorithm used to generate new texts that mimic the writing style of existing literary works.

[0431] A "smartphone application" is application software for a mobile device that a user uses to view or download newly generated literary works.

[0432] A system for implementing the present invention is configured as follows.

[0433] First, the user uses the smartphone application to read an existing literary work from an input device (such as a smartphone or tablet), and can specify a PDF file and upload it to the system.

[0434] The server side checks for the existence of the uploaded PDF file, reads its contents, and uses analytical techniques to extract text from the PDF file. This involves using an AI model to efficiently parse and extract text data from the PDF. Specifically, it uses a library such as PyPDF2 to extract text from the PDF.

[0435] Next, the user enters a prompt into the smartphone application that will become the plot or storyline of the new literary work, which will set the theme and direction of the user's intended new work. For example, the user could enter the prompt "A sequel set 10 years later."

[0436] On the server side, a generator is used to generate new literary works based on the existing text data extracted by the analysis tool and the user's story prompts. This generator is performed using a generative AI model such as OpenAI's GPT-3. The AI ​​model imitates the writing style of existing works to generate new literary works.

[0437] The generated new literary work is saved as a text file in a specified location on the server, and then notified to the user through a distribution method. The user can then view or download the generated new work through a smartphone application.

[0438] As a specific example, the process is carried out as follows.

[0439] 1. User Input:

[0440] PDF: “existing_literature.pdf”

[0441] Prompt: "A sequel set 10 years later."

[0442] 2. PDF Text Extraction:

[0443] Text: "This is the content of an existing literary work..."

[0444] 3. AI model prompt:

[0445] existing_text + "\n\nSequel 10 years later"

[0446] The server generates, stores, and distributes new literary works based on the user-provided PDF files and prompts, making them readily accessible to users.

[0447] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0448] Step 1:

[0449] A user uploads a PDF file of an existing literary work to a server using a smartphone application.

[0450] Input: A PDF file of an existing literary work (e.g., "existing_literature.pdf")

[0451] How it works: A user uses the upload function of a smartphone application to select a PDF file and send it to a server.

[0452] Output: A PDF file is saved on the server.

[0453] Step 2:

[0454] The server checks for the existence of the uploaded PDF file and extracts the text.

[0455] Input: PDF file uploaded in Step 1

[0456] How it works: The server uses a PDF parsing library such as PyPDF2 to extract text data from a PDF file. It opens the PDF file and extracts the text from each page sequentially.

[0457] Output: Extracted text data (e.g., "This is the content of an existing literary work...")

[0458] Step 3:

[0459] A user enters a prompt for a new literary work through a smartphone application.

[0460] Input: The prompt text that the user enters (e.g., "The sequel 10 years later")

[0461] How it works: Through a smartphone application, the user enters a prompt statement that describes the plot or theme of a new work into the input field and presses the send button.

[0462] Output: The user's prompt is sent to the server.

[0463] Step 4:

[0464] The server generates new literary works based on the extracted text data and user prompts.

[0465] Input: Text data extracted in step 2, prompt sent in step 3

[0466] How it works: The server uses a generative AI model such as OpenAI's GPT-3 to generate a new literary work by integrating the extracted text data and the prompt sentence. It generates a complete_prompt sentence for the AI ​​model and executes the generation process by calling the API.

[0467] Output: The newly generated literary work (in text format)

[0468] Step 5:

[0469] The server saves the generated new literary work as a text file.

[0470] Input: The new literary work generated in step 4

[0471] Operation: The server saves the generated text data as a text file in the specified location.

[0472] Output: The path to the saved text file (e.g., " / path / to / generated_literature.txt")

[0473] Step 6:

[0474] The server notifies users of the existence of the generated literary works, allowing them to view and download them through a smartphone application.

[0475] Input: The file path of the new literary work saved in step 5

[0476] How it works: The server notifies the user that a new literary work has been created and delivers the path to the text file to the smartphone application. The user receives the notification and opens the application to view or download the work.

[0477] Output: The user can view or download the new literary work generated in a smartphone application.

[0478] 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.

[0479] The present invention is a system for recognizing a user's emotional state and generating new literary works based on that state. In addition to the basic function of reading existing literary works and analyzing them to generate new works, the system can also recognize the user's emotions using an emotion engine and adjust the tone and style of the resulting work. Below, we will generate a program for this system and explain its processing in natural language. We will also demonstrate how the invention can be put into practice using concrete examples.

[0480] Program processing

[0481] 1. User uploads a PDF file

[0482] The user terminal specifies a PDF file of an existing literary work by the author they want to read and uploads it to the system. This PDF file is then sent to the server, which verifies the file's existence and reads its contents.

[0483] 2. The server extracts the text from the PDF file.

[0484] The server extracts text from the uploaded PDF file, which involves using an AI model to analyze and efficiently extract the text data within the PDF.

[0485] 3. User enters story prompt

[0486] The user then inputs the plot or storyline of a new literary work into the system as a prompt, which acts as a guideline for generating the new work.

[0487] 4. The server recognizes the user's emotions

[0488] The server recognizes the user's emotional state using an emotion engine, which analyzes the user's facial expressions and voice input to determine the user's current emotional state.

[0489] 5. The server generates new literary works based on emotions.

[0490] The server uses an AI model to generate new literary works based on the user's perceived emotional state. The AI ​​model adjusts the tone and style of the work depending on the user's emotions. In this process, the AI ​​mimics the author's writing style and literary style to generate new text.

[0491] 6. The server saves the new novel.

[0492] The server saves the generated new novel as a text file. This file is saved in a specified location for the user to access later. For example, the generated new literary work is saved as "New Work.txt."

[0493] 7. User gets new novel

[0494] The user can download the new novel from the specified location and begin reading it. The user can then download the "New Work.txt" file to their device and enjoy the content.

[0495] Specific examples

[0496] For example, suppose a user uploads an existing literary work, "Existing Work.pdf," to the system and enters "Sequel 10 Years Later" as the new story prompt. The emotion engine then recognizes the user's current emotional state as "Joy." As a result, the system adjusts the tone and style of the work according to the user's emotion of "Joy" and generates a new literary work that is bright and hopeful. This new work is then saved as "New Work.txt" for the user to download and enjoy.

[0497] This invention not only allows users to enjoy new works by deceased authors, but also provides a special experience tailored to their own emotional state.

[0498] The processing flow will be explained below.

[0499] Step 1:

[0500] The user selects a PDF file of an existing literary work and uploads it to the system from the user terminal. For example, the user selects "Existing Work.pdf" and sends the file to the system.

[0501] Step 2:

[0502] The server checks whether the uploaded PDF file exists. If the file does not exist, it returns an error message. If the file exists, the server reads its contents. The server stores the read PDF file in memory in binary format.

[0503] Step 3:

[0504] The server extracts text from the uploaded PDF file. The server uses an AI model to analyze the PDF's content and efficiently extract the text data, for example, extracting the text from each page and summarizing it as a whole.

[0505] Step 4:

[0506] The user inputs a prompt outlining the storyline or plot of the new work, for example, "a sequel set 10 years later." The user terminal then sends this prompt to the server.

[0507] Step 5:

[0508] The server recognizes the user's emotional state using an emotion engine, which analyzes the user's facial expressions and voice input to determine the user's current emotion, for example, by using data from a camera or microphone to detect the user's emotion.

[0509] Step 6:

[0510] The server uses an AI model to generate new literary works based on the user's perceived emotional state. The AI ​​model uses existing text data and prompts to create new works with a tone and style that matches the user's emotions. For example, if the user is feeling "joy," the server generates text with an upbeat and hopeful feel.

[0511] Step 7:

[0512] The server saves the new novel as a text file. The user can specify the location. For example, it can be saved as "New Work.txt."

[0513] Step 8:

[0514] The user downloads the new novel from the specified location. The user can then download the file "New Work.txt" to their device and read the contents.

[0515] This series of steps allows users to not only enjoy new works by deceased authors, but also experience literary works tailored to their own emotional state.

[0516] Example 2

[0517] 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."

[0518] With conventional document generation systems, it was difficult for users to reflect their own emotions and current state when creating a new document. Furthermore, when creating a new work based on an existing document, it was difficult to accurately reproduce the tone and style unique to that document. These issues led to a decrease in user satisfaction.

[0519] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0520] In this invention, the server includes means for reading an existing document from an input device, means for analyzing the text of the document read from the input device, means for generating a new document using the text data extracted by the analysis means based on instructions provided by a user, means for recognizing the emotional state of the user, means for adjusting the tone and style of the generated document based on the recognized emotional state, means for saving the generated new document, and means for providing the new document to the user, thereby making it possible to generate a new document that reflects the emotional state of the user.

[0521] An "input device" is a device for inputting existing document data into a computer system.

[0522] "Analysis means" refers to a technical method for extracting text information from imported document data and understanding its content.

[0523] The "generation means" is a technical method for creating a new document based on the extracted text data and instructions from the user.

[0524] "Emotional state" refers to the psychological and emotional state of the user as recognized from their facial expressions and voice.

[0525] "Means of recognition" refers to the technical methods and algorithms used to analyze and determine the user's emotional state.

[0526] "Tone and style" refers to the mood and writing characteristics of a document, which change depending on the emotional state.

[0527] "Storage means" refers to the technical method for storing the newly generated document as data and making it accessible later.

[0528] The "means of provision" refers to the technical method used to distribute the stored document data in a form that allows users to view and download it.

[0529] The present invention is a system for recognizing a user's emotional state and generating new literary works based on that. In addition to the basic function of reading an existing document from an input device and analyzing its content to generate a new work, the system can also recognize the user's emotions using an emotion engine and adjust the tone and style of the generated work.

[0530] Hardware and software used

[0531] Hardware

[0532] Server: The central processing device that analyzes documents, recognizes emotions, and generates new documents.

[0533] User device: A device that uploads documents and provides emotion data. This includes PCs, smartphones, tablets, etc.

[0534] Input device: A device for uploading PDF files. Typically, this corresponds to the file input function of a PC or smartphone.

[0535] software

[0536] Document analysis software: Used to extract text from PDFs, for example, Tesseract OCR.

[0537] Emotion recognition engine: Used to analyze user emotions. For example, Microsoft Azure's Emotion API is used.

[0538] Generative AI models: used to generate new documents, such as OpenAI's GPT-4.

[0539] Process Overview

[0540] 1. User uploads a PDF file

[0541] A user terminal uploads a PDF file containing an existing literary work by a particular author to the system, which then sends the uploaded PDF file to the server, which verifies the file's existence and reads its contents.

[0542] 2. The server extracts the text from the PDF file.

[0543] The server extracts text from the uploaded PDF file using document analysis software such as Tesseract OCR, which efficiently extracts the text data from the PDF.

[0544] 3. User enters story prompt

[0545] The user inputs the plot or storyline of a new literary work into the system as a prompt, providing a specific prompt such as "a sequel set 10 years later."

[0546] 4. The server recognizes the user's emotions

[0547] The server recognizes the user's emotional state using an emotion recognition engine that analyzes the user's facial expressions and voice to determine the user's current emotional state.

[0548] 5. The server generates a new document based on the emotion.

[0549] The server uses a generative AI model (e.g., GPT-4) to generate new literary works based on the user's emotional state. The AI ​​model mimics the author's writing style and adjusts the tone and style of the work depending on the user's emotions.

[0550] 6. The server saves the new document.

[0551] The server saves the generated new literary work as a text file called "New Work.txt" in a specified location for later access by the user.

[0552] 7. User retrieves new document

[0553] The user can download the generated new literary work from the specified location and begin reading it. The user can then download the file "New Work.txt" to their device and enjoy its contents.

[0554] Specific examples

[0555] For example, suppose a user uploads an existing literary work by a particular author ("Existing Work.pdf") to the system and enters "A Sequel 10 Years Later" as the new story prompt. The server uses an emotion engine to recognize the user's current emotional state as "Joy." As a result, the system adjusts the tone and style of the work according to the user's emotion of "Joy," generating a new literary work that is bright and hopeful. This new work is saved as "New Work.txt," and the user can download and enjoy it.

[0556] The above is a detailed description of the embodiment of the present invention.

[0557] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0558] Step 1:

[0559] User uploads a PDF file

[0560] A user terminal uploads a PDF file, which is an existing literary work by a specific author, to the system. This operation is performed by clicking the file selection button on the user interface and selecting the specified PDF file. As input, the PDF file selected by the user is given and sent to the server. The output is the PDF file received by the server.

[0561] Step 2:

[0562] The server extracts the text from the PDF file.

[0563] The server extracts text from the uploaded PDF file. Specifically, it uses document analysis software such as Tesseract OCR. The PDF file stored on the server is given as input, and text data is extracted from the PDF file. The output is the extracted text data. This text extraction operation is performed using text recognition processing using computer vision technology.

[0564] Step 3:

[0565] User enters story prompt

[0566] A user inputs the plot or storyline of a new literary work by entering text into input fields on a Web form and clicking a "Submit" button. The input is a text prompt provided by the user. The output is the prompt received by the server.

[0567] Step 4:

[0568] The server recognizes the user's emotions

[0569] The server uses an emotion recognition engine to recognize the user's emotional state. The server receives and analyzes facial and voice data sent from the user's device to determine the user's emotional state. The user's facial and voice data are given as input, and the emotion engine identifies the user's emotional state based on this. The output is the recognized user's emotional state.

[0570] Step 5:

[0571] The server generates new documents based on the emotion.

[0572] The server uses a generative AI model (e.g., GPT-4) to generate a new literary work based on the user's emotional state. The extracted text data, user-provided prompts, and the recognized emotional state are given as input. The generative AI model takes these data as input and generates the text of the new literary work. The output is the generated text of the new literary work. In this generation process, the generative AI model mimics the author's writing style, incorporating tone and style according to the emotion.

[0573] Step 6:

[0574] The server saves the new generated document.

[0575] The server saves the generated new literary work as a text file. As input, it takes the text of the generated new document and performs an operation to save it in a specific location. The output is a saved text file (e.g., "New Work.txt") in a directory that can be accessed by the user at a later date.

[0576] Step 7:

[0577] User gets new document

[0578] The user downloads the generated new literary work from the specified location and begins reading it. As input, the user is provided with the path to the saved text file and performs an operation to download it from the user's device. The output is the new document downloaded to the user's device. This operation is performed by clicking the download link on the user interface.

[0579] (Application example 2)

[0580] 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."

[0581] Conventional literary production generation systems have difficulty automatically generating works that reflect the user's emotional state. Furthermore, because they do not adjust the tone or style according to the user's emotions, the works provided often do not meet the user's expectations. This limits the quality of the user experience.

[0582] 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: means for reading an existing literary work from an input device; analysis means for analyzing the text of the literary work read from the input device; generation means for generating a new literary work using the text data extracted by the analysis means based on a prompt provided by the user; storage means for saving the new literary work generated by the generation means; and emotion recognition means for recognizing the emotional state of the user and adjusting the generation means based on the emotional state. This makes it possible to automatically generate a new literary work with a tone and style that matches the emotional state of the user.

[0583] "Existing literary works" are literary content that already exists, including books, stories, poems, etc.

[0584] An "input device" is a device that allows a user to supply data to a system, such as a PC, smartphone, or tablet.

[0585] The "reading means" has the function of receiving data from an input device and converting it into a format that can be used within the system.

[0586] The "analysis means" has the function of analyzing input text data and extracting necessary information.

[0587] A "prompt" is a user-provided guideline or instruction that indicates the theme or storyline for generating a new literary work.

[0588] A "generator" is a device that has the function of generating a new literary work based on the provided prompt and the analyzed text data.

[0589] "Storage means" are those that have the function of preserving the new literary works produced in digital form.

[0590] The "emotion recognition means" has a function for analyzing and determining the user's emotional state, and uses face recognition technology, voice analysis technology, and the like.

[0591] The invention is a system that recognizes a user's emotional state and generates new literary works based on it.

[0592] System Configuration

[0593] The system uses the following hardware and software components:

[0594] Hardware

[0595] Input device: The device through which the user uploads the PDF file, such as a computer, smartphone, or tablet.

[0596] Camera and microphone: A device for capturing a user's facial expressions and voice. For example, a camera and microphone built into a computer or smartphone can be used.

[0597] software

[0598] PDF Text Extraction Software: Software for extracting text data from PDF files, such as PDFMiner.

[0599] Emotion recognition software: Libraries such as EmotionRecognizer that analyze a user's facial expressions and voice to recognize emotions.

[0600] Generative AI models: AI models for generating new literary works based on the user's emotional state and prompts, such as OpenAI GPT-3.

[0601] Data processing and calculation

[0602] User operations

[0603] First, the user uploads a PDF file of an existing literary work by the author they want to read to the system using their input device, and then they need to enable the camera and microphone to recognize their emotional state.

[0604] Processing on the server

[0605] The server processes the data as follows:

[0606] 1. Extract PDF text:

[0607] The server uses PDFMiner to extract text data from uploaded PDF files, which later serves as the basis for generating new literary works.

[0608] 2. Enter and parse prompts:

[0609] Users input the plot or storyline of a new literary work as a prompt, which is parsed by a generative AI model and used as a guideline for generating the work.

[0610] 3. Emotion recognition:

[0611] The server uses EmotionRecognizer to recognize the user's current emotional state, specifically by using facial images captured by a camera and audio recorded by a microphone to identify the user's emotions.

[0612] 4. The creation of literary works:

[0613] The server uses a generative AI model (e.g., OpenAI GPT-3) to generate new literary works based on the user's perceived emotional state and the provided prompts. The generated works have a tone and style that correspond to the user's emotions.

[0614] 5. Saving Your Generated Work:

[0615] The generated new literary works are stored as text files by the server, so that users can download and read the new literary works at any time.

[0616] Specific examples

[0617] For example, suppose a user uploads "Existing Works.pdf" to the system and enters "A Future Detective Story" as the new story prompt. If the emotion recognition system then recognizes the user's emotional state as "Excited," the system will generate a new Future Detective Story with a tone and style that corresponds to the user's emotion of excitement.

[0618] Prompt example

[0619] Given the base text: The original text from the uploaded PDF, and the emotion: Excitement, generate a new literary work based on the prompt: A detective story set in the future.

[0620] In this way, new literary works tailored to the user's emotions are generated, providing a special reading experience.

[0621] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0622] Step 1:

[0623] The user uploads a PDF file of an existing literary work from an input device. The user provides the PDF file of the existing work to the system using a PC or smartphone. At this stage, the system checks the existence of the file and transfers it to the server.

[0624] Input: PDF files uploaded from a PC or smartphone

[0625] Output: PDF file saved on the server

[0626] Step 2:

[0627] The server uses PDFMiner to extract text data from the uploaded PDF file. The server parses the PDF file and extracts the text for each page and paragraph.

[0628] Input: PDF file stored on the server

[0629] Output: Extracted text data

[0630] Step 3:

[0631] The user inputs the plot or storyline of a new literary work into the system as a prompt, which the terminal sends to the system to use as a guideline for subsequent work generation.

[0632] Input: The prompt text entered by the user

[0633] Output: The prompt sent to the server

[0634] Step 4:

[0635] The server uses EmotionRecognizer to analyze the user's facial expressions and voice data acquired from the camera and microphone of the input device to recognize the user's emotional state. The server performs facial recognition and voiceprint analysis to identify the user's emotions in real time.

[0636] Input: User facial and voice data acquired from a camera and microphone

[0637] Output: Recognized emotional state of the user

[0638] Step 5:

[0639] The server generates new literary works using a generative AI model (e.g., OpenAI GPT-3) based on the recognized emotional state and prompt sentences. The generative AI model generates new text based on the input text data and prompts, adjusting the tone and style according to the user's emotions.

[0640] Input: Recognized emotional state, prompt sentence, extracted text data

[0641] Output: The new literary work generated

[0642] Step 6:

[0643] The server saves the new literary work as a text file, which the user can download and read later. The saved file is stored in a specified directory.

[0644] Input: A new literary work is generated.

[0645] Output: A text file saved on the server

[0646] Step 7:

[0647] A user uses a device to download a text file of a new literary work. The user can retrieve the file from the storage location and begin reading.

[0648] Input: A text file stored on the server

[0649] Output: A text file downloaded to the user's device.

[0650] 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.

[0651] 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.

[0652] 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.

[0653] [Third embodiment]

[0654] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0655] 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.

[0656] 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).

[0657] 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.

[0658] 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.

[0659] 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).

[0660] 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.

[0661] 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.

[0662] 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.

[0663] 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.

[0664] 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.

[0665] 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."

[0666] The system of the present invention generates new literary works based on existing literary works. Below, we will generate a program for this system and explain its processing in natural language. We will also show how to put the invention into practice using concrete examples.

[0667] Program processing

[0668] 1. User uploads a PDF file

[0669] The user terminal specifies a PDF file of an existing literary work by the author they want to read and uploads it to the system. This PDF file is then sent to the server, which verifies the file's existence and reads its contents.

[0670] 2. The server extracts the text from the PDF file.

[0671] The server extracts text from the uploaded PDF file, which involves using an AI model to analyze and efficiently extract the text data within the PDF.

[0672] 3. User enters story prompt

[0673] The user then inputs the plot or storyline of a new literary work into the system as a prompt, which acts as a guideline for generating the new work.

[0674] 4. The server uses AI to generate new novels

[0675] The AI ​​model deployed on the server generates new literary works based on the extracted existing text data and the user's story prompts. In this process, the AI ​​generates new text by imitating the author's writing style and literary style.

[0676] 5. The server saves the new novel.

[0677] The server saves the generated new story as a text file, which is saved in a specified location for the user to access later.

[0678] Specific examples

[0679] As a concrete example, consider the case where a user wants to read a new work by "Author A." First, the user uploads "Author A's" existing work, "Existing Work.pdf," to the system. Next, the user enters "Sequel 10 Years Later" as a prompt for the new story. Based on this information, the server extracts text from "Existing Work.pdf" and uses an AI model to generate a new literary work. The generated new literary work is saved as "New Work.txt," which the user can download and read.

[0680] This system provides an environment where fans can enjoy new works by deceased authors, meeting their demands.

[0681] The processing flow will be explained below.

[0682] Step 1:

[0683] The user selects a PDF file of an existing literary work. The user terminal uploads the specified PDF file to the server. For example, the user selects "Existing Work.pdf" and uploads it to the system.

[0684] Step 2:

[0685] The server checks whether the uploaded PDF file exists. If the file does not exist, it returns an error message. If the file exists, the server reads the file contents. The server stores the read PDF file in memory in binary format.

[0686] Step 3:

[0687] The server extracts text from the PDF file. The server uses an AI model to analyze the PDF's content and efficiently extract the text data. For example, the AI ​​model extracts text from each page and summarizes it.

[0688] Step 4:

[0689] The user inputs a prompt outlining the storyline or plot of the new work, for example, "a sequel set 10 years later." The user terminal then sends this prompt to the server.

[0690] Step 5:

[0691] The server then passes the extracted text data and user-provided prompts to the AI ​​model to generate a new literary work. The AI ​​model then generates the new work while imitating the writing style of the existing work. For example, the AI ​​model may add new episodes or events related to the characters based on the prompts.

[0692] Step 6:

[0693] The server saves the new work as a text file. The location can be specified by the user. For example, the new work can be saved as "New Work.txt".

[0694] Step 7:

[0695] The user can download the newly created work from the specified save location. The user can download the "New Work.txt" file on their device and read its contents.

[0696] This series of steps allows users to enjoy new works by deceased authors.

[0697] Example 1

[0698] 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."

[0699] The process of generating new literary works based on existing works requires a lot of manual work and is inefficient. It is also difficult to automatically generate new works that faithfully imitate the style and writing style of existing works. Therefore, there is a need for a system that can easily generate new literary works with the content desired by users.

[0700] 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.

[0701] In this invention, the server includes a means for a user to upload a PDF file, a means for the server to check the existence of the PDF file and read its contents, a means for the server to extract text from the PDF file, a means for the user to input a story prompt, a means for the server to generate a new literary work using a generative AI model, and a means for the server to save the generated new literary work, thereby enabling a user to easily and efficiently generate a new literary work based on an existing literary work.

[0702] "User" means an individual or entity that utilizes the system to upload existing literary works and enter story prompts to generate new literary works.

[0703] A "PDF file" is a standard electronic document format in which existing literary works are stored.

[0704] The "server" is a computer system that receives data from users, processes PDF files, generates new literary works using generative AI models, and stores the results.

[0705] "Text extraction" is the process of extracting textual information from a PDF file and visualizing it.

[0706] A "story prompt" is a narrative guideline or setting that a user provides to generate a new literary work.

[0707] A "generative AI model" is an artificial intelligence algorithm that automatically generates new literary works based on given text data or prompts.

[0708] A "new literary work" is a document newly created by a generative AI model based on existing text data and story prompts.

[0709] "Preservation" is the process of recording new literary works in digital form on a storage device so that they can be accessed at a later time.

[0710] "OCR technology" refers to optical character recognition technology, a technology used to extract text data from scanned images and PDFs.

[0711] The system according to the present invention generates new literary works based on existing literary works. To implement this system, the following hardware and software are used.

[0712] Users access the system's web interface using their own devices and select PDF files of existing literary works by the author they want to read. Once the file selection is complete, they click the "Upload" button, and the device sends the PDF files to the server.

[0713] When a server receives a PDF file, it verifies its existence and reads its contents into memory. The server then uses AI models such as Tesseract OCR or Adobe PDF Extractor to extract text data from the PDF file. The extracted text data is then stored in storage for use in subsequent processing steps.

[0714] The user then enters a prompt into the system's interface describing the plot or storyline of the new literary work, such as "a sequel set 10 years later" or "the main characters embark on a new adventure," and clicks the "Submit" button. The prompt is then sent to the server.

[0715] Based on the submitted prompt and the extracted text data, a generative AI model (e.g., OpenAI's GPT-4) deployed on the server generates a new literary work. The generative AI model receives existing text data and the prompt as input and generates new content based on it.

[0716] The server saves the new literary work as a text file named "New Work.txt" in a designated directory accessible to the user. Once saved, the server notifies the user that a new work is available and allows them to download and read it.

[0717] As a concrete example, consider the case where a user wants to read a new work by "Author A." First, the user uploads "Author A's" existing work, "Existing Work.pdf," to the system. Next, the user enters "Sequel 10 Years Later" as a prompt for the new story. Based on this information, the server extracts text from "Existing Work.pdf" and generates a new literary work using a generative AI model. The generated new literary work is saved as "New Work.txt," which the user can download and read.

[0718] This system provides an environment where fans can enjoy new works by deceased authors, meeting their demands.

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

[0720] Step 1:

[0721] A user uploads a PDF file. The user accesses the system's web interface using a terminal, selects an existing PDF file of a literary work, and clicks the "Upload" button. This operation sends the PDF file from the terminal to the server. The input is a PDF file, and the output is a PDF file saved in a specified directory on the server.

[0722] Step 2:

[0723] The server checks whether the PDF file exists and reads its contents. The server checks whether the received PDF file is saved correctly in the specified directory. If the file exists, the server reads its contents into memory. The input is the PDF file saved in the server, and the output is the contents of the PDF file read into the server's memory.

[0724] Step 3:

[0725] The server extracts text from PDF files. The server uses AI models such as Tesseract OCR and Adobe PDF Extractor to extract text data. Specifically, it analyzes character information from scanned images of PDF files and extracts it as text. The input is the scanned image of the PDF file, and the output is the extracted text data.

[0726] Step 4:

[0727] User Enters Story Prompt: The user enters a prompt in the web interface that describes the plot or storyline of a new literary work, such as "A sequel set 10 years later" or "The main characters go on a new adventure," and clicks the "Submit" button. The input is the prompt entered by the user, and the output is the prompt sent to the server.

[0728] Step 5:

[0729] The server generates a new literary work using a generative AI model. The server generates a new literary work using a generative AI model (e.g., OpenAI's GPT-4) based on the extracted existing text data and the prompt sentence entered by the user. The input is the extracted text data and the prompt sentence, and the output is the text of the generated new literary work.

[0730] Step 6:

[0731] The server saves the generated new literary work. The server saves the generated new literary work as "New Work.txt" in a specified directory. The input is the text of the generated new literary work, and the output is a text file of the new literary work saved in the specified directory. Users can download and read the new literary work from this directory.

[0732] Through the above processing steps, the system can efficiently provide new literary works to the user.

[0733] (Application example 1)

[0734] 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."

[0735] In systems that generate new literary works based on existing works, a mechanism is required that allows users to easily view and download the generated works. Another challenge is to improve the user experience by promptly notifying users of the generated works.

[0736] 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.

[0737] In this invention, the server includes: means for reading an existing literary work from an input device; analysis means for analyzing the text of the literary work read from the input device; generation means for generating a new literary work using the text data extracted by the analysis means based on a prompt provided by a user; storage means for saving the new literary work generated by the generation means; and distribution means for notifying the user of the generated new literary work and enabling the user to view or download the generated work through a smartphone application. This allows the user to quickly receive the generated new literary work and easily view or download it.

[0738] An "input device" is a device that a user uses to provide existing literary works to the system.

[0739] The "reading means" is a means for executing a process of importing a literary work provided via an input device into the system.

[0740] "Analysis means" refers to a means for extracting text data from the literary work that has been read and analyzing its content.

[0741] The "generation means" is a means for generating a new literary work based on the text data extracted by the analysis means and prompts provided by the user.

[0742] The "storage means" is a means for storing within the system the new literary work generated by the generation means.

[0743] "Distribution means" refers to the means by which users are notified of new stored literary works and are able to view or download them through a smartphone application.

[0744] A "prompt" is a user-provided text that serves as a guideline or theme for the generation of a new literary work.

[0745] An "artificial intelligence model" is a machine learning algorithm used to generate new texts that mimic the writing style of existing literary works.

[0746] A "smartphone application" is application software for a mobile device that a user uses to view or download newly generated literary works.

[0747] A system for implementing the present invention is configured as follows.

[0748] First, the user uses the smartphone application to read an existing literary work from an input device (such as a smartphone or tablet), and can specify a PDF file and upload it to the system.

[0749] The server side checks for the existence of the uploaded PDF file, reads its contents, and uses analytical techniques to extract text from the PDF file. This involves using an AI model to efficiently parse and extract text data from the PDF. Specifically, it uses a library such as PyPDF2 to extract text from the PDF.

[0750] Next, the user enters a prompt into the smartphone application that will become the plot or storyline of the new literary work, which will set the theme and direction of the user's intended new work. For example, the user could enter the prompt "A sequel set 10 years later."

[0751] On the server side, a generator is used to generate new literary works based on the existing text data extracted by the analysis tool and the user's story prompts. This generator is performed using a generative AI model such as OpenAI's GPT-3. The AI ​​model imitates the writing style of existing works to generate new literary works.

[0752] The generated new literary work is saved as a text file in a specified location on the server, and then notified to the user through a distribution method. The user can then view or download the generated new work through a smartphone application.

[0753] As a specific example, the process is carried out as follows.

[0754] 1. User Input:

[0755] PDF: “existing_literature.pdf”

[0756] Prompt: "A sequel set 10 years later."

[0757] 2. PDF Text Extraction:

[0758] Text: "This is the content of an existing literary work..."

[0759] 3. AI model prompt:

[0760] existing_text + "\n\nSequel 10 years later"

[0761] The server generates, stores, and distributes new literary works based on the user-provided PDF files and prompts, making them readily accessible to users.

[0762] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0763] Step 1:

[0764] A user uploads a PDF file of an existing literary work to a server using a smartphone application.

[0765] Input: A PDF file of an existing literary work (e.g., "existing_literature.pdf")

[0766] How it works: A user uses the upload function of a smartphone application to select a PDF file and send it to a server.

[0767] Output: A PDF file is saved on the server.

[0768] Step 2:

[0769] The server checks for the existence of the uploaded PDF file and extracts the text.

[0770] Input: PDF file uploaded in Step 1

[0771] How it works: The server uses a PDF parsing library such as PyPDF2 to extract text data from a PDF file. It opens the PDF file and extracts the text from each page sequentially.

[0772] Output: Extracted text data (e.g., "This is the content of an existing literary work...")

[0773] Step 3:

[0774] A user enters a prompt for a new literary work through a smartphone application.

[0775] Input: The prompt text that the user enters (e.g., "The sequel 10 years later")

[0776] How it works: Through a smartphone application, the user enters a prompt statement that describes the plot or theme of a new work into the input field and presses the send button.

[0777] Output: The user's prompt is sent to the server.

[0778] Step 4:

[0779] The server generates new literary works based on the extracted text data and user prompts.

[0780] Input: Text data extracted in step 2, prompt sent in step 3

[0781] How it works: The server uses a generative AI model such as OpenAI's GPT-3 to generate a new literary work by integrating the extracted text data and the prompt sentence. It generates a complete_prompt sentence for the AI ​​model and executes the generation process by calling the API.

[0782] Output: The newly generated literary work (in text format)

[0783] Step 5:

[0784] The server saves the generated new literary work as a text file.

[0785] Input: The new literary work generated in step 4

[0786] Operation: The server saves the generated text data as a text file in the specified location.

[0787] Output: The path to the saved text file (e.g., " / path / to / generated_literature.txt")

[0788] Step 6:

[0789] The server notifies users of the existence of the generated literary works, allowing them to view and download them through a smartphone application.

[0790] Input: The file path of the new literary work saved in step 5

[0791] How it works: The server notifies the user that a new literary work has been created and delivers the path to the text file to the smartphone application. The user receives the notification and opens the application to view or download the work.

[0792] Output: The user can view or download the new literary work generated in a smartphone application.

[0793] 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.

[0794] The present invention is a system for recognizing a user's emotional state and generating new literary works based on that state. In addition to the basic function of reading existing literary works and analyzing them to generate new works, the system can also recognize the user's emotions using an emotion engine and adjust the tone and style of the resulting work. Below, we will generate a program for this system and explain its processing in natural language. We will also demonstrate how the invention can be put into practice using concrete examples.

[0795] Program processing

[0796] 1. User uploads a PDF file

[0797] The user terminal specifies a PDF file of an existing literary work by the author they want to read and uploads it to the system. This PDF file is then sent to the server, which verifies the file's existence and reads its contents.

[0798] 2. The server extracts the text from the PDF file.

[0799] The server extracts text from the uploaded PDF file, which involves using an AI model to analyze and efficiently extract the text data within the PDF.

[0800] 3. User enters story prompt

[0801] The user then inputs the plot or storyline of a new literary work into the system as a prompt, which acts as a guideline for generating the new work.

[0802] 4. The server recognizes the user's emotions

[0803] The server recognizes the user's emotional state using an emotion engine, which analyzes the user's facial expressions and voice input to determine the user's current emotional state.

[0804] 5. The server generates new literary works based on emotions.

[0805] The server uses an AI model to generate new literary works based on the user's perceived emotional state. The AI ​​model adjusts the tone and style of the work depending on the user's emotions. In this process, the AI ​​mimics the author's writing style and literary style to generate new text.

[0806] 6. The server saves the new novel.

[0807] The server saves the generated new novel as a text file. This file is saved in a specified location for the user to access later. For example, the generated new literary work is saved as "New Work.txt."

[0808] 7. User gets new novel

[0809] The user can download the new novel from the specified location and begin reading it. The user can then download the "New Work.txt" file to their device and enjoy the content.

[0810] Specific examples

[0811] For example, suppose a user uploads an existing literary work, "Existing Work.pdf," to the system and enters "Sequel 10 Years Later" as the new story prompt. The emotion engine then recognizes the user's current emotional state as "Joy." As a result, the system adjusts the tone and style of the work according to the user's emotion of "Joy" and generates a new literary work that is bright and hopeful. This new work is then saved as "New Work.txt" for the user to download and enjoy.

[0812] This invention not only allows users to enjoy new works by deceased authors, but also provides a special experience tailored to their own emotional state.

[0813] The processing flow will be explained below.

[0814] Step 1:

[0815] The user selects a PDF file of an existing literary work and uploads it to the system from the user terminal. For example, the user selects "Existing Work.pdf" and sends the file to the system.

[0816] Step 2:

[0817] The server checks whether the uploaded PDF file exists. If the file does not exist, it returns an error message. If the file exists, the server reads its contents. The server stores the read PDF file in memory in binary format.

[0818] Step 3:

[0819] The server extracts text from the uploaded PDF file. The server uses an AI model to analyze the PDF's content and efficiently extract the text data, for example, extracting the text from each page and summarizing it as a whole.

[0820] Step 4:

[0821] The user inputs a prompt outlining the storyline or plot of the new work, for example, "a sequel set 10 years later." The user terminal then sends this prompt to the server.

[0822] Step 5:

[0823] The server recognizes the user's emotional state using an emotion engine, which analyzes the user's facial expressions and voice input to determine the user's current emotion, for example, by using data from a camera or microphone to detect the user's emotion.

[0824] Step 6:

[0825] The server uses an AI model to generate new literary works based on the user's perceived emotional state. The AI ​​model uses existing text data and prompts to create new works with a tone and style that matches the user's emotions. For example, if the user is feeling "joy," the server generates text with an upbeat and hopeful feel.

[0826] Step 7:

[0827] The server saves the new novel as a text file. The user can specify the location. For example, it can be saved as "New Work.txt."

[0828] Step 8:

[0829] The user downloads the new novel from the specified location. The user can then download the file "New Work.txt" to their device and read the contents.

[0830] This series of steps allows users to not only enjoy new works by deceased authors, but also experience literary works tailored to their own emotional state.

[0831] Example 2

[0832] 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."

[0833] With conventional document generation systems, it was difficult for users to reflect their own emotions and current state when creating a new document. Furthermore, when creating a new work based on an existing document, it was difficult to accurately reproduce the tone and style unique to that document. These issues led to a decrease in user satisfaction.

[0834] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0835] In this invention, the server includes means for reading an existing document from an input device, means for analyzing the text of the document read from the input device, means for generating a new document using the text data extracted by the analysis means based on instructions provided by a user, means for recognizing the emotional state of the user, means for adjusting the tone and style of the generated document based on the recognized emotional state, means for saving the generated new document, and means for providing the new document to the user, thereby making it possible to generate a new document that reflects the emotional state of the user.

[0836] An "input device" is a device for inputting existing document data into a computer system.

[0837] "Analysis means" refers to a technical method for extracting text information from imported document data and understanding its content.

[0838] The "generation means" is a technical method for creating a new document based on the extracted text data and instructions from the user.

[0839] "Emotional state" refers to the psychological and emotional state of the user as recognized from their facial expressions and voice.

[0840] "Means of recognition" refers to the technical methods and algorithms used to analyze and determine the user's emotional state.

[0841] "Tone and style" refers to the mood and writing characteristics of a document, which change depending on the emotional state.

[0842] "Storage means" refers to the technical method for storing the newly generated document as data and making it accessible later.

[0843] The "means of provision" refers to the technical method used to distribute the stored document data in a form that allows users to view and download it.

[0844] The present invention is a system for recognizing a user's emotional state and generating new literary works based on that. In addition to the basic function of reading an existing document from an input device and analyzing its content to generate a new work, the system can also recognize the user's emotions using an emotion engine and adjust the tone and style of the generated work.

[0845] Hardware and software used

[0846] Hardware

[0847] Server: The central processing device that analyzes documents, recognizes emotions, and generates new documents.

[0848] User device: A device that uploads documents and provides emotion data. This includes PCs, smartphones, tablets, etc.

[0849] Input device: A device for uploading PDF files. Typically, this corresponds to the file input function of a PC or smartphone.

[0850] software

[0851] Document analysis software: Used to extract text from PDFs, for example, Tesseract OCR.

[0852] Emotion recognition engine: Used to analyze user emotions. For example, Microsoft Azure's Emotion API is used.

[0853] Generative AI models: used to generate new documents, such as OpenAI's GPT-4.

[0854] Process Overview

[0855] 1. User uploads a PDF file

[0856] A user terminal uploads a PDF file containing an existing literary work by a particular author to the system, which then sends the uploaded PDF file to the server, which verifies the file's existence and reads its contents.

[0857] 2. The server extracts the text from the PDF file.

[0858] The server extracts text from the uploaded PDF file using document analysis software such as Tesseract OCR, which efficiently extracts the text data from the PDF.

[0859] 3. User enters story prompt

[0860] The user inputs the plot or storyline of a new literary work into the system as a prompt, providing a specific prompt such as "a sequel set 10 years later."

[0861] 4. The server recognizes the user's emotions

[0862] The server recognizes the user's emotional state using an emotion recognition engine that analyzes the user's facial expressions and voice to determine the user's current emotional state.

[0863] 5. The server generates a new document based on the emotion.

[0864] The server uses a generative AI model (e.g., GPT-4) to generate new literary works based on the user's emotional state. The AI ​​model mimics the author's writing style and adjusts the tone and style of the work depending on the user's emotions.

[0865] 6. The server saves the new document.

[0866] The server saves the generated new literary work as a text file called "New Work.txt" in a specified location for later access by the user.

[0867] 7. User retrieves new document

[0868] The user can download the generated new literary work from the specified location and begin reading it. The user can then download the file "New Work.txt" to their device and enjoy its contents.

[0869] Specific examples

[0870] For example, suppose a user uploads an existing literary work by a particular author ("Existing Work.pdf") to the system and enters "A Sequel 10 Years Later" as the new story prompt. The server uses an emotion engine to recognize the user's current emotional state as "Joy." As a result, the system adjusts the tone and style of the work according to the user's emotion of "Joy," generating a new literary work that is bright and hopeful. This new work is saved as "New Work.txt," and the user can download and enjoy it.

[0871] The above is a detailed description of the embodiment of the present invention.

[0872] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0873] Step 1:

[0874] User uploads a PDF file

[0875] A user terminal uploads a PDF file, which is an existing literary work by a specific author, to the system. This operation is performed by clicking the file selection button on the user interface and selecting the specified PDF file. As input, the PDF file selected by the user is given and sent to the server. The output is the PDF file received by the server.

[0876] Step 2:

[0877] The server extracts the text from the PDF file.

[0878] The server extracts text from the uploaded PDF file. Specifically, it uses document analysis software such as Tesseract OCR. The PDF file stored on the server is given as input, and text data is extracted from the PDF file. The output is the extracted text data. This text extraction operation is performed using text recognition processing using computer vision technology.

[0879] Step 3:

[0880] User enters story prompt

[0881] A user inputs the plot or storyline of a new literary work by entering text into input fields on a Web form and clicking a "Submit" button. The input is a text prompt provided by the user. The output is the prompt received by the server.

[0882] Step 4:

[0883] The server recognizes the user's emotions

[0884] The server uses an emotion recognition engine to recognize the user's emotional state. The server receives and analyzes facial and voice data sent from the user's device to determine the user's emotional state. The user's facial and voice data are given as input, and the emotion engine identifies the user's emotional state based on this. The output is the recognized user's emotional state.

[0885] Step 5:

[0886] The server generates new documents based on the emotion.

[0887] The server uses a generative AI model (e.g., GPT-4) to generate a new literary work based on the user's emotional state. The extracted text data, user-provided prompts, and the recognized emotional state are given as input. The generative AI model takes these data as input and generates the text of the new literary work. The output is the generated text of the new literary work. In this generation process, the generative AI model mimics the author's writing style, incorporating tone and style according to the emotion.

[0888] Step 6:

[0889] The server saves the new generated document.

[0890] The server saves the generated new literary work as a text file. As input, it takes the text of the generated new document and performs an operation to save it in a specific location. The output is a saved text file (e.g., "New Work.txt") in a directory that can be accessed by the user at a later date.

[0891] Step 7:

[0892] User gets new document

[0893] The user downloads the generated new literary work from the specified location and begins reading it. As input, the user is provided with the path to the saved text file and performs an operation to download it from the user's device. The output is the new document downloaded to the user's device. This operation is performed by clicking the download link on the user interface.

[0894] (Application example 2)

[0895] 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."

[0896] Conventional literary production generation systems have difficulty automatically generating works that reflect the user's emotional state. Furthermore, because they do not adjust the tone or style according to the user's emotions, the works provided often do not meet the user's expectations. This limits the quality of the user experience.

[0897] 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: means for reading an existing literary work from an input device; analysis means for analyzing the text of the literary work read from the input device; generation means for generating a new literary work using the text data extracted by the analysis means based on a prompt provided by the user; storage means for saving the new literary work generated by the generation means; and emotion recognition means for recognizing the emotional state of the user and adjusting the generation means based on the emotional state. This makes it possible to automatically generate a new literary work with a tone and style that matches the emotional state of the user.

[0898] "Existing literary works" are literary content that already exists, including books, stories, poems, etc.

[0899] An "input device" is a device that allows a user to supply data to a system, such as a PC, smartphone, or tablet.

[0900] The "reading means" has the function of receiving data from an input device and converting it into a format that can be used within the system.

[0901] The "analysis means" has the function of analyzing input text data and extracting necessary information.

[0902] A "prompt" is a user-provided guideline or instruction that indicates the theme or storyline for generating a new literary work.

[0903] A "generator" is a device that has the function of generating a new literary work based on the provided prompt and the analyzed text data.

[0904] "Storage means" are those that have the function of preserving the new literary works produced in digital form.

[0905] The "emotion recognition means" has a function for analyzing and determining the user's emotional state, and uses face recognition technology, voice analysis technology, and the like.

[0906] The invention is a system that recognizes a user's emotional state and generates new literary works based on it.

[0907] System Configuration

[0908] The system uses the following hardware and software components:

[0909] Hardware

[0910] Input device: The device through which the user uploads the PDF file, such as a computer, smartphone, or tablet.

[0911] Camera and microphone: A device for capturing a user's facial expressions and voice. For example, a camera and microphone built into a computer or smartphone can be used.

[0912] software

[0913] PDF Text Extraction Software: Software for extracting text data from PDF files, such as PDFMiner.

[0914] Emotion recognition software: Libraries such as EmotionRecognizer that analyze a user's facial expressions and voice to recognize emotions.

[0915] Generative AI models: AI models for generating new literary works based on the user's emotional state and prompts, such as OpenAI GPT-3.

[0916] Data processing and calculation

[0917] User operations

[0918] First, the user uploads a PDF file of an existing literary work by the author they want to read to the system using their input device, and then they need to enable the camera and microphone to recognize their emotional state.

[0919] Processing on the server

[0920] The server processes the data as follows:

[0921] 1. Extract PDF text:

[0922] The server uses PDFMiner to extract text data from uploaded PDF files, which later serves as the basis for generating new literary works.

[0923] 2. Enter and parse prompts:

[0924] Users input the plot or storyline of a new literary work as a prompt, which is parsed by a generative AI model and used as a guideline for generating the work.

[0925] 3. Emotion recognition:

[0926] The server uses EmotionRecognizer to recognize the user's current emotional state, specifically by using facial images captured by a camera and audio recorded by a microphone to identify the user's emotions.

[0927] 4. The creation of literary works:

[0928] The server uses a generative AI model (e.g., OpenAI GPT-3) to generate new literary works based on the user's perceived emotional state and the provided prompts. The generated works have a tone and style that correspond to the user's emotions.

[0929] 5. Saving Your Generated Work:

[0930] The generated new literary works are stored as text files by the server, so that users can download and read the new literary works at any time.

[0931] Specific examples

[0932] For example, suppose a user uploads "Existing Works.pdf" to the system and enters "A Future Detective Story" as the new story prompt. If the emotion recognition system then recognizes the user's emotional state as "Excited," the system will generate a new Future Detective Story with a tone and style that corresponds to the user's emotion of excitement.

[0933] Prompt example

[0934] Given the base text: The original text from the uploaded PDF, and the emotion: Excitement, generate a new literary work based on the prompt: A detective story set in the future.

[0935] In this way, new literary works tailored to the user's emotions are generated, providing a special reading experience.

[0936] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0937] Step 1:

[0938] The user uploads a PDF file of an existing literary work from an input device. The user provides the PDF file of the existing work to the system using a PC or smartphone. At this stage, the system checks the existence of the file and transfers it to the server.

[0939] Input: PDF files uploaded from a PC or smartphone

[0940] Output: PDF file saved on the server

[0941] Step 2:

[0942] The server uses PDFMiner to extract text data from the uploaded PDF file. The server parses the PDF file and extracts the text for each page and paragraph.

[0943] Input: PDF file stored on the server

[0944] Output: Extracted text data

[0945] Step 3:

[0946] The user inputs the plot or storyline of a new literary work into the system as a prompt, which the terminal sends to the system to use as a guideline for subsequent work generation.

[0947] Input: The prompt text entered by the user

[0948] Output: The prompt sent to the server

[0949] Step 4:

[0950] The server uses EmotionRecognizer to analyze the user's facial expressions and voice data acquired from the camera and microphone of the input device to recognize the user's emotional state. The server performs facial recognition and voiceprint analysis to identify the user's emotions in real time.

[0951] Input: User facial and voice data acquired from a camera and microphone

[0952] Output: Recognized emotional state of the user

[0953] Step 5:

[0954] The server generates new literary works using a generative AI model (e.g., OpenAI GPT-3) based on the recognized emotional state and prompt sentences. The generative AI model generates new text based on the input text data and prompts, adjusting the tone and style according to the user's emotions.

[0955] Input: Recognized emotional state, prompt sentence, extracted text data

[0956] Output: The new literary work generated

[0957] Step 6:

[0958] The server saves the new literary work as a text file, which the user can download and read later. The saved file is stored in a specified directory.

[0959] Input: A new literary work is generated.

[0960] Output: A text file saved on the server

[0961] Step 7:

[0962] A user uses a device to download a text file of a new literary work. The user can retrieve the file from the storage location and begin reading.

[0963] Input: A text file stored on the server

[0964] Output: A text file downloaded to the user's device.

[0965] 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.

[0966] 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.

[0967] 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.

[0968] [Fourth embodiment]

[0969] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0970] 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.

[0971] 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).

[0972] 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.

[0973] 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.

[0974] 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).

[0975] 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.

[0976] 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.

[0977] 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.

[0978] 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.

[0979] 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.

[0980] 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.

[0981] 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."

[0982] The system of the present invention generates new literary works based on existing literary works. Below, we will generate a program for this system and explain its processing in natural language. We will also show how to put the invention into practice using concrete examples.

[0983] Program processing

[0984] 1. User uploads a PDF file

[0985] The user terminal specifies a PDF file of an existing literary work by the author they want to read and uploads it to the system. This PDF file is then sent to the server, which verifies the file's existence and reads its contents.

[0986] 2. The server extracts the text from the PDF file.

[0987] The server extracts text from the uploaded PDF file, which involves using an AI model to analyze and efficiently extract the text data within the PDF.

[0988] 3. User enters story prompt

[0989] The user then inputs the plot or storyline of a new literary work into the system as a prompt, which acts as a guideline for generating the new work.

[0990] 4. The server uses AI to generate new novels

[0991] The AI ​​model deployed on the server generates new literary works based on the extracted existing text data and the user's story prompts. In this process, the AI ​​generates new text by imitating the author's writing style and literary style.

[0992] 5. The server saves the new novel.

[0993] The server saves the generated new story as a text file, which is saved in a specified location for the user to access later.

[0994] Specific examples

[0995] As a concrete example, consider the case where a user wants to read a new work by "Author A." First, the user uploads "Author A's" existing work, "Existing Work.pdf," to the system. Next, the user enters "Sequel 10 Years Later" as a prompt for the new story. Based on this information, the server extracts text from "Existing Work.pdf" and uses an AI model to generate a new literary work. The generated new literary work is saved as "New Work.txt," which the user can download and read.

[0996] This system provides an environment where fans can enjoy new works by deceased authors, meeting their demands.

[0997] The processing flow will be explained below.

[0998] Step 1:

[0999] The user selects a PDF file of an existing literary work. The user terminal uploads the specified PDF file to the server. For example, the user selects "Existing Work.pdf" and uploads it to the system.

[1000] Step 2:

[1001] The server checks whether the uploaded PDF file exists. If the file does not exist, it returns an error message. If the file exists, the server reads the file contents. The server stores the read PDF file in memory in binary format.

[1002] Step 3:

[1003] The server extracts text from the PDF file. The server uses an AI model to analyze the PDF's content and efficiently extract the text data. For example, the AI ​​model extracts text from each page and summarizes it.

[1004] Step 4:

[1005] The user inputs a prompt outlining the storyline or plot of the new work, for example, "a sequel set 10 years later." The user terminal then sends this prompt to the server.

[1006] Step 5:

[1007] The server then passes the extracted text data and user-provided prompts to the AI ​​model to generate a new literary work. The AI ​​model then generates the new work while imitating the writing style of the existing work. For example, the AI ​​model may add new episodes or events related to the characters based on the prompts.

[1008] Step 6:

[1009] The server saves the new work as a text file. The location can be specified by the user. For example, the new work can be saved as "New Work.txt".

[1010] Step 7:

[1011] The user can download the newly created work from the specified save location. The user can download the "New Work.txt" file on their device and read its contents.

[1012] This series of steps allows users to enjoy new works by deceased authors.

[1013] Example 1

[1014] 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."

[1015] The process of generating new literary works based on existing works requires a lot of manual work and is inefficient. It is also difficult to automatically generate new works that faithfully imitate the style and writing style of existing works. Therefore, there is a need for a system that can easily generate new literary works with the content desired by users.

[1016] 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.

[1017] In this invention, the server includes a means for a user to upload a PDF file, a means for the server to check the existence of the PDF file and read its contents, a means for the server to extract text from the PDF file, a means for the user to input a story prompt, a means for the server to generate a new literary work using a generative AI model, and a means for the server to save the generated new literary work, thereby enabling a user to easily and efficiently generate a new literary work based on an existing literary work.

[1018] "User" means an individual or entity that utilizes the system to upload existing literary works and enter story prompts to generate new literary works.

[1019] A "PDF file" is a standard electronic document format in which existing literary works are stored.

[1020] The "server" is a computer system that receives data from users, processes PDF files, generates new literary works using generative AI models, and stores the results.

[1021] "Text extraction" is the process of extracting textual information from a PDF file and visualizing it.

[1022] A "story prompt" is a narrative guideline or setting that a user provides to generate a new literary work.

[1023] A "generative AI model" is an artificial intelligence algorithm that automatically generates new literary works based on given text data or prompts.

[1024] A "new literary work" is a document newly created by a generative AI model based on existing text data and story prompts.

[1025] "Preservation" is the process of recording new literary works in digital form on a storage device so that they can be accessed at a later time.

[1026] "OCR technology" refers to optical character recognition technology, a technology used to extract text data from scanned images and PDFs.

[1027] The system according to the present invention generates new literary works based on existing literary works. To implement this system, the following hardware and software are used.

[1028] Users access the system's web interface using their own devices and select PDF files of existing literary works by the author they want to read. Once the file selection is complete, they click the "Upload" button, and the device sends the PDF files to the server.

[1029] When a server receives a PDF file, it verifies its existence and reads its contents into memory. The server then uses AI models such as Tesseract OCR or Adobe PDF Extractor to extract text data from the PDF file. The extracted text data is then stored in storage for use in subsequent processing steps.

[1030] The user then enters a prompt into the system's interface describing the plot or storyline of the new literary work, such as "a sequel set 10 years later" or "the main characters embark on a new adventure," and clicks the "Submit" button. The prompt is then sent to the server.

[1031] Based on the submitted prompt and the extracted text data, a generative AI model (e.g., OpenAI's GPT-4) deployed on the server generates a new literary work. The generative AI model receives existing text data and the prompt as input and generates new content based on it.

[1032] The server saves the new literary work as a text file named "New Work.txt" in a designated directory accessible to the user. Once saved, the server notifies the user that a new work is available and allows them to download and read it.

[1033] As a concrete example, consider the case where a user wants to read a new work by "Author A." First, the user uploads "Author A's" existing work, "Existing Work.pdf," to the system. Next, the user enters "Sequel 10 Years Later" as a prompt for the new story. Based on this information, the server extracts text from "Existing Work.pdf" and generates a new literary work using a generative AI model. The generated new literary work is saved as "New Work.txt," which the user can download and read.

[1034] This system provides an environment where fans can enjoy new works by deceased authors, meeting their demands.

[1035] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1036] Step 1:

[1037] A user uploads a PDF file. The user accesses the system's web interface using a terminal, selects an existing PDF file of a literary work, and clicks the "Upload" button. This operation sends the PDF file from the terminal to the server. The input is a PDF file, and the output is a PDF file saved in a specified directory on the server.

[1038] Step 2:

[1039] The server checks whether the PDF file exists and reads its contents. The server checks whether the received PDF file is saved correctly in the specified directory. If the file exists, the server reads its contents into memory. The input is the PDF file saved in the server, and the output is the contents of the PDF file read into the server's memory.

[1040] Step 3:

[1041] The server extracts text from PDF files. The server uses AI models such as Tesseract OCR and Adobe PDF Extractor to extract text data. Specifically, it analyzes character information from scanned images of PDF files and extracts it as text. The input is the scanned image of the PDF file, and the output is the extracted text data.

[1042] Step 4:

[1043] User Enters Story Prompt: The user enters a prompt in the web interface that describes the plot or storyline of a new literary work, such as "A sequel set 10 years later" or "The main characters go on a new adventure," and clicks the "Submit" button. The input is the prompt entered by the user, and the output is the prompt sent to the server.

[1044] Step 5:

[1045] The server generates a new literary work using a generative AI model. The server generates a new literary work using a generative AI model (e.g., OpenAI's GPT-4) based on the extracted existing text data and the prompt sentence entered by the user. The input is the extracted text data and the prompt sentence, and the output is the text of the generated new literary work.

[1046] Step 6:

[1047] The server saves the generated new literary work. The server saves the generated new literary work as "New Work.txt" in a specified directory. The input is the text of the generated new literary work, and the output is a text file of the new literary work saved in the specified directory. Users can download and read the new literary work from this directory.

[1048] Through the above processing steps, the system can efficiently provide new literary works to the user.

[1049] (Application example 1)

[1050] 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."

[1051] In systems that generate new literary works based on existing works, a mechanism is required that allows users to easily view and download the generated works. Another challenge is to improve the user experience by promptly notifying users of the generated works.

[1052] 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.

[1053] In this invention, the server includes: means for reading an existing literary work from an input device; analysis means for analyzing the text of the literary work read from the input device; generation means for generating a new literary work using the text data extracted by the analysis means based on a prompt provided by a user; storage means for saving the new literary work generated by the generation means; and distribution means for notifying the user of the generated new literary work and enabling the user to view or download the generated work through a smartphone application. This allows the user to quickly receive the generated new literary work and easily view or download it.

[1054] An "input device" is a device that a user uses to provide existing literary works to the system.

[1055] The "reading means" is a means for executing a process of importing a literary work provided via an input device into the system.

[1056] "Analysis means" refers to a means for extracting text data from the literary work that has been read and analyzing its content.

[1057] The "generation means" is a means for generating a new literary work based on the text data extracted by the analysis means and prompts provided by the user.

[1058] The "storage means" is a means for storing within the system the new literary work generated by the generation means.

[1059] "Distribution means" refers to the means by which users are notified of new stored literary works and are able to view or download them through a smartphone application.

[1060] A "prompt" is a user-provided text that serves as a guideline or theme for the generation of a new literary work.

[1061] An "artificial intelligence model" is a machine learning algorithm used to generate new texts that mimic the writing style of existing literary works.

[1062] A "smartphone application" is application software for a mobile device that a user uses to view or download newly generated literary works.

[1063] A system for implementing the present invention is configured as follows.

[1064] First, the user uses the smartphone application to read an existing literary work from an input device (such as a smartphone or tablet), and can specify a PDF file and upload it to the system.

[1065] The server side checks for the existence of the uploaded PDF file, reads its contents, and uses analytical techniques to extract text from the PDF file. This involves using an AI model to efficiently parse and extract text data from the PDF. Specifically, it uses a library such as PyPDF2 to extract text from the PDF.

[1066] Next, the user enters a prompt into the smartphone application that will become the plot or storyline of the new literary work, which will set the theme and direction of the user's intended new work. For example, the user could enter the prompt "A sequel set 10 years later."

[1067] On the server side, a generator is used to generate new literary works based on the existing text data extracted by the analysis tool and the user's story prompts. This generator is performed using a generative AI model such as OpenAI's GPT-3. The AI ​​model imitates the writing style of existing works to generate new literary works.

[1068] The generated new literary work is saved as a text file in a specified location on the server, and then notified to the user through a distribution method. The user can then view or download the generated new work through a smartphone application.

[1069] As a specific example, the process is carried out as follows.

[1070] 1. User Input:

[1071] PDF: “existing_literature.pdf”

[1072] Prompt: "A sequel set 10 years later."

[1073] 2. PDF Text Extraction:

[1074] Text: "This is the content of an existing literary work..."

[1075] 3. AI model prompt:

[1076] existing_text + "\n\nSequel 10 years later"

[1077] The server generates, stores, and distributes new literary works based on the user-provided PDF files and prompts, making them readily accessible to users.

[1078] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1079] Step 1:

[1080] A user uploads a PDF file of an existing literary work to a server using a smartphone application.

[1081] Input: A PDF file of an existing literary work (e.g., "existing_literature.pdf")

[1082] How it works: A user uses the upload function of a smartphone application to select a PDF file and send it to a server.

[1083] Output: A PDF file is saved on the server.

[1084] Step 2:

[1085] The server checks for the existence of the uploaded PDF file and extracts the text.

[1086] Input: PDF file uploaded in Step 1

[1087] How it works: The server uses a PDF parsing library such as PyPDF2 to extract text data from a PDF file. It opens the PDF file and extracts the text from each page sequentially.

[1088] Output: Extracted text data (e.g., "This is the content of an existing literary work...")

[1089] Step 3:

[1090] A user enters a prompt for a new literary work through a smartphone application.

[1091] Input: The prompt text that the user enters (e.g., "The sequel 10 years later")

[1092] How it works: Through a smartphone application, the user enters a prompt statement that describes the plot or theme of a new work into the input field and presses the send button.

[1093] Output: The user's prompt is sent to the server.

[1094] Step 4:

[1095] The server generates new literary works based on the extracted text data and user prompts.

[1096] Input: Text data extracted in step 2, prompt sent in step 3

[1097] How it works: The server uses a generative AI model such as OpenAI's GPT-3 to generate a new literary work by integrating the extracted text data and the prompt sentence. It generates a complete_prompt sentence for the AI ​​model and executes the generation process by calling the API.

[1098] Output: The newly generated literary work (in text format)

[1099] Step 5:

[1100] The server saves the generated new literary work as a text file.

[1101] Input: The new literary work generated in step 4

[1102] Operation: The server saves the generated text data as a text file in the specified location.

[1103] Output: The path to the saved text file (e.g., " / path / to / generated_literature.txt")

[1104] Step 6:

[1105] The server notifies users of the existence of the generated literary works, allowing them to view and download them through a smartphone application.

[1106] Input: The file path of the new literary work saved in step 5

[1107] How it works: The server notifies the user that a new literary work has been created and delivers the path to the text file to the smartphone application. The user receives the notification and opens the application to view or download the work.

[1108] Output: The user can view or download the new literary work generated in a smartphone application.

[1109] 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.

[1110] The present invention is a system for recognizing a user's emotional state and generating new literary works based on that state. In addition to the basic function of reading existing literary works and analyzing them to generate new works, the system can also recognize the user's emotions using an emotion engine and adjust the tone and style of the resulting work. Below, we will generate a program for this system and explain its processing in natural language. We will also demonstrate how the invention can be put into practice using concrete examples.

[1111] Program processing

[1112] 1. User uploads a PDF file

[1113] The user terminal specifies a PDF file of an existing literary work by the author they want to read and uploads it to the system. This PDF file is then sent to the server, which verifies the file's existence and reads its contents.

[1114] 2. The server extracts the text from the PDF file.

[1115] The server extracts text from the uploaded PDF file, which involves using an AI model to analyze and efficiently extract the text data within the PDF.

[1116] 3. User enters story prompt

[1117] The user then inputs the plot or storyline of a new literary work into the system as a prompt, which acts as a guideline for generating the new work.

[1118] 4. The server recognizes the user's emotions

[1119] The server recognizes the user's emotional state using an emotion engine, which analyzes the user's facial expressions and voice input to determine the user's current emotional state.

[1120] 5. The server generates new literary works based on emotions.

[1121] The server uses an AI model to generate new literary works based on the user's perceived emotional state. The AI ​​model adjusts the tone and style of the work depending on the user's emotions. In this process, the AI ​​mimics the author's writing style and literary style to generate new text.

[1122] 6. The server saves the new novel.

[1123] The server saves the generated new novel as a text file. This file is saved in a specified location for the user to access later. For example, the generated new literary work is saved as "New Work.txt."

[1124] 7. User gets new novel

[1125] The user can download the new novel from the specified location and begin reading it. The user can then download the "New Work.txt" file to their device and enjoy the content.

[1126] Specific examples

[1127] For example, suppose a user uploads an existing literary work, "Existing Work.pdf," to the system and enters "Sequel 10 Years Later" as the new story prompt. The emotion engine then recognizes the user's current emotional state as "Joy." As a result, the system adjusts the tone and style of the work according to the user's emotion of "Joy" and generates a new literary work that is bright and hopeful. This new work is then saved as "New Work.txt" for the user to download and enjoy.

[1128] This invention not only allows users to enjoy new works by deceased authors, but also provides a special experience tailored to their own emotional state.

[1129] The processing flow will be explained below.

[1130] Step 1:

[1131] The user selects a PDF file of an existing literary work and uploads it to the system from the user terminal. For example, the user selects "Existing Work.pdf" and sends the file to the system.

[1132] Step 2:

[1133] The server checks whether the uploaded PDF file exists. If the file does not exist, it returns an error message. If the file exists, the server reads its contents. The server stores the read PDF file in memory in binary format.

[1134] Step 3:

[1135] The server extracts text from the uploaded PDF file. The server uses an AI model to analyze the PDF's content and efficiently extract the text data, for example, extracting the text from each page and summarizing it as a whole.

[1136] Step 4:

[1137] The user inputs a prompt outlining the storyline or plot of the new work, for example, "a sequel set 10 years later." The user terminal then sends this prompt to the server.

[1138] Step 5:

[1139] The server recognizes the user's emotional state using an emotion engine, which analyzes the user's facial expressions and voice input to determine the user's current emotion, for example, by using data from a camera or microphone to detect the user's emotion.

[1140] Step 6:

[1141] The server uses an AI model to generate new literary works based on the user's perceived emotional state. The AI ​​model uses existing text data and prompts to create new works with a tone and style that matches the user's emotions. For example, if the user is feeling "joy," the server generates text with an upbeat and hopeful feel.

[1142] Step 7:

[1143] The server saves the new novel as a text file. The user can specify the location. For example, it can be saved as "New Work.txt."

[1144] Step 8:

[1145] The user downloads the new novel from the specified location. The user can then download the file "New Work.txt" to their device and read the contents.

[1146] This series of steps allows users to not only enjoy new works by deceased authors, but also experience literary works tailored to their own emotional state.

[1147] Example 2

[1148] 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."

[1149] With conventional document generation systems, it was difficult for users to reflect their own emotions and current state when creating a new document. Furthermore, when creating a new work based on an existing document, it was difficult to accurately reproduce the tone and style unique to that document. These issues led to a decrease in user satisfaction.

[1150] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1151] In this invention, the server includes means for reading an existing document from an input device, means for analyzing the text of the document read from the input device, means for generating a new document using the text data extracted by the analysis means based on instructions provided by a user, means for recognizing the emotional state of the user, means for adjusting the tone and style of the generated document based on the recognized emotional state, means for saving the generated new document, and means for providing the new document to the user, thereby making it possible to generate a new document that reflects the emotional state of the user.

[1152] An "input device" is a device for inputting existing document data into a computer system.

[1153] "Analysis means" refers to a technical method for extracting text information from imported document data and understanding its content.

[1154] The "generation means" is a technical method for creating a new document based on the extracted text data and instructions from the user.

[1155] "Emotional state" refers to the psychological and emotional state of the user as recognized from their facial expressions and voice.

[1156] "Means of recognition" refers to the technical methods and algorithms used to analyze and determine the user's emotional state.

[1157] "Tone and style" refers to the mood and writing characteristics of a document, which change depending on the emotional state.

[1158] "Storage means" refers to the technical method for storing the newly generated document as data and making it accessible later.

[1159] The "means of provision" refers to the technical method used to distribute the stored document data in a form that allows users to view and download it.

[1160] The present invention is a system for recognizing a user's emotional state and generating new literary works based on that. In addition to the basic function of reading an existing document from an input device and analyzing its content to generate a new work, the system can also recognize the user's emotions using an emotion engine and adjust the tone and style of the generated work.

[1161] Hardware and software used

[1162] Hardware

[1163] Server: The central processing device that analyzes documents, recognizes emotions, and generates new documents.

[1164] User device: A device that uploads documents and provides emotion data. This includes PCs, smartphones, tablets, etc.

[1165] Input device: A device for uploading PDF files. Typically, this corresponds to the file input function of a PC or smartphone.

[1166] software

[1167] Document analysis software: Used to extract text from PDFs, for example, Tesseract OCR.

[1168] Emotion recognition engine: Used to analyze user emotions. For example, Microsoft Azure's Emotion API is used.

[1169] Generative AI models: used to generate new documents, such as OpenAI's GPT-4.

[1170] Process Overview

[1171] 1. User uploads a PDF file

[1172] A user terminal uploads a PDF file containing an existing literary work by a particular author to the system, which then sends the uploaded PDF file to the server, which verifies the file's existence and reads its contents.

[1173] 2. The server extracts the text from the PDF file.

[1174] The server extracts text from the uploaded PDF file using document analysis software such as Tesseract OCR, which efficiently extracts the text data from the PDF.

[1175] 3. User enters story prompt

[1176] The user inputs the plot or storyline of a new literary work into the system as a prompt, providing a specific prompt such as "a sequel set 10 years later."

[1177] 4. The server recognizes the user's emotions

[1178] The server recognizes the user's emotional state using an emotion recognition engine that analyzes the user's facial expressions and voice to determine the user's current emotional state.

[1179] 5. The server generates a new document based on the emotion.

[1180] The server uses a generative AI model (e.g., GPT-4) to generate new literary works based on the user's emotional state. The AI ​​model mimics the author's writing style and adjusts the tone and style of the work depending on the user's emotions.

[1181] 6. The server saves the new document.

[1182] The server saves the generated new literary work as a text file called "New Work.txt" in a specified location for later access by the user.

[1183] 7. User retrieves new document

[1184] The user can download the generated new literary work from the specified location and begin reading it. The user can then download the file "New Work.txt" to their device and enjoy its contents.

[1185] Specific examples

[1186] For example, suppose a user uploads an existing literary work by a particular author ("Existing Work.pdf") to the system and enters "A Sequel 10 Years Later" as the new story prompt. The server uses an emotion engine to recognize the user's current emotional state as "Joy." As a result, the system adjusts the tone and style of the work according to the user's emotion of "Joy," generating a new literary work that is bright and hopeful. This new work is saved as "New Work.txt," and the user can download and enjoy it.

[1187] The above is a detailed description of the embodiment of the present invention.

[1188] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1189] Step 1:

[1190] User uploads a PDF file

[1191] A user terminal uploads a PDF file, which is an existing literary work by a specific author, to the system. This operation is performed by clicking the file selection button on the user interface and selecting the specified PDF file. As input, the PDF file selected by the user is given and sent to the server. The output is the PDF file received by the server.

[1192] Step 2:

[1193] The server extracts the text from the PDF file.

[1194] The server extracts text from the uploaded PDF file. Specifically, it uses document analysis software such as Tesseract OCR. The PDF file stored on the server is given as input, and text data is extracted from the PDF file. The output is the extracted text data. This text extraction operation is performed using text recognition processing using computer vision technology.

[1195] Step 3:

[1196] User enters story prompt

[1197] A user inputs the plot or storyline of a new literary work by entering text into input fields on a Web form and clicking a "Submit" button. The input is a text prompt provided by the user. The output is the prompt received by the server.

[1198] Step 4:

[1199] The server recognizes the user's emotions

[1200] The server uses an emotion recognition engine to recognize the user's emotional state. The server receives and analyzes facial and voice data sent from the user's device to determine the user's emotional state. The user's facial and voice data are given as input, and the emotion engine identifies the user's emotional state based on this. The output is the recognized user's emotional state.

[1201] Step 5:

[1202] The server generates new documents based on the emotion.

[1203] The server uses a generative AI model (e.g., GPT-4) to generate a new literary work based on the user's emotional state. The extracted text data, user-provided prompts, and the recognized emotional state are given as input. The generative AI model takes these data as input and generates the text of the new literary work. The output is the generated text of the new literary work. In this generation process, the generative AI model mimics the author's writing style, incorporating tone and style according to the emotion.

[1204] Step 6:

[1205] The server saves the new generated document.

[1206] The server saves the generated new literary work as a text file. As input, it takes the text of the generated new document and performs an operation to save it in a specific location. The output is a saved text file (e.g., "New Work.txt") in a directory that can be accessed by the user at a later date.

[1207] Step 7:

[1208] User gets new document

[1209] The user downloads the generated new literary work from the specified location and begins reading it. As input, the user is provided with the path to the saved text file and performs an operation to download it from the user's device. The output is the new document downloaded to the user's device. This operation is performed by clicking the download link on the user interface.

[1210] (Application example 2)

[1211] 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."

[1212] Conventional literary production generation systems have difficulty automatically generating works that reflect the user's emotional state. Furthermore, because they do not adjust the tone or style according to the user's emotions, the works provided often do not meet the user's expectations. This limits the quality of the user experience.

[1213] 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: means for reading an existing literary work from an input device; analysis means for analyzing the text of the literary work read from the input device; generation means for generating a new literary work using the text data extracted by the analysis means based on a prompt provided by the user; storage means for saving the new literary work generated by the generation means; and emotion recognition means for recognizing the emotional state of the user and adjusting the generation means based on the emotional state. This makes it possible to automatically generate a new literary work with a tone and style that matches the emotional state of the user.

[1214] "Existing literary works" are literary content that already exists, including books, stories, poems, etc.

[1215] An "input device" is a device that allows a user to supply data to a system, such as a PC, smartphone, or tablet.

[1216] The "reading means" has the function of receiving data from an input device and converting it into a format that can be used within the system.

[1217] The "analysis means" has the function of analyzing input text data and extracting necessary information.

[1218] A "prompt" is a user-provided guideline or instruction that indicates the theme or storyline for generating a new literary work.

[1219] A "generator" is a device that has the function of generating a new literary work based on the provided prompt and the analyzed text data.

[1220] "Storage means" are those that have the function of preserving the new literary works produced in digital form.

[1221] The "emotion recognition means" has a function for analyzing and determining the user's emotional state, and uses face recognition technology, voice analysis technology, and the like.

[1222] The invention is a system that recognizes a user's emotional state and generates new literary works based on it.

[1223] System Configuration

[1224] The system uses the following hardware and software components:

[1225] Hardware

[1226] Input device: The device through which the user uploads the PDF file, such as a computer, smartphone, or tablet.

[1227] Camera and microphone: A device for capturing a user's facial expressions and voice. For example, a camera and microphone built into a computer or smartphone can be used.

[1228] software

[1229] PDF Text Extraction Software: Software for extracting text data from PDF files, such as PDFMiner.

[1230] Emotion recognition software: Libraries such as EmotionRecognizer that analyze a user's facial expressions and voice to recognize emotions.

[1231] Generative AI models: AI models for generating new literary works based on the user's emotional state and prompts, such as OpenAI GPT-3.

[1232] Data processing and calculation

[1233] User operations

[1234] First, the user uploads a PDF file of an existing literary work by the author they want to read to the system using their input device, and then they need to enable the camera and microphone to recognize their emotional state.

[1235] Processing on the server

[1236] The server processes the data as follows:

[1237] 1. Extract PDF text:

[1238] The server uses PDFMiner to extract text data from uploaded PDF files, which later serves as the basis for generating new literary works.

[1239] 2. Enter and parse prompts:

[1240] Users input the plot or storyline of a new literary work as a prompt, which is parsed by a generative AI model and used as a guideline for generating the work.

[1241] 3. Emotion recognition:

[1242] The server uses EmotionRecognizer to recognize the user's current emotional state, specifically by using facial images captured by a camera and audio recorded by a microphone to identify the user's emotions.

[1243] 4. The creation of literary works:

[1244] The server uses a generative AI model (e.g., OpenAI GPT-3) to generate new literary works based on the user's perceived emotional state and the provided prompts. The generated works have a tone and style that correspond to the user's emotions.

[1245] 5. Saving Your Generated Work:

[1246] The generated new literary works are stored as text files by the server, so that users can download and read the new literary works at any time.

[1247] Specific examples

[1248] For example, suppose a user uploads "Existing Works.pdf" to the system and enters "A Future Detective Story" as the new story prompt. If the emotion recognition system then recognizes the user's emotional state as "Excited," the system will generate a new Future Detective Story with a tone and style that corresponds to the user's emotion of excitement.

[1249] Prompt example

[1250] Given the base text: The original text from the uploaded PDF, and the emotion: Excitement, generate a new literary work based on the prompt: A detective story set in the future.

[1251] In this way, new literary works tailored to the user's emotions are generated, providing a special reading experience.

[1252] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1253] Step 1:

[1254] The user uploads a PDF file of an existing literary work from an input device. The user provides the PDF file of the existing work to the system using a PC or smartphone. At this stage, the system checks the existence of the file and transfers it to the server.

[1255] Input: PDF files uploaded from a PC or smartphone

[1256] Output: PDF file saved on the server

[1257] Step 2:

[1258] The server uses PDFMiner to extract text data from the uploaded PDF file. The server parses the PDF file and extracts the text for each page and paragraph.

[1259] Input: PDF file stored on the server

[1260] Output: Extracted text data

[1261] Step 3:

[1262] The user inputs the plot or storyline of a new literary work into the system as a prompt, which the terminal sends to the system to use as a guideline for subsequent work generation.

[1263] Input: The prompt text entered by the user

[1264] Output: The prompt sent to the server

[1265] Step 4:

[1266] The server uses EmotionRecognizer to analyze the user's facial expressions and voice data acquired from the camera and microphone of the input device to recognize the user's emotional state. The server performs facial recognition and voiceprint analysis to identify the user's emotions in real time.

[1267] Input: User facial and voice data acquired from a camera and microphone

[1268] Output: Recognized emotional state of the user

[1269] Step 5:

[1270] The server generates new literary works using a generative AI model (e.g., OpenAI GPT-3) based on the recognized emotional state and prompt sentences. The generative AI model generates new text based on the input text data and prompts, adjusting the tone and style according to the user's emotions.

[1271] Input: Recognized emotional state, prompt sentence, extracted text data

[1272] Output: The new literary work generated

[1273] Step 6:

[1274] The server saves the new literary work as a text file, which the user can download and read later. The saved file is stored in a specified directory.

[1275] Input: A new literary work is generated.

[1276] Output: A text file saved on the server

[1277] Step 7:

[1278] A user uses a device to download a text file of a new literary work. The user can retrieve the file from the storage location and begin reading.

[1279] Input: A text file stored on the server

[1280] Output: A text file downloaded to the user's device.

[1281] 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.

[1282] 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.

[1283] 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.

[1284] 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.

[1285] 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.

[1286] 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.

[1287] 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).

[1288] 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.

[1289] 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."

[1290] 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.

[1291] 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).

[1292] 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.

[1293] 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.

[1294] 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.

[1295] 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.

[1296] 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.

[1297] 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.

[1298] 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.

[1299] 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.

[1300] 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.

[1301] 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.

[1302] The following is further disclosed regarding the above embodiment.

[1303] (Claim 1)

[1304] A means of reading existing literary works from an input device,

[1305] analysis means for analyzing the text of the literary work read from the input device;

[1306] generating means for generating a new literary work using the text data extracted by said analyzing means based on prompts provided by a user;

[1307] a storage means for storing the new literary work generated by the generation means;

[1308] A system including:

[1309] (Claim 2)

[1310] 2. The system of claim 1, wherein if the literary work read from the input device is in PDF format, the analyzing means includes means for extracting text from the PDF.

[1311] (Claim 3)

[1312] 10. The system of claim 1, wherein the generating means uses an artificial intelligence model to generate new literary works that mimic the writing style of existing literary works based on provided prompts.

[1313] "Example 1"

[1314] (Claim 1)

[1315] A means for users to upload PDF files;

[1316] The server must be able to verify the existence of the PDF file and read its contents.

[1317] A means by which the server extracts text from the PDF file;

[1318] a means for a user to input a story prompt;

[1319] A means for the server to generate new literary works using the generative AI model;

[1320] a means for the server to store the generated new literary works;

[1321] A system including:

[1322] (Claim 2)

[1323] 10. The system of claim 1, wherein the analyzing means includes means for using OCR technology for text extraction.

[1324] (Claim 3)

[1325] 10. The system of claim 1, wherein the generating means uses a generative AI model to mimic the writing style of an existing literary work based on user-provided prompts.

[1326] "Application Example 1"

[1327] (Claim 1)

[1328] A means of reading existing literary works from an input device,

[1329] analysis means for analyzing the text of the literary work read from the input device;

[1330] generating means for generating a new literary work using the text data extracted by said analyzing means based on prompts provided by a user;

[1331] a storage means for storing the new literary work generated by the generation means;

[1332] a distribution means for notifying a user of the newly generated literary work and allowing the user to view or download the generated work through a smartphone application;

[1333] A system including:

[1334] (Claim 2)

[1335] 2. The system of claim 1, wherein if the literary work read from the input device is in PDF format, the analyzing means includes means for extracting text from the PDF.

[1336] (Claim 3)

[1337] 10. The system of claim 1, wherein the generating means uses an artificial intelligence model to generate new literary works that mimic the writing style of existing literary works based on provided prompts.

[1338] "Example 2: Combining Emotion Engines"

[1339] (Claim 1)

[1340] means for reading an existing document from an input device;

[1341] means for analyzing the text of a document read from the input device;

[1342] means for generating a new document using the text data extracted by said analyzing means based on instructions provided by a user;

[1343] means for recognizing the emotional state of a user;

[1344] means for adjusting the tone and style of the generated document based on said recognized emotional state;

[1345] means for storing the generated new document;

[1346] a means for providing new documents to the user;

[1347] A system including:

[1348] (Claim 2)

[1349] 2. The system according to claim 1, wherein, when the document read from the input device is in a specific file format, the analyzing means includes means for extracting text in that file format.

[1350] (Claim 3)

[1351] 10. The system of claim 1, wherein the generating means uses a machine learning model to generate new documents that mimic the writing style of existing documents based on provided instructions.

[1352] "Application example 2 when combining emotion engines"

[1353] (Claim 1)

[1354] A means of reading existing literary works from an input device,

[1355] analysis means for analyzing the text of the literary work read from the input device;

[1356] generating means for generating a new literary work using the text data extracted by said analyzing means based on prompts provided by a user;

[1357] a storage means for storing the new literary work generated by the generation means;

[1358] emotion recognition means for recognizing an emotional state of a user and adjusting the generating means based on the emotional state;

[1359] A system including:

[1360] (Claim 2)

[1361] 2. The system of claim 1, wherein if the literary work read from the input device is in PDF format, the analyzing means includes means for extracting text from the PDF.

[1362] (Claim 3)

[1363] 10. The system of claim 1, wherein the generating means uses a generative AI model to generate new literary works that mimic the writing style of existing literary works based on provided prompts. [Explanation of symbols]

[1364] 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 of reading existing literary works from an input device, analysis means for analyzing the text of the literary work read from the input device; generating means for generating a new literary work using the text data extracted by said analyzing means based on prompts provided by a user; a storage means for storing the new literary work generated by the generation means; A system including:

2. 2. The system of claim 1, wherein if the literary work read from the input device is in PDF format, the analyzing means includes means for extracting text from the PDF.

3. 10. The system of claim 1, wherein the generating means uses an artificial intelligence model to generate new literary works that mimic the writing style of existing literary works based on provided prompts.

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