System

Abstract

A system is provided.SOLUTION: Means for converting a handwritten character sample provided by a user into digital data using an optical character recognition technology, means for generating a user-specific font from the handwritten character digital data, means for using a natural language processing technology for analyzing a sentence sample provided by the user and learning a stylistic feature, means for automatically generating content designated by the user as a handwriting-like sentence using the generated font and the learned stylistic feature, and means for displaying the automatically generated handwriting-like sentence to the user, A system comprising: means for enabling editing; and means for outputting the edited text in a specified format.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Even today, there are many situations where documents must be created by hand, such as New Year's cards, apology letters, and paper medical records. This type of handwriting work is time-consuming, and any mistakes require starting over from scratch, making it extremely inefficient. Furthermore, it is difficult to generate large quantities of documents while preserving the uniqueness and individuality of handwriting. There is a growing need for a system that can solve these problems and efficiently provide the value of handwriting. [Means for solving the problem]

[0005] The present invention includes a means for converting a handwritten character sample provided by a user into digital data using optical character recognition technology and generating a user-specific font from the digital handwritten character data. It also includes a means for analyzing a text sample provided by the user and using natural language processing technology to learn stylistic features. This provides a means for automatically generating handwritten-style text based on content specified by the user using the generated font and the learned stylistic features. It also includes a means for displaying the automatically generated handwritten-style text to the user and making it editable, allowing the user to confirm and correct the generated document and ultimately output it in a specified format. This makes it possible to efficiently generate documents that retain the value of handwriting while significantly reducing the effort required for handwriting.

[0006] "User" means any person or entity that provides handwriting samples or text samples for use in generating handwritten-look documents using the system.

[0007] "Handwriting sample" refers to an image or digital data of a user's own handwriting that the user provides to the system.

[0008] "Optical character recognition technology (OCR)" refers to technology that automatically reads handwritten or printed characters from scans or image data and converts them into digital text.

[0009] "Digital data" refers to data that electronically represents handwritten characters, which are analog information, using OCR technology or other methods.

[0010] "Font" refers to a set of characters in a digital format for visually representing characters in a particular style or design.

[0011] "Natural language processing technology (NLP)" is a general term for technologies that allow computers to understand, interpret, and generate human language.

[0012] "Stylistic features" refer to consistent patterns of writing and expression in the texts written by a user.

[0013] "Sample writing" refers to data that provides the system with examples of writing that a user has written in the past.

[0014] "Handwritten text" refers to a form of text in which the generated text mimics the user's handwriting style and appears to be handwritten.

[0015] "Specified format" refers to a format for outputting a document in a specific file format (e.g., PDF, JPEG) desired by the user. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] The present invention provides a system that automatically generates handwritten-style documents by learning the characteristics of handwritten characters and writing styles provided by a user. This system is configured as follows.

[0038] First, the user provides the system with samples of their handwriting and previous writing, which can be in the form of scanned images, digital photographs of their handwriting, or text files.

[0039] The server converts the handwritten sample provided by the user into digital data using OCR (optical character recognition) technology, which involves detailed analysis of the contours and shape characteristics of each character and then generates a unique font for the user.

[0040] The server then analyzes the user-provided text samples using natural language processing (NLP) techniques, learning the user's writing style, including their grammar, punctuation, and sentence structure.

[0041] The user inputs the content of the document they want to create through the system interface, and the terminal checks the input and sends it to the server.

[0042] The server converts the input content into text in the user's writing style based on the input content and the learned writing style characteristics.The server then converts the generated text into a handwritten-style text using the user's unique font and generates a preview image.

[0043] The device displays a preview of the generated handwritten document to the user, who can check the content and appearance of the document and make edits as needed. Once editing is complete, the server outputs the final handwritten document in the specified format (e.g., PDF, JPEG) and sends it to the device.

[0044] Specific examples

[0045] As a specific example, a scene in which a user creates a New Year's card as a New Year's greeting will be described.

[0046] 1. Providing handwritten data

[0047] The user uploads to the system a sample of a handwritten New Year's card they have created in the past, along with a text file of an apology letter. The handwritten sample includes greetings such as "Happy New Year."

[0048] 2. Handwritten Character Recognition

[0049] The server uses OCR technology to recognize the uploaded New Year's card sample and extracts the shape patterns of each character in "Happy New Year" and "Thank you for your continued support this year" as digital data.

[0050] 3. Font Generation

[0051] The server generates a user - specific font based on the recognized shape pattern of the handwritten characters. For example, it saves the unique handwriting of characters such as "明" and "年" as fonts.

[0052] 4. Learning of writing style

[0053] The server analyzes the apology text file using NLP technology and learns expression patterns such as "ご迷惑をお掛けして申し訳ございません" and sentence structures.

[0054] 5. Input of generation request

[0055] The user inputs "Please write a New Year greeting" through the interface. Specifically, a sentence such as "新年あけましておめでとうございます。今年もよろしくお願いいたします。" is requested.

[0056] 6. Automatic generation of document

[0057] The server generates a sentence such as "新年あけましておめでとうございます。今年もよろしくお願いいたします。" based on the input content and the learned writing style features. Then, it creates a handwritten - style New Year card using the user - specific font.

[0058] 7. Display and editing of results

[0059] The terminal previews and displays the generated handwritten - style New Year card for the user to check. For example, it can be edited to "新年あけましておめでとうございます。今年もどうぞご健康でありますように。".

[0060] 8. Output of document [[ID=_{37}]]

[0061] If the user is satisfied with the document, the server generates the final handwritten - style New Year card in PDF format and sends it to the terminal. The user can print this and use it as an actual New Year card.

[0062] This system allows users to easily generate high-quality handwritten-looking documents that utilize their own handwritten characters and writing style, while eliminating the need for handwriting.

[0063] The processing flow will be explained below.

[0064] Step 1: User provides handwritten data

[0065] Through the system's interface, users upload handwriting samples and past writing samples, which can include scanned images, digital photographs of handwriting, or text files.

[0066] Step 2: The server recognizes the handwritten characters

[0067] The server converts the uploaded handwritten samples into digital data using OCR (optical character recognition) technology, which extracts the outline and shape of each character and generates a digital representation of the character.

[0068] Step 3: The server generates the font

[0069] The server generates a user-specific font from the digital data of the handwritten characters, which involves detailed analysis of the character shape patterns to create a font that reflects the user's unique handwriting and style.

[0070] Step 4: The server learns the writing style

[0071] The server analyzes the sample text provided by the user using NLP (natural language processing) technology, specifically extracting and learning stylistic features such as expression patterns, punctuation, and sentence structure used in the text.

[0072] Step 5: User enters generation request

[0073] The user uses the system's interface to input the content of the document they want to generate, which may include entering a specific phrase or sentence.

[0074] Step 6: The server automatically generates the document

[0075] The server converts the specified content into text in the user's writing style based on the input content and the learned writing style characteristics. For example, it generates a document based on the input content, such as a greeting for a New Year's card or an apology letter.

[0076] Step 7: The server converts it to handwritten style

[0077] The server converts the generated text into a handwritten style using the user's own font, resulting in a visual representation of the text as if it were handwritten.

[0078] Step 8: Your device will display the results

[0079] The device displays a preview of the generated handwritten document to the user, allowing the user to check the content and appearance of the document and make edits as needed.

[0080] Step 9: User edits the document

[0081] The user can edit the generated document through the system interface, for example by correcting parts of the text or inserting additional messages.

[0082] Step 10: The server outputs the final document

[0083] The server generates a document in the specified format (PDF, JPEG, etc.) that has been finalized by the user, and sends the generated document to the terminal so that the user can download or print it.

[0084] Example 1

[0085] 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."

[0086] In recent years, there has been an increasing need for users to efficiently generate documents that look handwritten. However, with conventional technologies, it has been difficult to efficiently convert handwritten characters and writing styles into digital data and automatically generate documents that look handwritten. In particular, generating a user-specific font or learning a writing style from past documents and reflecting it in new documents requires advanced technology, making it difficult to achieve.

[0087] 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.

[0088] In this invention, the server includes means for converting handwritten character samples provided by a user into digital data using optical character recognition technology, means for generating a user-specific font from the digital handwritten character data, and means for analyzing the user-provided text samples and learning stylistic features using natural language processing technology, thereby enabling the user to automatically generate handwritten-look documents that reflect their own handwriting and writing style.

[0089] "Handwriting sample" refers to a user-provided material containing handwritten characters, and refers to digital data such as a scanned image or digital photograph.

[0090] "Optical character recognition technology" is a technology that analyzes the character information contained in images and photographs and converts it into text data.

[0091] "Digital data" refers to information that has been converted into a form that a computer can understand and process.

[0092] A "user-specific font" is a font that reproduces the characteristics of the user's handwritten characters, and refers to font data that includes individual character shapes and handwriting characteristics.

[0093] "Sample writing" refers to digital data of previously written writing provided by the user, which is a text file for learning stylistic features.

[0094] "Natural language processing technology" refers to technology that enables computers to understand, analyze, and generate human language.

[0095] "Interface" refers to the screen and operating environment through which a user interacts with a system and inputs information.

[0096] "Handwritten-style text" refers to a digital document that is generated using a font that reproduces the user's handwriting and that looks handwritten.

[0097] "Preview" refers to a function that displays the shape and content of the final generated document in a state that allows the user to check it.

[0098] "Format" refers to the output format of the digital document, including file formats such as PDF and JPEG.

[0099] The present invention is a system that learns handwritten characters and writing styles provided by a user and automatically generates handwritten-style documents based on them. This system is composed of a server, a terminal, and a user interface, and is implemented using the following hardware and software.

[0100] The server converts handwritten samples provided by the user into digital data using optical character recognition technology (OCR). The specific OCR software used is "Tesseract OCR." This allows for detailed analysis of the contours and shapes of the handwritten characters, and the server uses the results to generate a digital font unique to the user. The font creation tool "FontForge" is used to generate the font.

[0101] The server then analyzes the user-provided text samples using natural language processing (NLP) techniques, such as "spaCy" and "NLTK." Through this analysis, the server learns the user's writing style, including their grammar, punctuation, and sentence structure.

[0102] The user inputs the content of the document they want to generate through the interface. Specifically, the user uses a terminal to input a prompt such as "Please write a New Year's greeting." This input is sent from the terminal to the server. The server generates a sentence based on the input content and pre-trained stylistic features. The generated sentence is then converted to look like handwriting using a user-specific font.

[0103] The terminal displays the generated handwritten document preview to the user. The user can check the preview content on the terminal and edit it as necessary. For example, the sentence "Happy New Year. I hope you will continue to work hard this year." can be changed to "Happy New Year. I hope you will be healthy this year."

[0104] Finally, the server outputs the edited text in the specified format (e.g., PDF, JPEG) and sends it to the terminal, where the user can download the final document, print it, and use it as an actual New Year's card, etc.

[0105] A concrete example of a prompt is as follows:

[0106] "Please upload a handwriting sample."

[0107] "Please upload a sample of your previous writing."

[0108] Please write a New Year's greeting.

[0109] Please review the preview and make any edits you need.

[0110] This system allows users to efficiently generate high-quality handwritten documents that reflect their own handwriting style and idiom.

[0111] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0112] Step 1: User provides handwriting and writing samples

[0113] The user accesses the system interface and uploads an image file (e.g., PNG, JPEG) of a handwriting sample and a text file (e.g., TXT, DOC) of a writing sample. The image file and the text file are provided to the system as input. These files are sent to the server as output, ready for the next analysis step.

[0114] Step 2: The server analyzes the handwriting sample using OCR technology

[0115] The server receives the handwritten sample image uploaded by the user and begins analyzing it using "Tesseract OCR." It receives the image file of the handwritten sample as input and uses OCR technology to analyze the outlines and shapes of the characters. Digital data for each character is extracted as output and stored on the server. The server then uses this data to prepare for font generation.

[0116] Step 3: The server generates a font from the handwritten characters

[0117] The server uses the font creation tool "FontForge" to generate a user-specific font based on the digital data of handwritten characters obtained using OCR technology. Using the digital data of the characters as input, it runs an algorithm to extract the shape patterns of the characters. The output is a user-specific font file (e.g., .ttf or .otf), which is stored on the server.

[0118] Step 4: The server analyzes the text sample using natural language processing

[0119] The server uses "spaCy" or "NLTK" to analyze the text samples uploaded by users. It receives the text sample as input and uses natural language processing technology to analyze the sentence's expression patterns, punctuation usage, sentence structure, etc. The output is stylistic features extracted and stored on the server. The server uses this data to prepare the document to be generated.

[0120] Step 5: The user enters the generation request in the interface.

[0121] The user uses the system's interface to input the content of the document they want to generate. Specifically, they type "Please write a New Year's greeting" and click the send button. This sends the generation request content as input to the server. The server then receives the input and is ready to proceed to the next step.

[0122] Step 6: The server generates the document content and converts it to a handwritten style.

[0123] The server uses natural language processing technology to generate text based on the generation request received from the user and the stylistic features it has previously learned. The generation request and stylistic feature data are used as input. A font specific to the user is used to convert the generated text into a handwritten-style text. The document converted into a handwritten-style text is generated as output as preview image data and saved on the server.

[0124] Step 7: The device will display a preview of the generated document

[0125] The terminal displays a handwritten document preview sent from the server to the user. It receives preview image data from the server as input and displays it on the interface. The user can check the displayed preview and is ready to make corrections if necessary.

[0126] Step 8: User reviews and edits the document

[0127] The user checks the preview document displayed on the terminal and edits the text content as necessary. For example, they edit the text from "Happy New Year. Thank you for your continued support this year." to "Happy New Year. I hope you stay healthy this year." The corrected text content is sent as input to the server. The server generates a new preview image reflecting the edited content as output.

[0128] Step 9: The server outputs the final document and sends it to the device.

[0129] The server receives final confirmation of the edits from the user and generates the final document in the specified format (e.g. PDF, JPEG). It receives the edited text data as input. It saves the final document as output in PDF or JPEG format and sends it to the terminal. The user can download the final document from the terminal and print it to use as an actual New Year's card, etc.

[0130] The above is the specific processing flow of the program.

[0131] (Application example 1)

[0132] 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."

[0133] Previous systems for generating handwritten-style messages not only required a complex and time-consuming process for training handwriting samples and writing styles, but also resulted in messages that were not effectively integrated into product images. Furthermore, there was a lack of an easy way for users to add handwritten-style messages when customizing their orders.

[0134] 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.

[0135] In this invention, the server includes means for converting handwritten sample text provided by a user into digital data using optical character recognition technology, means for generating a user-specific font from the digital data of the handwritten text, means for analyzing the user-provided text sample and learning stylistic features using natural language processing technology, means for automatically generating handwritten-style text based on content specified by the user using the generated font and the learned stylistic features, means for displaying the automatically generated handwritten-style text to the user and making it editable, means for generating a handwritten-style message to accompany a product and combining it with a product image, and means for outputting the edited text in a specified format. This allows users to easily create and edit handwritten-style messages and integrate them with product images when placing orders.

[0136] "Handwritten characters" refer to characters written by a user by hand.

[0137] A "sample" is a portion of handwritten text or a sentence that is provided to the system for learning.

[0138] "Optical character recognition technology" is a technology that converts character information acquired as an image into digital data.

[0139] "Digital data" refers to the digitized form of analog data used to process and store information electronically.

[0140] A "font" is a set of characters created according to specific design rules.

[0141] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[0142] "Stylistic features" refer to the characteristics of individual writing styles, such as sentence expression patterns, use of punctuation, and sentence structure.

[0143] "Handwritten-style text" refers to text that is generated from digital data in a format that looks like handwriting.

[0144] "User" refers to an individual or corporation that uses the system.

[0145] The "means for enabling editing" refers to an interface that allows the user to check the generated handwritten-style text and make changes as necessary.

[0146] The "specified format" refers to the format in which the final handwritten-style text is output, such as PDF or JPEG.

[0147] "Products" means goods and services sold in the Virtual Store.

[0148] "Message" refers to system-generated text that a user can accompany with an item.

[0149] "Product images" refers to visual data that represents a product, including photographs and illustrations.

[0150] "Synthesis" refers to the process of combining multiple pieces of data into one.

[0151] This invention relates to a system that learns a user's handwriting and writing style, generates handwritten-style messages, and combines them with product images. As an embodiment of the invention, a specific description will be given using an application example in which handwritten-style messages are attached to products in a virtual store.

[0152] System Configuration

[0153] Providing a sample of handwriting

[0154] Users upload handwriting samples to the system as image data captured using a smartphone or scanner, which is then sent to a server where it is converted into digital data using optical character recognition (OCR) technology.

[0155] Font Generation

[0156] The server uses OCR technology to generate a unique font for each user from the digital data of handwritten characters. Software such as Tesseract and OpenCV are used to extract the outline and shape characteristics of the characters to create a unique font.

[0157] Studying writing style

[0158] Users provide the system with text files containing examples of their writing. The text is then sent to a server, where natural language processing (NLP) techniques are used to learn stylistic features. This process uses NLP libraries such as NLTK and SpaCy.

[0159] Message Generation

[0160] The user inputs a handwritten-style message to accompany the product order through the system interface. The server converts the input message into a handwritten-style sentence using the user's writing style and a custom font. The generated handwritten-style message is then superimposed onto the product image. The image processing used here includes "PIL" and "OpenCV."

[0161] Viewing and editing messages

[0162] The generated handwritten message is previewed on the user's device, and the user can review the preview and edit the message as needed. The edited content is then resent to the server and converted back into handwritten text.

[0163] Final Output

[0164] The final handwritten message is then generated in a specified format (PDF, JPEG, etc.) and sent to the user's device. The user can then download this data and use it to order products.

[0165] Specific examples of use

[0166] For example, consider a scenario in which a user orders a birthday gift from a virtual store and creates a handwritten message saying "Happy Birthday" to accompany the gift. The user uploads images of handwritten text such as "Thank you" and "Congratulations" to the system as samples, and provides a text file of a blog post they previously wrote as a writing style sample. After that, by entering "Happy Birthday," the server combines the handwritten message with the product image and displays a preview. After the user has confirmed and edited the message, the final message is output in the specified format, completing the ordering process.

[0167] Prompt Sentence Examples

[0168] "I want to create handwritten birthday messages. Develop an application that learns the user's handwriting and writing style to generate 'Happy Birthday' messages."

[0169] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0170] Step 1: Provide a sample of your handwriting

[0171] Users use their smartphones or scanners to capture sample images of handwritten characters and upload them to the system, which then sends the image data to the server.

[0172] Input: Handwritten text image file (JPEG or PNG format)

[0173] Output: Image data stored on the server

[0174] Step 2: Recognizing handwriting

[0175] The server analyzes the received sample image of handwritten characters using OCR technology. Specifically, it reads the image using OpenCV and recognizes the characters using Tesseract.

[0176] Input: Image data of handwritten characters

[0177] Output: Character string converted into digital data by OCR

[0178] Step 3: Font generation

[0179] The server extracts the outline and shape characteristics of the characters recognized by OCR and generates a unique font using an outline extraction algorithm.

[0180] Input: Character string data obtained by OCR

[0181] Output: User-specific font data

[0182] Step 4: Provide a writing sample

[0183] Users upload samples of their writing (text files) to the system, and this data is sent to the server.

[0184] Input: Text file (TXT format)

[0185] Output: Text data stored on the server

[0186] Step 5: Learning stylistic features

[0187] The server analyzes the received text samples using natural language processing technology to learn the user's writing style. This analysis involves analyzing the structure of the sentences using NLTK and SpaCy.

[0188] Input: Text sample data

[0189] Output: Stylistic feature data

[0190] Step 6: Enter a Message Generation Request

[0191] The user inputs the message content they want to create through the system interface, and this content is sent to the server.

[0192] Input: Message content (e.g. "Happy Birthday")

[0193] Output: Message content saved on the server

[0194] Step 7: Create a handwritten message

[0195] The server converts the input message content into a handwritten-like form using the user's writing style and a custom font, and then generates an image of the message using PIL (Python Imaging Library).

[0196] Input: Message content, style feature data, unique font data

[0197] Output: Handwritten message image

[0198] Step 8: Adding a message to product images

[0199] The server synthesizes the generated handwritten message onto the product image using OpenCV and PIL.

[0200] Input: Handwritten message image, product image

[0201] Output: Product image with message

[0202] Step 9: Review and edit your message

[0203] The terminal displays a preview of the product image with the generated handwritten message to the user, who can check it and edit the message as necessary.

[0204] Input: Product image with message

[0205] Output: Message content edited by the user

[0206] Step 10: Final output

[0207] The server outputs the final edited handwritten-style message in a specified format (PDF, JPEG, etc.) and sends the final data to the terminal.

[0208] Input: Edited message content, product image

[0209] Output: Final output data in the specified format

[0210] 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.

[0211] The present invention is a system that automatically generates handwritten-style documents by learning the characteristics of handwritten characters and sentence samples provided by the user, and further combines it with an emotion engine that recognizes the user's emotions. The system consists of the following components:

[0212] First, users upload samples of their handwriting and previous writing to the system, which can include scanned images, digital photographs of their handwriting, text files, etc. The samples are used as the basis for recognizing the shape of the handwriting.

[0213] The server converts the uploaded handwritten text samples into digital data using OCR (optical character recognition) technology, extracting the outline and shape of each character and generating a custom font for the user. This creates the foundation for digitally reproducing handwritten text.

[0214] The server then analyzes the user-provided text samples using natural language processing (NLP) techniques. This analysis extracts stylistic features such as expression patterns, punctuation usage, and sentence structure within the text, and learns the user's unique writing style. This allows the generated documents to faithfully reproduce the user's writing style.

[0215] Furthermore, an emotion engine is built into the server, which analyzes the text entered by the user and recognizes emotions from it. The emotion engine learns the emotional patterns inherent in the user's text and reflects these when generating text. At the same time, it analyzes the user's emotional state in real time and suggests appropriate writing styles and expressions.

[0216] The user can input the content of the document they want to create through the system interface. For example, they can input the content of a New Year's greeting card or an apology letter. The input data is then sent to the server.

[0217] The server converts the specified content into a sentence in the user's writing style based on the input content and the learned stylistic features and emotional information, and then converts it into a handwritten-style sentence using a handwriting font to generate a preview image.

[0218] The terminal displays these handwritten-look documents to the user in a preview format. The user can check the document contents and edit them as necessary. Once editing is complete, the server outputs the final handwritten-look document in the specified format (e.g., PDF, JPEG) and sends it to the terminal.

[0219] Specific examples

[0220] As a specific example, a scene in which a user creates a New Year's card as a New Year's greeting will be described.

[0221] 1. The user uploads a sample handwritten New Year's card they have created in the past and a text file containing the text. The sample New Year's card contains greetings such as "Happy New Year."

[0222] 2. The server uses OCR technology to recognize the New Year's card sample and extracts the character shapes of "Happy New Year" and "Thank you for your continued support this year" as digital data.

[0223] 3. The server generates a user - specific font based on the shape pattern of handwritten characters. As a result, the unique handwriting of characters such as "明" and "年" is saved as a digital font.

[0224] 4. The server analyzes the text file of the article using NLP technology and learns expression patterns such as "申し訳ございませんが、ご迷惑をおかけしました" and the sentence structure.

[0225] 5. The emotion engine analyzes the emotion from the user's input content and past articles and adjusts the appropriate expressions and writing styles.

[0226] 6. The user inputs a "New Year greeting text" through the interface and requests the system to generate a New Year card.

[0227] 7. Based on the input content, learned writing style characteristics, and emotion information, the server generates a sentence such as "Happy New Year. I look forward to your continued support this year." Furthermore, a handwritten - style New Year card is created using the user - specific font.

[0228] 8. The terminal previews and displays the generated handwritten - style New Year card for the user to check the content. For example, it can be edited to "Happy New Year. May you be in good health this year."

[0229] 9. When the user is satisfied with the document, the server generates the final handwritten - style New Year card in PDF format and sends it to the terminal. The user can download this and print it to use as an actual New Year card.

[0230] With this system, the user can generate a high - quality handwritten - style document that utilizes their own handwritten characters and writing style, while also appropriately reflecting emotions, without the hassle of handwriting.

[0231] The processing flow will be described below.

[0232] Step 1: User provides handwritten data

[0233] Through the system's interface, users upload handwriting samples and samples of previous writing, including scanned images, digital photographs, and text files, and the system begins to recognize the user's unique writing and writing characteristics.

[0234] Step 2: The server recognizes the handwritten characters

[0235] The server receives the uploaded handwritten samples and converts them into digital data using OCR (optical character recognition) technology. The server extracts the outline and shape of each character and generates a digital representation of the character, which provides the basis for creating a handwritten font.

[0236] Step 3: The server generates the font

[0237] The server generates a user-specific font based on the digital data of handwritten characters, based on a detailed analysis of the shape patterns of each character. The generated font preserves the user's unique handwriting and style and is used for subsequent text generation.

[0238] Step 4: The server learns the writing style

[0239] The server uses natural language processing (NLP) technology to analyze the sample text provided by the user, extracting stylistic features such as expression patterns, punctuation usage, and sentence structure. This allows the system to learn the user's unique writing style and reflect it in the text it generates.

[0240] Step 5: The server recognizes the emotion

[0241] The server's built-in emotion engine recognizes emotions from user-provided text and real-time input. The server performs sentiment analysis based on keywords and expressions contained in the text and adds this to the user's writing style data.

[0242] Step 6: User enters generation request

[0243] The user uses the system interface to input the content of the document they want to generate, for example, by making a request such as "Please write a New Year's greeting." The input is then sent to the server.

[0244] Step 7: The server automatically generates the document

[0245] The server converts the specified content into a sentence in the user's writing style based on the content entered by the user, the learned stylistic features, and emotional information. The generated sentence is then converted into a handwritten-style sentence using the user's own font.

[0246] Step 8: Your device will display the results

[0247] The device displays a preview of the handwritten document to the user. The user can check the content and appearance of the document and edit it as needed. For example, they can edit the greeting text in a New Year's card or an apology letter.

[0248] Step 9: User edits the document

[0249] The user edits the generated document through the system interface, for example, by changing part of the greeting or inserting an additional message. Once the editing is complete, the user performs a final confirmation.

[0250] Step 10: The server outputs the final document

[0251] The server generates a document in the specified format (PDF, JPEG, etc.) that has been finalized by the user, and the generated document is sent to the terminal, where the user can download or print it.

[0252] Through the above processing steps, users can reduce the effort required for handwriting, automatically generate documents that make use of their own handwritten characters and writing style, and can also take advantage of high-quality handwritten-style documents that appropriately reflect emotions using an emotion engine.

[0253] Example 2

[0254] 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."

[0255] Conventional handwritten-style document generation systems have difficulty fully reflecting the characteristics and style of a user's handwriting, and lack the ability to recognize the emotion of the user's input text and reflect it in the document. This makes it difficult to automatically generate documents personalized for each user. Furthermore, the generated documents cannot be easily previewed or edited, resulting in a problem of low user convenience.

[0256] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for converting handwritten character samples provided by the user into digital data using optical character recognition technology, means for generating a user-specific font from the handwritten character digital data, means for analyzing text samples provided by the user and using natural language processing technology to learn stylistic features, and means for analyzing the content of text entered by the user, recognizing emotions, and reflecting the emotions in document generation. This enables the automatic generation of personalized handwritten-style documents that reflect the user's handwritten character features, style, and emotions.

[0257] A "handwriting sample" is user-provided handwriting data in digital form or as a scanned image.

[0258] "Optical character recognition technology" is a technology for converting characters in an image into digital data.

[0259] "Digital data" is data in a format that can be processed by a computer.

[0260] A "user-specific font" is a special font that is generated based on the characteristics of the user's handwriting.

[0261] "Natural language processing technology" is a technology that analyzes text data and understands and processes stylistic features and content.

[0262] "Style characteristics" are characteristics such as expression patterns, use of punctuation marks, sentence structure, etc., when a particular user writes a sentence.

[0263] "Emotion recognition" is a technology that analyzes and identifies emotions from the content of a user's writing.

[0264] "Handwritten text" is text in digital form that reflects a user's handwriting and writing style.

[0265] "Automatic generation" means that the system creates an artifact through a specific process without human intervention.

[0266] A "preview format" is a temporary or provisional display format used to confirm the final result.

[0267] "Making it editable" means allowing a user to make changes or modifications to the generated document.

[0268] "Specified format" refers to the particular format in which a document or data is saved (e.g., PDF, JPEG).

[0269] This invention is a system that automatically generates handwritten documents by allowing a user to provide handwritten and written samples, and further combines it with an emotion engine that recognizes the user's emotions. The system includes multiple means for digitally reproducing the user's handwriting and writing style and automatically generating documents that reflect the user's emotions.

[0270] Users first upload samples of their handwriting and previous writing to the system, which can include scanned images, digital photographs of their handwriting, text files, etc. These samples are used as the basis for recognizing the shape of the handwriting.

[0271] The server converts the uploaded handwritten sample into digital data using OCR (Optical Character Recognition) technology. Specifically, the OCR technology used is "Tesseract OCR." The outline and shape of each character are extracted and a custom font is generated based on this. This font reflects the user's unique handwriting.

[0272] The server then analyzes the user-provided text samples using NLP (natural language processing) technology, specifically technologies such as "spaCy" and "NLTK." This analysis extracts stylistic features such as expression patterns, punctuation usage, and sentence structure within the text, and learns the user's unique writing style.

[0273] Furthermore, an emotion engine is built into the server, which analyzes the text entered by the user and recognizes emotions from it. For example, the "Sentiment Analysis API" is used. This emotion engine learns the emotional patterns inherent in the user's text and reflects them when generating text. At the same time, it analyzes the user's emotional state in real time and suggests appropriate writing styles and expressions.

[0274] The user inputs the content of the document to be generated through the system interface. For example, input the content of the New Year greeting or the apology letter. The input data is sent to the server.

[0275] Based on the input content, the learned writing style characteristics and sentiment information, the server formulates the specified content in the user's writing style. Furthermore, it uses the font of handwritten characters to convert it into a handwritten style and generates a preview image.

[0276] The terminal displays these handwritten-style documents to the user in a preview format. The user can check the document content and make edits if necessary. For example, it can be edited to "Happy New Year. May you be healthy this year too." After the editing is completed, the server outputs the final handwritten-style document in a specified format (e.g., PDF or JPEG format) and sends it to the terminal. The user can download this and print it to use as an actual New Year greeting card.

[0277] As a specific example, it shows the scenario where the user creates a New Year greeting card as a New Year greeting. The user uploads a handwritten New Year greeting card sample created in the past and a text file of the article to the system. The New Year greeting card sample contains greeting sentences such as "Happy New Year."

[0278] The server recognizes the New Year greeting card sample with OCR technology and extracts the character shapes of "Happy New Year" and "Please take care of me this year too" as digital data. Next, a user-specific font is generated based on the shape pattern of handwritten characters. In this way, the unique handwriting of characters such as "明" and "年" is saved as a digital font.

[0279] Also, the server analyzes the text file of the article with NLP technology and learns expression patterns such as "I'm very sorry for the trouble" and the sentence structure. The sentiment engine analyzes the sentiment from the user's input content and past articles and adjusts the appropriate expressions and writing styles.

[0280] The user inputs a "New Year's greeting" into the interface and requests the system to generate a New Year's card. Based on the input content and the learned stylistic features and emotional information, the server generates the phrase "Happy New Year. Thank you for your continued support this year," and creates a handwritten-style New Year's card using the user's own font.

[0281] The device displays a preview of the generated New Year's card, allowing the user to confirm its contents. Once the user is satisfied with the document, the server generates the final New Year's card in PDF format and sends it to the device. The user can then download it, print it, and use it as a New Year's card.

[0282] This system allows users to generate high-quality handwritten-looking documents that utilize their own unique handwriting and writing style, while reducing the effort required for handwriting, and that also appropriately reflect emotions.

[0283] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0284] Step 1:

[0285] Input: The user uploads handwriting samples (scanned images, digital handwriting photographs) and text samples (text files) to the system.

[0286] Specific operation: The user uploads these samples through the system interface. For example, they provide the system with a scanned image of a handwritten New Year's card that reads "Happy New Year" and a corresponding text file.

[0287] Output: The uploaded sample data is saved on the server.

[0288] Step 2:

[0289] Input: The server converts handwritten samples into digital data using OCR technology.

[0290] How it works: The server analyzes the uploaded handwritten image and converts it into digital data, using, for example, Tesseract OCR. It extracts the outline and shape of each character.

[0291] Output: Digital data of handwritten characters is obtained.

[0292] Step 3:

[0293] Input: Digital data of handwritten characters.

[0294] What it does: The server runs an algorithm to generate a custom font for the user based on the extracted character contours and shapes.

[0295] Output: Font data is generated that reproduces the user's unique handwriting.

[0296] Step 4:

[0297] Input: A text file of user-supplied text.

[0298] What it does: The server uses NLP technology (e.g., "spaCy" or "NLTK") to analyze the text sample, extracting stylistic features, punctuation usage, sentence structure, etc.

[0299] Output: Data that has learned the user's unique writing style characteristics is obtained.

[0300] Step 5:

[0301] Input: Parsed text content data.

[0302] How it works: The emotion recognition engine built into the server analyzes emotional patterns. For example, to identify emotional states such as "positive" or "negative," the "Sentiment Analysis API" is used.

[0303] Output: The emotional information inherent in the text is analyzed, and data is generated to reflect this in the writing style.

[0304] Step 6:

[0305] Input: The user inputs the content of the document they want to generate into the system.

[0306] Specific operation: The user inputs the content of the "New Year's greeting message" through the interface, for example, "Happy New Year. I look forward to working with you again this year."

[0307] Output: The input data is sent to the server.

[0308] Step 7:

[0309] Input: Input content data, learned stylistic features, and sentiment information.

[0310] Specific operation: The server runs a text generation algorithm based on this data. The generated text reflects the user's writing style and emotions and is converted to look handwritten using a handwriting font.

[0311] Output: A handwritten document is generated and output as a preview image.

[0312] Step 8:

[0313] Input: The generated handwritten document.

[0314] Specific operation: The device displays a preview of the generated handwritten-style document to the user. The user can check the preview and edit it as needed, for example, by changing the content or style of the text.

[0315] Output: The corrected document data.

[0316] Step 9:

[0317] Input: The edited document data.

[0318] Specific operation: The server generates the final handwritten-style document in the specified format (e.g., PDF, JPEG), and sends the generated document to the device.

[0319] Output: The final handwritten document file is provided to the user.

[0320] Through these steps, the system can automatically generate high-quality handwritten documents that reflect the user's handwriting, writing style, and emotions.

[0321] (Application example 2)

[0322] 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."

[0323] In modern digital communication, there is a growing demand for personalized messages. However, generating handwritten-style messages is time-consuming and there are limited easy-to-use methods. Furthermore, it is difficult to generate documents that reflect the user's emotions, and they tend to be bland because they rely on a single template. Therefore, there is a need for a system that can easily generate personalized handwritten-style messages and provide high-quality documents that reflect the user's emotions.

[0324] 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 converting handwritten character samples provided by a user into digital data using optical character recognition technology; means for generating a user-specific font from the handwritten character digital data; means for analyzing the user-provided text sample and learning stylistic features using natural language processing technology; means for automatically generating handwritten-style text based on content specified by the user using the generated font and the learned stylistic features; means for analyzing the user's emotions based on the content of the generated text and reflecting them in the text; means for displaying the automatically generated handwritten-style text to the user and making it editable; and means for outputting the edited text in a specific format and transmitting it to a communication terminal. This makes it possible to easily generate personalized handwritten-style messages and provide high-quality documents that reflect the user's emotions.

[0325] A "handwriting sample" is an image or digital data of a user's own handwriting that the user provides to the system.

[0326] "Optical character recognition technology" is a technology that reads character information from an image and converts it into digital data.

[0327] "Digital data" refers to data that has been converted from analog information into digital form.

[0328] A "user-specific font" is a font that is generated to reproduce the characteristics of a user's handwriting.

[0329] "Sample text" is data of text written in the past that the user provides to the system.

[0330] "Stylistic features" are specific styles such as expression patterns, use of punctuation, and sentence structure in writing.

[0331] "Natural language processing technology" is a technology for understanding and analyzing human language.

[0332] "Handwritten text" is text in digital form that reproduces the style of a user's handwriting.

[0333] "Means for analyzing emotions and reflecting them in writing" refers to a method for analyzing the emotional state of a user from the text they have entered or from past writing, and adjusting the expression and writing style based on that.

[0334] The "means for enabling editing" is a function that allows the user to check the generated handwritten-style text and make corrections or changes as necessary.

[0335] "Means for outputting in a specific format and transmitting to a communication terminal" refers to a method for generating the final handwritten-style text in a specified format (such as PDF or JPEG) and transmitting it to the user's device.

[0336] MODE FOR CARRYING OUT THE INVENTION

[0337] The present invention is a system for generating personalized handwritten messages in a specific format and sending them to a user's communication device. The system is designed to utilize handwriting samples and sentence samples to generate a user's personalized font and writing style, and further analyze and reflect sentiment in the sentences. Specific examples are described below.

[0338] Program processing explanation

[0339] Hardware and software used

[0340] Hardware: Servers, user devices (PCs, smartphones, tablets)

[0341] Software: OCR technology (pytesseract), natural language processing technology (NLP tools), emotion engine (emotion_engine)

[0342] 1. Upload your handwritten sample:

[0343] Users upload their own handwritten character samples (images, digital data) and past writing samples (text files) to the system, which converts the samples into digital data using OCR technology (pytesseract), and extracts the outline and shape characteristics of the characters.

[0344] 2. Font generation:

[0345] The server uses the extracted character features to generate a user-specific font that faithfully reproduces the user's handwriting style.

[0346] 3. Analysis of stylistic features:

[0347] The server uses natural language processing technology to analyze user-provided text samples and learn stylistic features, identifying expression patterns and sentence structures within the text and defining the user's unique writing style.

[0348] 4. Emotion analysis:

[0349] The emotion engine analyzes the content of the user's text and recognizes emotions from it, allowing the user's emotional state to be reflected in the text.

[0350] 5. Message Creation:

[0351] The user inputs the message content they want to generate using the system interface. The input data is automatically generated as handwritten text based on the generated font and the learned stylistic features and emotional information.

[0352] 6. Preview and edit:

[0353] The server generates a preview image of the generated handwritten text and sends it to the user's terminal. The user can check the preview and edit it as necessary.

[0354] 7. Final output and transmission:

[0355] Once editing is complete, the server generates the final handwritten-style text in the specified format (PDF, JPEG) and sends it to the user's device, where the user can download it and use it as needed.

[0356] Specific examples

[0357] For example, if a user wants to purchase a special gift from a virtual store and want to include a handwritten message, the user can upload handwriting samples and past writings to the system, which will then generate a personalized message.

[0358] Prompt Sentence Examples

[0359] "Generate personalized handwritten messages using your handwriting and text samples."

[0360] Examples:

[0361] Handwritten character samples: "sample1.png", "sample2.png"

[0362] Text samples: "sample_text1.txt", "sample_text2.txt"

[0363] Message we want to generate: "Thank you for shopping with us! Your support means a lot to us."

[0364] This allows users to easily create personalized handwritten messages and provide a special customer experience in virtual stores.

[0365] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0366] Step 1:

[0367] (Sample upload)

[0368] Users upload sample images of their own handwritten characters and sample text files of past sentences to the system, which receives the images of handwritten characters and the text files of sentences as input and sends them to the server.

[0369] Input: Sample image of handwritten characters, sample text file

[0370] Output: Sample images of handwritten characters transferred to the server, sample text files

[0371] Step 2:

[0372] (OCR processing of handwritten characters)

[0373] The server converts sample images of handwritten characters into digital data using OCR technology (pytesseract), and then processes the images to recognize characters and extract their outlines and shapes.

[0374] Input: Sample image of handwritten characters

[0375] Data processing / data calculation: Character recognition and contour extraction using OCR

[0376] Output: Digital data of handwritten characters

[0377] Step 3:

[0378] (font generation)

[0379] The server generates a user-specific font based on the digital data of handwritten characters obtained through OCR processing. It creates a font file using the character feature information.

[0380] Input: Digital data of handwritten characters

[0381] Data processing / data calculation: character feature modeling and font file generation

[0382] Output: User-specific font file

[0383] Step 4:

[0384] (Analysis of stylistic features)

[0385] The server analyzes the sample text provided by the user using natural language processing technology (NLP tools) to learn stylistic features, extracting sentence structure and expression patterns, and creating a unique stylistic model for the user.

[0386] Input: Sample text file of sentences

[0387] Data processing / data calculation: Extraction and learning of stylistic features using NLP

[0388] Output: User-specific writing style model

[0389] Step 5:

[0390] (emotional analysis)

[0391] The emotion engine recognizes emotions from the content of the text provided by the user and applies algorithms to recommend appropriate writing styles and expressions based on that. It learns emotional patterns and reflects them in the generated text.

[0392] Input: Text content

[0393] Data processing / data calculation: Applying emotion recognition models and learning emotion patterns

[0394] Output: Sentiment analysis results and recommended writing style

[0395] Step 6:

[0396] (Message generation)

[0397] Based on the message content entered by the user into the system interface, the server automatically generates handwritten-style text using the generated font, learned stylistic features, and emotional information. Finally, the handwritten-style text is created using the digital font.

[0398] Input: Message content, user-specific font, writing style model, sentiment analysis results

[0399] Data processing / data calculation: style model, sentence generation based on emotion information, font application

[0400] Output: Handwritten message

[0401] Step 7:

[0402] (Preview and Edit)

[0403] The server generates a preview image of the handwritten text and sends it to the user's terminal. The user can check the preview and edit it as necessary.

[0404] Input: Handwritten message

[0405] Data processing / data calculation: Preview image generation

[0406] Output: Preview image

[0407] Step 8:

[0408] (Final output and transmission)

[0409] After the user has completed editing, the server generates the final handwritten-style text in the specified format (PDF, JPEG) and sends it to the user's device, where the user can download the file and use it.

[0410] Input: Edited handwritten message

[0411] Data processing / data calculation: Generate files in specified format

[0412] Output: Final PDF or JPEG file

[0413] These processing steps allow users to easily create personalized handwritten messages, providing high-quality documents that reflect their emotions.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] [Second embodiment]

[0418] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0419] 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.

[0420] 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).

[0421] 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.

[0422] 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.

[0423] 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).

[0424] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0425] 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.

[0426] 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.

[0427] 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.

[0428] In the smart glasses 214, 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.

[0429] 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."

[0430] The present invention provides a system that automatically generates handwritten-style documents by learning the characteristics of handwritten characters and writing styles provided by a user. This system is configured as follows.

[0431] First, the user provides the system with samples of their handwriting and previous writing, which can be in the form of scanned images, digital photographs of their handwriting, or text files.

[0432] The server converts the handwritten sample provided by the user into digital data using OCR (optical character recognition) technology, which involves detailed analysis of the contours and shape characteristics of each character and then generates a unique font for the user.

[0433] The server then analyzes the user-provided text samples using natural language processing (NLP) techniques, learning the user's writing style, including their grammar, punctuation, and sentence structure.

[0434] The user inputs the content of the document they want to create through the system interface, and the terminal checks the input and sends it to the server.

[0435] The server converts the input content into text in the user's writing style based on the input content and the learned writing style characteristics.The server then converts the generated text into a handwritten-style text using the user's unique font and generates a preview image.

[0436] The device displays a preview of the generated handwritten document to the user, who can check the content and appearance of the document and make edits as needed. Once editing is complete, the server outputs the final handwritten document in the specified format (e.g., PDF, JPEG) and sends it to the device.

[0437] Specific examples

[0438] As a specific example, a scene in which a user creates a New Year's card as a New Year's greeting will be described.

[0439] 1. Providing handwritten data

[0440] The user uploads to the system a sample of a handwritten New Year's card they have created in the past, along with a text file of an apology letter. The handwritten sample includes greetings such as "Happy New Year."

[0441] 2. Recognition of Handwritten Characters

[0442] The server recognizes the uploaded New Year's card samples using OCR technology and extracts the shape patterns of each character in "A Happy New Year" and "Please continue to support me this year" as digital data.

[0443] 3. Font Generation

[0444] The server generates a user-specific font based on the recognized shape patterns of handwritten characters. For example, it saves the unique handwriting styles of characters such as "明" and "年" as fonts.

[0445] 4. Stylistic Learning

[0446] The server analyzes the apology text file using NLP technology and learns expression patterns and sentence structures such as "I'm very sorry for the trouble."

[0447] 5. Input of Generation Requirements

[0448] The user inputs "Please write a New Year greeting" through the interface. Specifically, it requests a sentence such as "A very happy New Year. Please continue to support me this year."

[0449] 6. Automatic Generation of Documents

[0450] The server generates a sentence "A very happy New Year. Please continue to support me this year." based on the input content and the learned stylistic features. Then, it creates a handwritten-style New Year's card using the user-specific font.

[0451] 7. Display and Editing of Results

[0452] The terminal displays a preview of the generated handwritten New Year's card, and the user can confirm it. For example, the user can edit it to say, "Happy New Year! I hope you are healthy this year."

[0453] 8. Document Output

[0454] Once the user is satisfied with the document, the server generates the final handwritten New Year's card in PDF format and sends it to the device, where the user can print it and use it as an actual New Year's card.

[0455] This system allows users to easily generate high-quality handwritten-looking documents that utilize their own handwritten characters and writing style, while eliminating the need for handwriting.

[0456] The processing flow will be explained below.

[0457] Step 1: User provides handwritten data

[0458] Through the system's interface, users upload handwriting samples and past writing samples, which can include scanned images, digital photographs of handwriting, or text files.

[0459] Step 2: The server recognizes the handwritten characters

[0460] The server converts the uploaded handwritten samples into digital data using OCR (optical character recognition) technology, which extracts the outline and shape of each character and generates a digital representation of the character.

[0461] Step 3: The server generates the font

[0462] The server generates a user-specific font from the digital data of the handwritten characters, which involves detailed analysis of the character shape patterns to create a font that reflects the user's unique handwriting and style.

[0463] Step 4: The server learns the writing style

[0464] The server analyzes the sample text provided by the user using NLP (natural language processing) technology, specifically extracting and learning stylistic features such as expression patterns, punctuation, and sentence structure used in the text.

[0465] Step 5: User enters generation request

[0466] The user uses the system's interface to input the content of the document they want to generate, which may include entering a specific phrase or sentence.

[0467] Step 6: The server automatically generates the document

[0468] The server converts the specified content into text in the user's writing style based on the input content and the learned writing style characteristics. For example, it generates a document based on the input content, such as a greeting for a New Year's card or an apology letter.

[0469] Step 7: The server converts it to handwritten style

[0470] The server converts the generated text into a handwritten style using the user's own font, resulting in a visual representation of the text as if it were handwritten.

[0471] Step 8: Your device will display the results

[0472] The device displays a preview of the generated handwritten document to the user, allowing the user to check the content and appearance of the document and make edits as needed.

[0473] Step 9: User edits the document

[0474] The user can edit the generated document through the system interface, for example by correcting parts of the text or inserting additional messages.

[0475] Step 10: The server outputs the final document

[0476] The server generates a document in the specified format (PDF, JPEG, etc.) that has been finalized by the user, and sends the generated document to the terminal so that the user can download or print it.

[0477] Example 1

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

[0479] In recent years, there has been an increasing need for users to efficiently generate documents that look handwritten. However, with conventional technologies, it has been difficult to efficiently convert handwritten characters and writing styles into digital data and automatically generate documents that look handwritten. In particular, generating a user-specific font or learning a writing style from past documents and reflecting it in new documents requires advanced technology, making it difficult to achieve.

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

[0481] In this invention, the server includes means for converting handwritten character samples provided by a user into digital data using optical character recognition technology, means for generating a user-specific font from the digital handwritten character data, and means for analyzing the user-provided text samples and learning stylistic features using natural language processing technology, thereby enabling the user to automatically generate handwritten-look documents that reflect their own handwriting and writing style.

[0482] "Handwriting sample" refers to a user-provided material containing handwritten characters, and refers to digital data such as a scanned image or digital photograph.

[0483] "Optical character recognition technology" is a technology that analyzes the character information contained in images and photographs and converts it into text data.

[0484] "Digital data" refers to information that has been converted into a form that a computer can understand and process.

[0485] A "user-specific font" is a font that reproduces the characteristics of the user's handwritten characters, and refers to font data that includes individual character shapes and handwriting characteristics.

[0486] "Sample writing" refers to digital data of previously written writing provided by the user, which is a text file for learning stylistic features.

[0487] "Natural language processing technology" refers to technology that enables computers to understand, analyze, and generate human language.

[0488] "Interface" refers to the screen and operating environment through which a user interacts with a system and inputs information.

[0489] "Handwritten-style text" refers to a digital document that is generated using a font that reproduces the user's handwriting and that looks handwritten.

[0490] "Preview" refers to a function that displays the shape and content of the final generated document in a state that allows the user to check it.

[0491] "Format" refers to the output format of the digital document, including file formats such as PDF and JPEG.

[0492] The present invention is a system that learns handwritten characters and writing styles provided by a user and automatically generates handwritten-style documents based on them. This system is composed of a server, a terminal, and a user interface, and is implemented using the following hardware and software.

[0493] The server converts handwritten samples provided by the user into digital data using optical character recognition technology (OCR). The specific OCR software used is "Tesseract OCR." This allows for detailed analysis of the contours and shapes of the handwritten characters, and the server uses the results to generate a digital font unique to the user. The font creation tool "FontForge" is used to generate the font.

[0494] The server then analyzes the user-provided text samples using natural language processing (NLP) techniques, such as "spaCy" and "NLTK." Through this analysis, the server learns the user's writing style, including their grammar, punctuation, and sentence structure.

[0495] The user inputs the content of the document they want to generate through the interface. Specifically, the user uses a terminal to input a prompt such as "Please write a New Year's greeting." This input is sent from the terminal to the server. The server generates a sentence based on the input content and pre-trained stylistic features. The generated sentence is then converted to look like handwriting using a user-specific font.

[0496] The terminal displays the generated handwritten document preview to the user. The user can check the preview content on the terminal and edit it as necessary. For example, the sentence "Happy New Year. I hope you will continue to work hard this year." can be changed to "Happy New Year. I hope you will be healthy this year."

[0497] Finally, the server outputs the edited text in the specified format (e.g., PDF, JPEG) and sends it to the terminal, where the user can download the final document, print it, and use it as an actual New Year's card, etc.

[0498] A concrete example of a prompt is as follows:

[0499] "Please upload a handwriting sample."

[0500] "Please upload a sample of your previous writing."

[0501] Please write a New Year's greeting.

[0502] Please review the preview and make any edits you need.

[0503] This system allows users to efficiently generate high-quality handwritten documents that reflect their own handwriting style and idiom.

[0504] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0505] Step 1: User provides handwriting and writing samples

[0506] The user accesses the system interface and uploads an image file (e.g., PNG, JPEG) of a handwriting sample and a text file (e.g., TXT, DOC) of a writing sample. The image file and the text file are provided to the system as input. These files are sent to the server as output, ready for the next analysis step.

[0507] Step 2: The server analyzes the handwriting sample using OCR technology

[0508] The server receives the handwritten sample image uploaded by the user and begins analyzing it using "Tesseract OCR." It receives the image file of the handwritten sample as input and uses OCR technology to analyze the outlines and shapes of the characters. Digital data for each character is extracted as output and stored on the server. The server then uses this data to prepare for font generation.

[0509] Step 3: The server generates a font from the handwritten characters

[0510] The server uses the font creation tool "FontForge" to generate a user-specific font based on the digital data of handwritten characters obtained using OCR technology. Using the digital data of the characters as input, it runs an algorithm to extract the shape patterns of the characters. The output is a user-specific font file (e.g., .ttf or .otf), which is stored on the server.

[0511] Step 4: The server analyzes the text sample using natural language processing

[0512] The server uses "spaCy" or "NLTK" to analyze the text samples uploaded by users. It receives the text sample as input and uses natural language processing technology to analyze the sentence's expression patterns, punctuation usage, sentence structure, etc. The output is stylistic features extracted and stored on the server. The server uses this data to prepare the document to be generated.

[0513] Step 5: The user enters the generation request in the interface.

[0514] The user uses the system's interface to input the content of the document they want to generate. Specifically, they type "Please write a New Year's greeting" and click the send button. This sends the generation request content as input to the server. The server then receives the input and is ready to proceed to the next step.

[0515] Step 6: The server generates the document content and converts it to a handwritten style.

[0516] The server uses natural language processing technology to generate text based on the generation request received from the user and the stylistic features it has previously learned. The generation request and stylistic feature data are used as input. A font specific to the user is used to convert the generated text into a handwritten-style text. The document converted into a handwritten-style text is generated as output as preview image data and saved on the server.

[0517] Step 7: The device will display a preview of the generated document

[0518] The terminal displays a handwritten document preview sent from the server to the user. It receives preview image data from the server as input and displays it on the interface. The user can check the displayed preview and is ready to make corrections if necessary.

[0519] Step 8: User reviews and edits the document

[0520] The user checks the preview document displayed on the terminal and edits the text content as necessary. For example, they edit the text from "Happy New Year. Thank you for your continued support this year." to "Happy New Year. I hope you stay healthy this year." The corrected text content is sent as input to the server. The server generates a new preview image reflecting the edited content as output.

[0521] Step 9: The server outputs the final document and sends it to the device.

[0522] The server receives final confirmation of the edits from the user and generates the final document in the specified format (e.g. PDF, JPEG). It receives the edited text data as input. It saves the final document as output in PDF or JPEG format and sends it to the terminal. The user can download the final document from the terminal and print it to use as an actual New Year's card, etc.

[0523] The above is the specific processing flow of the program.

[0524] (Application example 1)

[0525] 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."

[0526] Previous systems for generating handwritten-style messages not only required a complex and time-consuming process for training handwriting samples and writing styles, but also resulted in messages that were not effectively integrated into product images. Furthermore, there was a lack of an easy way for users to add handwritten-style messages when customizing their orders.

[0527] 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.

[0528] In this invention, the server includes means for converting handwritten sample text provided by a user into digital data using optical character recognition technology, means for generating a user-specific font from the digital data of the handwritten text, means for analyzing the user-provided text sample and learning stylistic features using natural language processing technology, means for automatically generating handwritten-style text based on content specified by the user using the generated font and the learned stylistic features, means for displaying the automatically generated handwritten-style text to the user and making it editable, means for generating a handwritten-style message to accompany a product and combining it with a product image, and means for outputting the edited text in a specified format. This allows users to easily create and edit handwritten-style messages and integrate them with product images when placing orders.

[0529] "Handwritten characters" refer to characters written by a user by hand.

[0530] A "sample" is a portion of handwritten text or a sentence that is provided to the system for learning.

[0531] "Optical character recognition technology" is a technology that converts character information acquired as an image into digital data.

[0532] "Digital data" refers to the digitized form of analog data used to process and store information electronically.

[0533] A "font" is a set of characters created according to specific design rules.

[0534] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[0535] "Stylistic features" refer to the characteristics of individual writing styles, such as sentence expression patterns, use of punctuation, and sentence structure.

[0536] "Handwritten-style text" refers to text that is generated from digital data in a format that looks like handwriting.

[0537] "User" refers to an individual or corporation that uses the system.

[0538] The "means for enabling editing" refers to an interface that allows the user to check the generated handwritten-style text and make changes as necessary.

[0539] The "specified format" refers to the format in which the final handwritten-style text is output, such as PDF or JPEG.

[0540] "Products" means goods and services sold in the Virtual Store.

[0541] "Message" refers to system-generated text that a user can accompany with an item.

[0542] "Product images" refers to visual data that represents a product, including photographs and illustrations.

[0543] "Synthesis" refers to the process of combining multiple pieces of data into one.

[0544] This invention relates to a system that learns a user's handwriting and writing style, generates handwritten-style messages, and combines them with product images. As an embodiment of the invention, a specific description will be given using an application example in which handwritten-style messages are attached to products in a virtual store.

[0545] System Configuration

[0546] Providing a sample of handwriting

[0547] Users upload handwriting samples to the system as image data captured using a smartphone or scanner, which is then sent to a server where it is converted into digital data using optical character recognition (OCR) technology.

[0548] Font Generation

[0549] The server uses OCR technology to generate a unique font for each user from the digital data of handwritten characters. Software such as Tesseract and OpenCV are used to extract the outline and shape characteristics of the characters to create a unique font.

[0550] Studying writing style

[0551] Users provide the system with text files containing examples of their writing. The text is then sent to a server, where natural language processing (NLP) techniques are used to learn stylistic features. This process uses NLP libraries such as NLTK and SpaCy.

[0552] Message Generation

[0553] The user inputs a handwritten-style message to accompany the product order through the system interface. The server converts the input message into a handwritten-style sentence using the user's writing style and a custom font. The generated handwritten-style message is then superimposed onto the product image. The image processing used here includes "PIL" and "OpenCV."

[0554] Viewing and editing messages

[0555] The generated handwritten message is previewed on the user's device, and the user can review the preview and edit the message as needed. The edited content is then resent to the server and converted back into handwritten text.

[0556] Final Output

[0557] The final handwritten message is then generated in a specified format (PDF, JPEG, etc.) and sent to the user's device. The user can then download this data and use it to order products.

[0558] Specific examples of use

[0559] For example, consider a scenario in which a user orders a birthday gift from a virtual store and creates a handwritten message saying "Happy Birthday" to accompany the gift. The user uploads images of handwritten text such as "Thank you" and "Congratulations" to the system as samples, and provides a text file of a blog post they previously wrote as a writing style sample. After that, by entering "Happy Birthday," the server combines the handwritten message with the product image and displays a preview. After the user has confirmed and edited the message, the final message is output in the specified format, completing the ordering process.

[0560] Prompt Sentence Examples

[0561] "I want to create handwritten birthday messages. Develop an application that learns the user's handwriting and writing style to generate 'Happy Birthday' messages."

[0562] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0563] Step 1: Provide a sample of your handwriting

[0564] Users use their smartphones or scanners to capture sample images of handwritten characters and upload them to the system, which then sends the image data to the server.

[0565] Input: Handwritten text image file (JPEG or PNG format)

[0566] Output: Image data stored on the server

[0567] Step 2: Recognizing handwriting

[0568] The server analyzes the received sample image of handwritten characters using OCR technology. Specifically, it reads the image using OpenCV and recognizes the characters using Tesseract.

[0569] Input: Image data of handwritten characters

[0570] Output: Character string converted into digital data by OCR

[0571] Step 3: Font generation

[0572] The server extracts the outline and shape characteristics of the characters recognized by OCR and generates a unique font using an outline extraction algorithm.

[0573] Input: Character string data obtained by OCR

[0574] Output: User-specific font data

[0575] Step 4: Provide a writing sample

[0576] Users upload samples of their writing (text files) to the system, and this data is sent to the server.

[0577] Input: Text file (TXT format)

[0578] Output: Text data stored on the server

[0579] Step 5: Learning stylistic features

[0580] The server analyzes the received text samples using natural language processing technology to learn the user's writing style. This analysis involves analyzing the structure of the sentences using NLTK and SpaCy.

[0581] Input: Text sample data

[0582] Output: Stylistic feature data

[0583] Step 6: Enter a Message Generation Request

[0584] The user inputs the message content they want to create through the system interface, and this content is sent to the server.

[0585] Input: Message content (e.g. "Happy Birthday")

[0586] Output: Message content saved on the server

[0587] Step 7: Create a handwritten message

[0588] The server converts the input message content into a handwritten-like form using the user's writing style and a custom font, and then generates an image of the message using PIL (Python Imaging Library).

[0589] Input: Message content, style feature data, unique font data

[0590] Output: Handwritten message image

[0591] Step 8: Adding a message to product images

[0592] The server synthesizes the generated handwritten message onto the product image using OpenCV and PIL.

[0593] Input: Handwritten message image, product image

[0594] Output: Product image with message

[0595] Step 9: Review and edit your message

[0596] The terminal displays a preview of the product image with the generated handwritten message to the user, who can check it and edit the message as necessary.

[0597] Input: Product image with message

[0598] Output: Message content edited by the user

[0599] Step 10: Final output

[0600] The server outputs the final edited handwritten-style message in a specified format (PDF, JPEG, etc.) and sends the final data to the terminal.

[0601] Input: Edited message content, product image

[0602] Output: Final output data in the specified format

[0603] 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.

[0604] The present invention is a system that automatically generates handwritten-style documents by learning the characteristics of handwritten characters and sentence samples provided by the user, and further combines it with an emotion engine that recognizes the user's emotions. The system consists of the following components:

[0605] First, users upload samples of their handwriting and previous writing to the system, which can include scanned images, digital photographs of their handwriting, text files, etc. The samples are used as the basis for recognizing the shape of the handwriting.

[0606] The server converts the uploaded handwritten text samples into digital data using OCR (optical character recognition) technology, extracting the outline and shape of each character and generating a custom font for the user. This creates the foundation for digitally reproducing handwritten text.

[0607] The server then analyzes the user-provided text samples using natural language processing (NLP) techniques. This analysis extracts stylistic features such as expression patterns, punctuation usage, and sentence structure within the text, and learns the user's unique writing style. This allows the generated documents to faithfully reproduce the user's writing style.

[0608] Furthermore, an emotion engine is built into the server, which analyzes the text entered by the user and recognizes emotions from it. The emotion engine learns the emotional patterns inherent in the user's text and reflects these when generating text. At the same time, it analyzes the user's emotional state in real time and suggests appropriate writing styles and expressions.

[0609] The user can input the content of the document they want to create through the system interface. For example, they can input the content of a New Year's greeting card or an apology letter. The input data is then sent to the server.

[0610] The server converts the specified content into a sentence in the user's writing style based on the input content and the learned stylistic features and emotional information, and then converts it into a handwritten-style sentence using a handwriting font to generate a preview image.

[0611] The terminal displays these handwritten-look documents to the user in a preview format. The user can check the document contents and edit them as necessary. Once editing is complete, the server outputs the final handwritten-look document in the specified format (e.g., PDF, JPEG) and sends it to the terminal.

[0612] Specific examples

[0613] As a specific example, a scene in which a user creates a New Year's card as a New Year's greeting will be described.

[0614] 1. The user uploads a sample handwritten New Year's card they have created in the past and a text file containing the text. The sample New Year's card contains greetings such as "Happy New Year."

[0615] 2. The server uses OCR technology to recognize the New Year's card samples and extracts the character shapes of "Akemashite omedetou gozaimasu" and "Kotoshi mo yoroshiku onegai shimasu" as digital data.

[0616] 3. The server generates a user - unique font based on the shape pattern of handwritten characters. As a result, the unique handwriting of characters such as "mei" and "nian" is saved as a digital font.

[0617] 4. The server analyzes the text file of the article using NLP technology and learns expression patterns such as "Moushiwake arimasen ga, go meiwaku o kakemasu" and the sentence structure.

[0618] 5. The emotion engine analyzes the emotion from the user's input content and past articles and adjusts the appropriate expressions and styles.

[0619] 6. The user inputs a "New Year greeting sentence" through the interface and requests the system to generate a New Year's card. [[ID=2I]]

[0620] 7. The server generates a sentence such as "Akemashite omedetou gozaimasu. Kotoshi mo yoroshiku onegai itashimasu." based on the input content, learned style features, and emotion information. Furthermore, it creates a handwritten - style New Year's card using the user - unique font.

[0621] 8. The terminal previews and displays the generated handwritten - style New Year's card for the user to check the content. For example, it can be edited to "Akemashite omedetou gozaimasu. Kotoshi mo douzo go kenkou de arimasu you ni."

[0622] 9. When the user is satisfied with the document, the server generates the final handwritten - style New Year's card in PDF format and sends it to the terminal. The user can download this and print it for use as an actual New Year's card.

[0623] This system allows users to generate high-quality handwritten-looking documents that utilize their own unique handwriting and writing style, while reducing the effort required for handwriting, and that also appropriately reflect emotions.

[0624] The processing flow will be explained below.

[0625] Step 1: User provides handwritten data

[0626] Through the system's interface, users upload handwriting samples and samples of previous writing, including scanned images, digital photographs, and text files, and the system begins to recognize the user's unique writing and writing characteristics.

[0627] Step 2: The server recognizes the handwritten characters

[0628] The server receives the uploaded handwritten samples and converts them into digital data using OCR (optical character recognition) technology. The server extracts the outline and shape of each character and generates a digital representation of the character, which provides the basis for creating a handwritten font.

[0629] Step 3: The server generates the font

[0630] The server generates a user-specific font based on the digital data of handwritten characters, based on a detailed analysis of the shape patterns of each character. The generated font preserves the user's unique handwriting and style and is used for subsequent text generation.

[0631] Step 4: The server learns the writing style

[0632] The server uses natural language processing (NLP) technology to analyze the sample text provided by the user, extracting stylistic features such as expression patterns, punctuation usage, and sentence structure. This allows the system to learn the user's unique writing style and reflect it in the text it generates.

[0633] Step 5: The server recognizes the emotion

[0634] The server's built-in emotion engine recognizes emotions from user-provided text and real-time input. The server performs sentiment analysis based on keywords and expressions contained in the text and adds this to the user's writing style data.

[0635] Step 6: User enters generation request

[0636] The user uses the system interface to input the content of the document they want to generate, for example, by making a request such as "Please write a New Year's greeting." The input is then sent to the server.

[0637] Step 7: The server automatically generates the document

[0638] The server converts the specified content into a sentence in the user's writing style based on the content entered by the user, the learned stylistic features, and emotional information. The generated sentence is then converted into a handwritten-style sentence using the user's own font.

[0639] Step 8: Your device will display the results

[0640] The device displays a preview of the handwritten document to the user. The user can check the content and appearance of the document and edit it as needed. For example, they can edit the greeting text in a New Year's card or an apology letter.

[0641] Step 9: User edits the document

[0642] The user edits the generated document through the system interface, for example, by changing part of the greeting or inserting an additional message. Once the editing is complete, the user performs a final confirmation.

[0643] Step 10: The server outputs the final document

[0644] The server generates a document in the specified format (PDF, JPEG, etc.) that has been finalized by the user, and the generated document is sent to the terminal, where the user can download or print it.

[0645] Through the above processing steps, users can reduce the effort required for handwriting, automatically generate documents that make use of their own handwritten characters and writing style, and can also take advantage of high-quality handwritten-style documents that appropriately reflect emotions using an emotion engine.

[0646] Example 2

[0647] 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."

[0648] Conventional handwritten-style document generation systems have difficulty fully reflecting the characteristics and style of a user's handwriting, and lack the ability to recognize the emotion of the user's input text and reflect it in the document. This makes it difficult to automatically generate documents personalized for each user. Furthermore, the generated documents cannot be easily previewed or edited, resulting in a problem of low user convenience.

[0649] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for converting handwritten character samples provided by the user into digital data using optical character recognition technology, means for generating a user-specific font from the handwritten character digital data, means for analyzing text samples provided by the user and using natural language processing technology to learn stylistic features, and means for analyzing the content of text entered by the user, recognizing emotions, and reflecting the emotions in document generation. This enables the automatic generation of personalized handwritten-style documents that reflect the user's handwritten character features, style, and emotions.

[0650] A "handwriting sample" is user-provided handwriting data in digital form or as a scanned image.

[0651] "Optical character recognition technology" is a technology for converting characters in an image into digital data.

[0652] "Digital data" is data in a format that can be processed by a computer.

[0653] A "user-specific font" is a special font that is generated based on the characteristics of the user's handwriting.

[0654] "Natural language processing technology" is a technology that analyzes text data and understands and processes stylistic features and content.

[0655] "Style characteristics" are characteristics such as expression patterns, use of punctuation marks, sentence structure, etc., when a particular user writes a sentence.

[0656] "Emotion recognition" is a technology that analyzes and identifies emotions from the content of a user's writing.

[0657] "Handwritten text" is text in digital form that reflects a user's handwriting and writing style.

[0658] "Automatic generation" means that the system creates an artifact through a specific process without human intervention.

[0659] A "preview format" is a temporary or provisional display format used to confirm the final result.

[0660] "Making it editable" means allowing a user to make changes or modifications to the generated document.

[0661] "Specified format" refers to the particular format in which a document or data is saved (e.g., PDF, JPEG).

[0662] This invention is a system that automatically generates handwritten documents by allowing a user to provide handwritten and written samples, and further combines it with an emotion engine that recognizes the user's emotions. The system includes multiple means for digitally reproducing the user's handwriting and writing style and automatically generating documents that reflect the user's emotions.

[0663] Users first upload samples of their handwriting and previous writing to the system, which can include scanned images, digital photographs of their handwriting, text files, etc. These samples are used as the basis for recognizing the shape of the handwriting.

[0664] The server converts the uploaded handwritten sample into digital data using OCR (Optical Character Recognition) technology. Specifically, the OCR technology used is "Tesseract OCR." The outline and shape of each character are extracted and a custom font is generated based on this. This font reflects the user's unique handwriting.

[0665] The server then analyzes the user-provided text samples using NLP (natural language processing) technology, specifically technologies such as "spaCy" and "NLTK." This analysis extracts stylistic features such as expression patterns, punctuation usage, and sentence structure within the text, and learns the user's unique writing style.

[0666] Furthermore, an emotion engine is built into the server, which analyzes the text entered by the user and recognizes emotions from it. For example, the "Sentiment Analysis API" is used. This emotion engine learns the emotional patterns inherent in the user's text and reflects them when generating text. At the same time, it analyzes the user's emotional state in real time and suggests appropriate writing styles and expressions.

[0667] The user inputs the content of the document they want to create through the system interface. For example, they input the content of a New Year's greeting card or an apology letter. The input data is then sent to the server.

[0668] The server converts the specified content into a sentence in the user's writing style based on the input content and the learned stylistic features and emotional information, and then converts it into a handwritten-style sentence using a handwriting font to generate a preview image.

[0669] The terminal displays these handwritten-style documents to the user in a preview format. The user can check the contents of the document and edit them as necessary. For example, the user can edit it to say, "Happy New Year! I hope you stay healthy this year." Once editing is complete, the server outputs the final handwritten-style document in a specified format (e.g., PDF or JPEG) and sends it to the terminal. The user can download it, print it, and use it as an actual New Year's card.

[0670] As a specific example, a scene where a user creates a New Year's greeting card as a New Year's greeting is shown. The user uploads a handwritten New Year's greeting card sample created in the past and a text file of the text of the message to the system. The New Year's greeting card sample contains greeting messages such as "A very happy New Year".

[0671] The server recognizes the New Year's greeting card sample using OCR technology and extracts the character shapes of "A very happy New Year" and "Please take care of me this year" as digital data. Next, a user-specific font is generated based on the shape pattern of the handwritten characters. As a result, the unique handwriting of characters such as "明" and "年" is saved as a digital font.

[0672] Also, the server analyzes the text file of the message using NLP technology and learns expression patterns such as "I'm very sorry for the trouble" and the sentence structure. The emotion engine analyzes the emotion from the user's input content and past messages and adjusts the appropriate expression and style.

[0673] The user inputs a "New Year's greeting message" through the interface and requests the system to generate a New Year's greeting card. The server generates a sentence such as "A very happy New Year. Please take care of me this year." based on the input content, learned style features, and emotion information, and creates a handwritten-style New Year's greeting card using the user-specific font.

[0674] The terminal previews and displays the generated New Year's greeting card, and the user checks the content. If the user is satisfied with the document, the server generates the final New Year's greeting card in PDF format and sends it to the terminal. The user downloads this and prints it to use as a New Year's greeting card.

[0675] With this system, the user can generate a high-quality handwritten-style document that utilizes their own handwritten characters and style, reflects emotions appropriately, while saving the trouble of handwriting.

[0676] The flow of the specific process in Example 2 will be described using FIG. 13.

[0677] Step 1:

[0678] Input: The user uploads handwriting samples (scanned images, digital handwriting photographs) and text samples (text files) to the system.

[0679] Specific operation: The user uploads these samples through the system interface. For example, they provide the system with a scanned image of a handwritten New Year's card that reads "Happy New Year" and a corresponding text file.

[0680] Output: The uploaded sample data is saved on the server.

[0681] Step 2:

[0682] Input: The server converts handwritten samples into digital data using OCR technology.

[0683] How it works: The server analyzes the uploaded handwritten image and converts it into digital data, using, for example, Tesseract OCR. It extracts the outline and shape of each character.

[0684] Output: Digital data of handwritten characters is obtained.

[0685] Step 3:

[0686] Input: Digital data of handwritten characters.

[0687] What it does: The server runs an algorithm to generate a custom font for the user based on the extracted character contours and shapes.

[0688] Output: Font data is generated that reproduces the user's unique handwriting.

[0689] Step 4:

[0690] Input: A text file of user-supplied text.

[0691] What it does: The server uses NLP technology (e.g., "spaCy" or "NLTK") to analyze the text sample, extracting stylistic features, punctuation usage, sentence structure, etc.

[0692] Output: Data that has learned the user's unique writing style characteristics is obtained.

[0693] Step 5:

[0694] Input: Parsed text content data.

[0695] How it works: The emotion recognition engine built into the server analyzes emotional patterns. For example, to identify emotional states such as "positive" or "negative," the "Sentiment Analysis API" is used.

[0696] Output: The emotional information inherent in the text is analyzed, and data is generated to reflect this in the writing style.

[0697] Step 6:

[0698] Input: The user inputs the content of the document they want to generate into the system.

[0699] Specific operation: The user inputs the content of the "New Year's greeting message" through the interface, for example, "Happy New Year. I look forward to working with you again this year."

[0700] Output: The input data is sent to the server.

[0701] Step 7:

[0702] Input: Input content data, learned stylistic features, and sentiment information.

[0703] Specific operation: The server runs a text generation algorithm based on this data. The generated text reflects the user's writing style and emotions and is converted to look handwritten using a handwriting font.

[0704] Output: A handwritten document is generated and output as a preview image.

[0705] Step 8:

[0706] Input: The generated handwritten document.

[0707] Specific operation: The device displays a preview of the generated handwritten-style document to the user. The user can check the preview and edit it as needed, for example, by changing the content or style of the text.

[0708] Output: The corrected document data.

[0709] Step 9:

[0710] Input: The edited document data.

[0711] Specific operation: The server generates the final handwritten-style document in the specified format (e.g., PDF, JPEG), and sends the generated document to the device.

[0712] Output: The final handwritten document file is provided to the user.

[0713] Through these steps, the system can automatically generate high-quality handwritten documents that reflect the user's handwriting, writing style, and emotions.

[0714] (Application example 2)

[0715] 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."

[0716] In modern digital communication, there is a growing demand for personalized messages. However, generating handwritten-style messages is time-consuming and there are limited easy-to-use methods. Furthermore, it is difficult to generate documents that reflect the user's emotions, and they tend to be bland because they rely on a single template. Therefore, there is a need for a system that can easily generate personalized handwritten-style messages and provide high-quality documents that reflect the user's emotions.

[0717] 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 converting handwritten character samples provided by a user into digital data using optical character recognition technology; means for generating a user-specific font from the handwritten character digital data; means for analyzing the user-provided text sample and learning stylistic features using natural language processing technology; means for automatically generating handwritten-style text based on content specified by the user using the generated font and the learned stylistic features; means for analyzing the user's emotions based on the content of the generated text and reflecting them in the text; means for displaying the automatically generated handwritten-style text to the user and making it editable; and means for outputting the edited text in a specific format and transmitting it to a communication terminal. This makes it possible to easily generate personalized handwritten-style messages and provide high-quality documents that reflect the user's emotions.

[0718] A "handwriting sample" is an image or digital data of a user's own handwriting that the user provides to the system.

[0719] "Optical character recognition technology" is a technology that reads character information from an image and converts it into digital data.

[0720] "Digital data" refers to data that has been converted from analog information into digital form.

[0721] A "user-specific font" is a font that is generated to reproduce the characteristics of a user's handwriting.

[0722] "Sample text" is data of text written in the past that the user provides to the system.

[0723] "Stylistic features" are specific styles such as expression patterns, use of punctuation, and sentence structure in writing.

[0724] "Natural language processing technology" is a technology for understanding and analyzing human language.

[0725] "Handwritten text" is text in digital form that reproduces the style of a user's handwriting.

[0726] "Means for analyzing emotions and reflecting them in writing" refers to a method for analyzing the emotional state of a user from the text they have entered or from past writing, and adjusting the expression and writing style based on that.

[0727] The "means for enabling editing" is a function that allows the user to check the generated handwritten-style text and make corrections or changes as necessary.

[0728] "Means for outputting in a specific format and transmitting to a communication terminal" refers to a method for generating the final handwritten-style text in a specified format (such as PDF or JPEG) and transmitting it to the user's device.

[0729] MODE FOR CARRYING OUT THE INVENTION

[0730] The present invention is a system for generating personalized handwritten messages in a specific format and sending them to a user's communication device. The system is designed to utilize handwriting samples and sentence samples to generate a user's personalized font and writing style, and further analyze and reflect sentiment in the sentences. Specific examples are described below.

[0731] Program processing explanation

[0732] Hardware and software used

[0733] Hardware: Servers, user devices (PCs, smartphones, tablets)

[0734] Software: OCR technology (pytesseract), natural language processing technology (NLP tools), emotion engine (emotion_engine)

[0735] 1. Upload your handwritten sample:

[0736] Users upload their own handwritten character samples (images, digital data) and past writing samples (text files) to the system, which converts the samples into digital data using OCR technology (pytesseract), and extracts the outline and shape characteristics of the characters.

[0737] 2. Font generation:

[0738] The server uses the extracted character features to generate a user-specific font that faithfully reproduces the user's handwriting style.

[0739] 3. Analysis of stylistic features:

[0740] The server uses natural language processing technology to analyze user-provided text samples and learn stylistic features, identifying expression patterns and sentence structures within the text and defining the user's unique writing style.

[0741] 4. Emotion analysis:

[0742] The emotion engine analyzes the content of the user's text and recognizes emotions from it, allowing the user's emotional state to be reflected in the text.

[0743] 5. Message Creation:

[0744] The user inputs the message content they want to generate using the system interface. The input data is automatically generated as handwritten text based on the generated font and the learned stylistic features and emotional information.

[0745] 6. Preview and edit:

[0746] The server generates a preview image of the generated handwritten text and sends it to the user's terminal. The user can check the preview and edit it as necessary.

[0747] 7. Final output and transmission:

[0748] Once editing is complete, the server generates the final handwritten-style text in the specified format (PDF, JPEG) and sends it to the user's device, where the user can download it and use it as needed.

[0749] Specific examples

[0750] For example, if a user wants to purchase a special gift from a virtual store and want to include a handwritten message, the user can upload handwriting samples and past writings to the system, which will then generate a personalized message.

[0751] Prompt Sentence Examples

[0752] "Generate personalized handwritten messages using your handwriting and text samples."

[0753] Examples:

[0754] Handwritten character samples: "sample1.png", "sample2.png"

[0755] Text samples: "sample_text1.txt", "sample_text2.txt"

[0756] Message we want to generate: "Thank you for shopping with us! Your support means a lot to us."

[0757] This allows users to easily create personalized handwritten messages and provide a special customer experience in virtual stores.

[0758] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0759] Step 1:

[0760] (Sample upload)

[0761] Users upload sample images of their own handwritten characters and sample text files of past sentences to the system, which receives the images of handwritten characters and the text files of sentences as input and sends them to the server.

[0762] Input: Sample image of handwritten characters, sample text file

[0763] Output: Sample images of handwritten characters transferred to the server, sample text files

[0764] Step 2:

[0765] (OCR processing of handwritten characters)

[0766] The server converts sample images of handwritten characters into digital data using OCR technology (pytesseract), and then processes the images to recognize characters and extract their outlines and shapes.

[0767] Input: Sample image of handwritten characters

[0768] Data processing / data calculation: Character recognition and contour extraction using OCR

[0769] Output: Digital data of handwritten characters

[0770] Step 3:

[0771] (font generation)

[0772] The server generates a user-specific font based on the digital data of handwritten characters obtained through OCR processing. It creates a font file using the character feature information.

[0773] Input: Digital data of handwritten characters

[0774] Data processing / data calculation: character feature modeling and font file generation

[0775] Output: User-specific font file

[0776] Step 4:

[0777] (Analysis of stylistic features)

[0778] The server analyzes the sample text provided by the user using natural language processing technology (NLP tools) to learn stylistic features, extracting sentence structure and expression patterns, and creating a unique stylistic model for the user.

[0779] Input: Sample text file of sentences

[0780] Data processing / data calculation: Extraction and learning of stylistic features using NLP

[0781] Output: User-specific writing style model

[0782] Step 5:

[0783] (emotional analysis)

[0784] The emotion engine recognizes emotions from the content of the text provided by the user and applies algorithms to recommend appropriate writing styles and expressions based on that. It learns emotional patterns and reflects them in the generated text.

[0785] Input: Text content

[0786] Data processing / data calculation: Applying emotion recognition models and learning emotion patterns

[0787] Output: Sentiment analysis results and recommended writing style

[0788] Step 6:

[0789] (Message generation)

[0790] Based on the message content entered by the user into the system interface, the server automatically generates handwritten-style text using the generated font, learned stylistic features, and emotional information. Finally, the handwritten-style text is created using the digital font.

[0791] Input: Message content, user-specific font, writing style model, sentiment analysis results

[0792] Data processing / data calculation: style model, sentence generation based on emotion information, font application

[0793] Output: Handwritten message

[0794] Step 7:

[0795] (Preview and Edit)

[0796] The server generates a preview image of the handwritten text and sends it to the user's terminal. The user can check the preview and edit it as necessary.

[0797] Input: Handwritten message

[0798] Data processing / data calculation: Preview image generation

[0799] Output: Preview image

[0800] Step 8:

[0801] (Final output and transmission)

[0802] After the user has completed editing, the server generates the final handwritten-style text in the specified format (PDF, JPEG) and sends it to the user's device, where the user can download the file and use it.

[0803] Input: Edited handwritten message

[0804] Data processing / data calculation: Generate files in specified format

[0805] Output: Final PDF or JPEG file

[0806] These processing steps allow users to easily create personalized handwritten messages, providing high-quality documents that reflect their emotions.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] [Third embodiment]

[0811] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0812] 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.

[0813] 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).

[0814] 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.

[0815] 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.

[0816] 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).

[0817] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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."

[0823] The present invention provides a system that automatically generates handwritten-style documents by learning the characteristics of handwritten characters and writing styles provided by a user. This system is configured as follows.

[0824] First, the user provides the system with samples of their handwriting and previous writing, which can be in the form of scanned images, digital photographs of their handwriting, or text files.

[0825] The server converts the handwritten sample provided by the user into digital data using OCR (optical character recognition) technology, which involves detailed analysis of the contours and shape characteristics of each character and then generates a unique font for the user.

[0826] The server then analyzes the user-provided text samples using natural language processing (NLP) techniques, learning the user's writing style, including their grammar, punctuation, and sentence structure.

[0827] The user inputs the content of the document they want to create through the system interface, and the terminal checks the input and sends it to the server.

[0828] The server converts the input content into text in the user's writing style based on the input content and the learned writing style characteristics.The server then converts the generated text into a handwritten-style text using the user's unique font and generates a preview image.

[0829] The device displays a preview of the generated handwritten document to the user, who can check the content and appearance of the document and make edits as needed. Once editing is complete, the server outputs the final handwritten document in the specified format (e.g., PDF, JPEG) and sends it to the device.

[0830] Specific examples

[0831] As a specific example, a scene in which a user creates a New Year's card as a New Year's greeting will be described.

[0832] 1. Providing handwritten data

[0833] The user uploads to the system a sample of a handwritten New Year's card they have created in the past, along with a text file of an apology letter. The handwritten sample includes greetings such as "Happy New Year."

[0834] 2. Recognition of Handwritten Characters

[0835] The server recognizes the uploaded New Year's card samples using OCR technology and extracts the shape patterns of each character in "A very happy new year" and "Please continue to support me this year" as digital data.

[0836] 3. Font Generation

[0837] The server generates a user-specific font based on the recognized shape patterns of handwritten characters. For example, it saves the unique handwriting styles of characters such as "明" and "年" as fonts.

[0838] 4. Learning of Writing Styles

[0839] The server analyzes the text file of the apology letter using NLP technology and learns expression patterns and sentence structures such as "I'm very sorry for the trouble."

[0840] 5. Input of Generation Requirements

[0841] The user inputs "Please write a New Year's greeting" through the interface. Specifically, a sentence such as "A very happy new year. Please continue to support me this year." is requested.

[0842] 6. Automatic Generation of Documents

[0843] The server generates a sentence "A very happy new year. Please continue to support me this year." based on the input content and the learned writing style features. Then, it creates a handwritten-style New Year's card using the user-specific font.

[0844] 7. Display and Editing of Results

[0845] The terminal displays a preview of the generated handwritten New Year's card, and the user can confirm it. For example, the user can edit it to say, "Happy New Year! I hope you are healthy this year."

[0846] 8. Document Output

[0847] Once the user is satisfied with the document, the server generates the final handwritten New Year's card in PDF format and sends it to the device, where the user can print it and use it as an actual New Year's card.

[0848] This system allows users to easily generate high-quality handwritten-looking documents that utilize their own handwritten characters and writing style, while eliminating the need for handwriting.

[0849] The processing flow will be explained below.

[0850] Step 1: User provides handwritten data

[0851] Through the system's interface, users upload handwriting samples and past writing samples, which can include scanned images, digital photographs of handwriting, or text files.

[0852] Step 2: The server recognizes the handwritten characters

[0853] The server converts the uploaded handwritten samples into digital data using OCR (optical character recognition) technology, which extracts the outline and shape of each character and generates a digital representation of the character.

[0854] Step 3: The server generates the font

[0855] The server generates a user-specific font from the digital data of the handwritten characters, which involves detailed analysis of the character shape patterns to create a font that reflects the user's unique handwriting and style.

[0856] Step 4: The server learns the writing style

[0857] The server analyzes the sample text provided by the user using NLP (natural language processing) technology, specifically extracting and learning stylistic features such as expression patterns, punctuation, and sentence structure used in the text.

[0858] Step 5: User enters generation request

[0859] The user uses the system's interface to input the content of the document they want to generate, which may include entering a specific phrase or sentence.

[0860] Step 6: The server automatically generates the document

[0861] The server converts the specified content into text in the user's writing style based on the input content and the learned writing style characteristics. For example, it generates a document based on the input content, such as a greeting for a New Year's card or an apology letter.

[0862] Step 7: The server converts it to handwritten style

[0863] The server converts the generated text into a handwritten style using the user's own font, resulting in a visual representation of the text as if it were handwritten.

[0864] Step 8: Your device will display the results

[0865] The device displays a preview of the generated handwritten document to the user, allowing the user to check the content and appearance of the document and make edits as needed.

[0866] Step 9: User edits the document

[0867] The user can edit the generated document through the system interface, for example by correcting parts of the text or inserting additional messages.

[0868] Step 10: The server outputs the final document

[0869] The server generates a document in the specified format (PDF, JPEG, etc.) that has been finalized by the user, and sends the generated document to the terminal so that the user can download or print it.

[0870] Example 1

[0871] 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."

[0872] In recent years, there has been an increasing need for users to efficiently generate documents that look handwritten. However, with conventional technologies, it has been difficult to efficiently convert handwritten characters and writing styles into digital data and automatically generate documents that look handwritten. In particular, generating a user-specific font or learning a writing style from past documents and reflecting it in new documents requires advanced technology, making it difficult to achieve.

[0873] 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.

[0874] In this invention, the server includes means for converting handwritten character samples provided by a user into digital data using optical character recognition technology, means for generating a user-specific font from the digital handwritten character data, and means for analyzing the user-provided text samples and learning stylistic features using natural language processing technology, thereby enabling the user to automatically generate handwritten-look documents that reflect their own handwriting and writing style.

[0875] "Handwriting sample" refers to a user-provided material containing handwritten characters, and refers to digital data such as a scanned image or digital photograph.

[0876] "Optical character recognition technology" is a technology that analyzes the character information contained in images and photographs and converts it into text data.

[0877] "Digital data" refers to information that has been converted into a form that a computer can understand and process.

[0878] A "user-specific font" is a font that reproduces the characteristics of the user's handwritten characters, and refers to font data that includes individual character shapes and handwriting characteristics.

[0879] "Sample writing" refers to digital data of previously written writing provided by the user, which is a text file for learning stylistic features.

[0880] "Natural language processing technology" refers to technology that enables computers to understand, analyze, and generate human language.

[0881] "Interface" refers to the screen and operating environment through which a user interacts with a system and inputs information.

[0882] "Handwritten-style text" refers to a digital document that is generated using a font that reproduces the user's handwriting and that looks handwritten.

[0883] "Preview" refers to a function that displays the shape and content of the final generated document in a state that allows the user to check it.

[0884] "Format" refers to the output format of the digital document, including file formats such as PDF and JPEG.

[0885] The present invention is a system that learns handwritten characters and writing styles provided by a user and automatically generates handwritten-style documents based on them. This system is composed of a server, a terminal, and a user interface, and is implemented using the following hardware and software.

[0886] The server converts handwritten samples provided by the user into digital data using optical character recognition technology (OCR). The specific OCR software used is "Tesseract OCR." This allows for detailed analysis of the contours and shapes of the handwritten characters, and the server uses the results to generate a digital font unique to the user. The font creation tool "FontForge" is used to generate the font.

[0887] The server then analyzes the user-provided text samples using natural language processing (NLP) techniques, such as "spaCy" and "NLTK." Through this analysis, the server learns the user's writing style, including their grammar, punctuation, and sentence structure.

[0888] The user inputs the content of the document they want to generate through the interface. Specifically, the user uses a terminal to input a prompt such as "Please write a New Year's greeting." This input is sent from the terminal to the server. The server generates a sentence based on the input content and pre-trained stylistic features. The generated sentence is then converted to look like handwriting using a user-specific font.

[0889] The terminal displays the generated handwritten document preview to the user. The user can check the preview content on the terminal and edit it as necessary. For example, the sentence "Happy New Year. I hope you will continue to work hard this year." can be changed to "Happy New Year. I hope you will be healthy this year."

[0890] Finally, the server outputs the edited text in the specified format (e.g., PDF, JPEG) and sends it to the terminal, where the user can download the final document, print it, and use it as an actual New Year's card, etc.

[0891] A concrete example of a prompt is as follows:

[0892] "Please upload a handwriting sample."

[0893] "Please upload a sample of your previous writing."

[0894] Please write a New Year's greeting.

[0895] Please review the preview and make any edits you need.

[0896] This system allows users to efficiently generate high-quality handwritten documents that reflect their own handwriting style and idiom.

[0897] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0898] Step 1: User provides handwriting and writing samples

[0899] The user accesses the system interface and uploads an image file (e.g., PNG, JPEG) of a handwriting sample and a text file (e.g., TXT, DOC) of a writing sample. The image file and the text file are provided to the system as input. These files are sent to the server as output, ready for the next analysis step.

[0900] Step 2: The server analyzes the handwriting sample using OCR technology

[0901] The server receives the handwritten sample image uploaded by the user and begins analyzing it using "Tesseract OCR." It receives the image file of the handwritten sample as input and uses OCR technology to analyze the outlines and shapes of the characters. Digital data for each character is extracted as output and stored on the server. The server then uses this data to prepare for font generation.

[0902] Step 3: The server generates a font from the handwritten characters

[0903] The server uses the font creation tool "FontForge" to generate a user-specific font based on the digital data of handwritten characters obtained using OCR technology. Using the digital data of the characters as input, it runs an algorithm to extract the shape patterns of the characters. The output is a user-specific font file (e.g., .ttf or .otf), which is stored on the server.

[0904] Step 4: The server analyzes the text sample using natural language processing

[0905] The server uses "spaCy" or "NLTK" to analyze the text samples uploaded by users. It receives the text sample as input and uses natural language processing technology to analyze the sentence's expression patterns, punctuation usage, sentence structure, etc. The output is stylistic features extracted and stored on the server. The server uses this data to prepare the document to be generated.

[0906] Step 5: The user enters the generation request in the interface.

[0907] The user uses the system's interface to input the content of the document they want to generate. Specifically, they type "Please write a New Year's greeting" and click the send button. This sends the generation request content as input to the server. The server then receives the input and is ready to proceed to the next step.

[0908] Step 6: The server generates the document content and converts it to a handwritten style.

[0909] The server uses natural language processing technology to generate text based on the generation request received from the user and the stylistic features it has previously learned. The generation request and stylistic feature data are used as input. A font specific to the user is used to convert the generated text into a handwritten-style text. The document converted into a handwritten-style text is generated as output as preview image data and saved on the server.

[0910] Step 7: The device will display a preview of the generated document

[0911] The terminal displays a handwritten document preview sent from the server to the user. It receives preview image data from the server as input and displays it on the interface. The user can check the displayed preview and is ready to make corrections if necessary.

[0912] Step 8: User reviews and edits the document

[0913] The user checks the preview document displayed on the terminal and edits the text content as necessary. For example, they edit the text from "Happy New Year. Thank you for your continued support this year." to "Happy New Year. I hope you stay healthy this year." The corrected text content is sent as input to the server. The server generates a new preview image reflecting the edited content as output.

[0914] Step 9: The server outputs the final document and sends it to the device.

[0915] The server receives final confirmation of the edits from the user and generates the final document in the specified format (e.g. PDF, JPEG). It receives the edited text data as input. It saves the final document as output in PDF or JPEG format and sends it to the terminal. The user can download the final document from the terminal and print it to use as an actual New Year's card, etc.

[0916] The above is the specific processing flow of the program.

[0917] (Application example 1)

[0918] 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."

[0919] Previous systems for generating handwritten-style messages not only required a complex and time-consuming process for training handwriting samples and writing styles, but also resulted in messages that were not effectively integrated into product images. Furthermore, there was a lack of an easy way for users to add handwritten-style messages when customizing their orders.

[0920] 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.

[0921] In this invention, the server includes means for converting handwritten sample text provided by a user into digital data using optical character recognition technology, means for generating a user-specific font from the digital data of the handwritten text, means for analyzing the user-provided text sample and learning stylistic features using natural language processing technology, means for automatically generating handwritten-style text based on content specified by the user using the generated font and the learned stylistic features, means for displaying the automatically generated handwritten-style text to the user and making it editable, means for generating a handwritten-style message to accompany a product and combining it with a product image, and means for outputting the edited text in a specified format. This allows users to easily create and edit handwritten-style messages and integrate them with product images when placing orders.

[0922] "Handwritten characters" refer to characters written by a user by hand.

[0923] A "sample" is a portion of handwritten text or a sentence that is provided to the system for learning.

[0924] "Optical character recognition technology" is a technology that converts character information acquired as an image into digital data.

[0925] "Digital data" refers to the digitized form of analog data used to process and store information electronically.

[0926] A "font" is a set of characters created according to specific design rules.

[0927] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[0928] "Stylistic features" refer to the characteristics of individual writing styles, such as sentence expression patterns, use of punctuation, and sentence structure.

[0929] "Handwritten-style text" refers to text that is generated from digital data in a format that looks like handwriting.

[0930] "User" refers to an individual or corporation that uses the system.

[0931] The "means for enabling editing" refers to an interface that allows the user to check the generated handwritten-style text and make changes as necessary.

[0932] The "specified format" refers to the format in which the final handwritten-style text is output, such as PDF or JPEG.

[0933] "Products" means goods and services sold in the Virtual Store.

[0934] "Message" refers to system-generated text that a user can accompany with an item.

[0935] "Product images" refers to visual data that represents a product, including photographs and illustrations.

[0936] "Synthesis" refers to the process of combining multiple pieces of data into one.

[0937] This invention relates to a system that learns a user's handwriting and writing style, generates handwritten-style messages, and combines them with product images. As an embodiment of the invention, a specific description will be given using an application example in which handwritten-style messages are attached to products in a virtual store.

[0938] System Configuration

[0939] Providing a sample of handwriting

[0940] Users upload handwriting samples to the system as image data captured using a smartphone or scanner, which is then sent to a server where it is converted into digital data using optical character recognition (OCR) technology.

[0941] Font Generation

[0942] The server uses OCR technology to generate a unique font for each user from the digital data of handwritten characters. Software such as Tesseract and OpenCV are used to extract the outline and shape characteristics of the characters to create a unique font.

[0943] Studying writing style

[0944] Users provide the system with text files containing examples of their writing. The text is then sent to a server, where natural language processing (NLP) techniques are used to learn stylistic features. This process uses NLP libraries such as NLTK and SpaCy.

[0945] Message Generation

[0946] The user inputs a handwritten-style message to accompany the product order through the system interface. The server converts the input message into a handwritten-style sentence using the user's writing style and a custom font. The generated handwritten-style message is then superimposed onto the product image. The image processing used here includes "PIL" and "OpenCV."

[0947] Viewing and editing messages

[0948] The generated handwritten message is previewed on the user's device, and the user can review the preview and edit the message as needed. The edited content is then resent to the server and converted back into handwritten text.

[0949] Final Output

[0950] The final handwritten message is then generated in a specified format (PDF, JPEG, etc.) and sent to the user's device. The user can then download this data and use it to order products.

[0951] Specific examples of use

[0952] For example, consider a scenario in which a user orders a birthday gift from a virtual store and creates a handwritten message saying "Happy Birthday" to accompany the gift. The user uploads images of handwritten text such as "Thank you" and "Congratulations" to the system as samples, and provides a text file of a blog post they previously wrote as a writing style sample. After that, by entering "Happy Birthday," the server combines the handwritten message with the product image and displays a preview. After the user has confirmed and edited the message, the final message is output in the specified format, completing the ordering process.

[0953] Prompt Sentence Examples

[0954] "I want to create handwritten birthday messages. Develop an application that learns the user's handwriting and writing style to generate 'Happy Birthday' messages."

[0955] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0956] Step 1: Provide a sample of your handwriting

[0957] Users use their smartphones or scanners to capture sample images of handwritten characters and upload them to the system, which then sends the image data to the server.

[0958] Input: Handwritten text image file (JPEG or PNG format)

[0959] Output: Image data stored on the server

[0960] Step 2: Recognizing handwriting

[0961] The server analyzes the received sample image of handwritten characters using OCR technology. Specifically, it reads the image using OpenCV and recognizes the characters using Tesseract.

[0962] Input: Image data of handwritten characters

[0963] Output: Character string converted into digital data by OCR

[0964] Step 3: Font generation

[0965] The server extracts the outline and shape characteristics of the characters recognized by OCR and generates a unique font using an outline extraction algorithm.

[0966] Input: Character string data obtained by OCR

[0967] Output: User-specific font data

[0968] Step 4: Provide a writing sample

[0969] Users upload samples of their writing (text files) to the system, and this data is sent to the server.

[0970] Input: Text file (TXT format)

[0971] Output: Text data stored on the server

[0972] Step 5: Learning stylistic features

[0973] The server analyzes the received text samples using natural language processing technology to learn the user's writing style. This analysis involves analyzing the structure of the sentences using NLTK and SpaCy.

[0974] Input: Text sample data

[0975] Output: Stylistic feature data

[0976] Step 6: Enter a Message Generation Request

[0977] The user inputs the message content they want to create through the system interface, and this content is sent to the server.

[0978] Input: Message content (e.g. "Happy Birthday")

[0979] Output: Message content saved on the server

[0980] Step 7: Create a handwritten message

[0981] The server converts the input message content into a handwritten-like form using the user's writing style and a custom font, and then generates an image of the message using PIL (Python Imaging Library).

[0982] Input: Message content, style feature data, unique font data

[0983] Output: Handwritten message image

[0984] Step 8: Adding a message to product images

[0985] The server synthesizes the generated handwritten message onto the product image using OpenCV and PIL.

[0986] Input: Handwritten message image, product image

[0987] Output: Product image with message

[0988] Step 9: Review and edit your message

[0989] The terminal displays a preview of the product image with the generated handwritten message to the user, who can check it and edit the message as necessary.

[0990] Input: Product image with message

[0991] Output: Message content edited by the user

[0992] Step 10: Final output

[0993] The server outputs the final edited handwritten-style message in a specified format (PDF, JPEG, etc.) and sends the final data to the terminal.

[0994] Input: Edited message content, product image

[0995] Output: Final output data in the specified format

[0996] 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.

[0997] The present invention is a system that automatically generates handwritten-style documents by learning the characteristics of handwritten characters and sentence samples provided by the user, and further combines it with an emotion engine that recognizes the user's emotions. The system consists of the following components:

[0998] First, users upload samples of their handwriting and previous writing to the system, which can include scanned images, digital photographs of their handwriting, text files, etc. The samples are used as the basis for recognizing the shape of the handwriting.

[0999] The server converts the uploaded handwritten text samples into digital data using OCR (optical character recognition) technology, extracting the outline and shape of each character and generating a custom font for the user. This creates the foundation for digitally reproducing handwritten text.

[1000] The server then analyzes the user-provided text samples using natural language processing (NLP) techniques. This analysis extracts stylistic features such as expression patterns, punctuation usage, and sentence structure within the text, and learns the user's unique writing style. This allows the generated documents to faithfully reproduce the user's writing style.

[1001] Furthermore, an emotion engine is built into the server, which analyzes the text entered by the user and recognizes emotions from it. The emotion engine learns the emotional patterns inherent in the user's text and reflects these when generating text. At the same time, it analyzes the user's emotional state in real time and suggests appropriate writing styles and expressions.

[1002] The user can input the content of the document they want to create through the system interface. For example, they can input the content of a New Year's greeting card or an apology letter. The input data is then sent to the server.

[1003] The server converts the specified content into a sentence in the user's writing style based on the input content and the learned stylistic features and emotional information, and then converts it into a handwritten-style sentence using a handwriting font to generate a preview image.

[1004] The terminal displays these handwritten-look documents to the user in a preview format. The user can check the document contents and edit them as necessary. Once editing is complete, the server outputs the final handwritten-look document in the specified format (e.g., PDF, JPEG) and sends it to the terminal.

[1005] Specific examples

[1006] As a specific example, a scene in which a user creates a New Year's card as a New Year's greeting will be described.

[1007] 1. The user uploads a sample handwritten New Year's card they have created in the past and a text file containing the text. The sample New Year's card contains greetings such as "Happy New Year."

[1008] 2. The server uses OCR technology to recognize the New Year's card samples and extracts the character shapes of "Akemashite omedetou gozaimasu" and "Kotoshi mo yoroshiku onegai shimasu" as digital data.

[1009] 3. The server generates a user - unique font based on the shape pattern of handwritten characters. As a result, the unique handwriting of characters such as "mei" and "nen" is saved as a digital font.

[1010] 4. The server analyzes the text file of the article using NLP technology and learns expression patterns such as "Moushiwake arimasen ga, go meiwaku o kakemashite" and the sentence structure.

[1011] 5. The emotion engine analyzes the emotion from the user's input content and past articles and adjusts the appropriate expressions and styles.

[1012] 6. The user inputs a "New Year's greeting sentence" through the interface and requests the system to generate a New Year's card.

[1013] 7. Based on the input content, learned style features, and emotion information, the server generates a sentence such as "Akemashite omedetou gozaimasu. Kotoshi mo yoroshiku onegai itashimasu." Furthermore, it creates a handwritten - style New Year's card using the user - unique font.

[1014] 8. The terminal previews and displays the generated handwritten - style New Year's card for the user to confirm the content. For example, it can be edited to "Akemashite omedetou gozaimasu. Kotoshi mo douzo go kenkou de arimasu you ni."

[1015] 9. When the user is satisfied with the document, the server generates the final handwritten - style New Year's card in PDF format and sends it to the terminal. The user can download this and print it for use as an actual New Year's card.

[1016] This system allows users to generate high-quality handwritten-looking documents that utilize their own unique handwriting and writing style, while reducing the effort required for handwriting, and that also appropriately reflect emotions.

[1017] The processing flow will be explained below.

[1018] Step 1: User provides handwritten data

[1019] Through the system's interface, users upload handwriting samples and samples of previous writing, including scanned images, digital photographs, and text files, and the system begins to recognize the user's unique writing and writing characteristics.

[1020] Step 2: The server recognizes the handwritten characters

[1021] The server receives the uploaded handwritten samples and converts them into digital data using OCR (optical character recognition) technology. The server extracts the outline and shape of each character and generates a digital representation of the character, which provides the basis for creating a handwritten font.

[1022] Step 3: The server generates the font

[1023] The server generates a user-specific font based on the digital data of handwritten characters, based on a detailed analysis of the shape patterns of each character. The generated font preserves the user's unique handwriting and style and is used for subsequent text generation.

[1024] Step 4: The server learns the writing style

[1025] The server uses natural language processing (NLP) technology to analyze the sample text provided by the user, extracting stylistic features such as expression patterns, punctuation usage, and sentence structure. This allows the system to learn the user's unique writing style and reflect it in the text it generates.

[1026] Step 5: The server recognizes the emotion

[1027] The server's built-in emotion engine recognizes emotions from user-provided text and real-time input. The server performs sentiment analysis based on keywords and expressions contained in the text and adds this to the user's writing style data.

[1028] Step 6: User enters generation request

[1029] The user uses the system interface to input the content of the document they want to generate, for example, by making a request such as "Please write a New Year's greeting." The input is then sent to the server.

[1030] Step 7: The server automatically generates the document

[1031] The server converts the specified content into a sentence in the user's writing style based on the content entered by the user, the learned stylistic features, and emotional information. The generated sentence is then converted into a handwritten-style sentence using the user's own font.

[1032] Step 8: Your device will display the results

[1033] The device displays a preview of the handwritten document to the user. The user can check the content and appearance of the document and edit it as needed. For example, they can edit the greeting text in a New Year's card or an apology letter.

[1034] Step 9: User edits the document

[1035] The user edits the generated document through the system interface, for example, by changing part of the greeting or inserting an additional message. Once the editing is complete, the user performs a final confirmation.

[1036] Step 10: The server outputs the final document

[1037] The server generates a document in the specified format (PDF, JPEG, etc.) that has been finalized by the user, and the generated document is sent to the terminal, where the user can download or print it.

[1038] Through the above processing steps, users can reduce the effort required for handwriting, automatically generate documents that make use of their own handwritten characters and writing style, and can also take advantage of high-quality handwritten-style documents that appropriately reflect emotions using an emotion engine.

[1039] Example 2

[1040] 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."

[1041] Conventional handwritten-style document generation systems have difficulty fully reflecting the characteristics and style of a user's handwriting, and lack the ability to recognize the emotion of the user's input text and reflect it in the document. This makes it difficult to automatically generate documents personalized for each user. Furthermore, the generated documents cannot be easily previewed or edited, resulting in a problem of low user convenience.

[1042] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for converting handwritten character samples provided by the user into digital data using optical character recognition technology, means for generating a user-specific font from the handwritten character digital data, means for analyzing text samples provided by the user and using natural language processing technology to learn stylistic features, and means for analyzing the content of text entered by the user, recognizing emotions, and reflecting the emotions in document generation. This enables the automatic generation of personalized handwritten-style documents that reflect the user's handwritten character features, style, and emotions.

[1043] A "handwriting sample" is user-provided handwriting data in digital form or as a scanned image.

[1044] "Optical character recognition technology" is a technology for converting characters in an image into digital data.

[1045] "Digital data" is data in a format that can be processed by a computer.

[1046] A "user-specific font" is a special font that is generated based on the characteristics of the user's handwriting.

[1047] "Natural language processing technology" is a technology that analyzes text data and understands and processes stylistic features and content.

[1048] "Style characteristics" are characteristics such as expression patterns, use of punctuation marks, sentence structure, etc., when a particular user writes a sentence.

[1049] "Emotion recognition" is a technology that analyzes and identifies emotions from the content of a user's writing.

[1050] "Handwritten text" is text in digital form that reflects a user's handwriting and writing style.

[1051] "Automatic generation" means that the system creates an artifact through a specific process without human intervention.

[1052] A "preview format" is a temporary or provisional display format used to confirm the final result.

[1053] "Making it editable" means allowing a user to make changes or modifications to the generated document.

[1054] "Specified format" refers to the particular format in which a document or data is saved (e.g., PDF, JPEG).

[1055] This invention is a system that automatically generates handwritten documents by allowing a user to provide handwritten and written samples, and further combines it with an emotion engine that recognizes the user's emotions. The system includes multiple means for digitally reproducing the user's handwriting and writing style and automatically generating documents that reflect the user's emotions.

[1056] Users first upload samples of their handwriting and previous writing to the system, which can include scanned images, digital photographs of their handwriting, text files, etc. These samples are used as the basis for recognizing the shape of the handwriting.

[1057] The server converts the uploaded handwritten sample into digital data using OCR (Optical Character Recognition) technology. Specifically, the OCR technology used is "Tesseract OCR." The outline and shape of each character are extracted and a custom font is generated based on this. This font reflects the user's unique handwriting.

[1058] The server then analyzes the user-provided text samples using NLP (natural language processing) technology, specifically technologies such as "spaCy" and "NLTK." This analysis extracts stylistic features such as expression patterns, punctuation usage, and sentence structure within the text, and learns the user's unique writing style.

[1059] Furthermore, an emotion engine is built into the server, which analyzes the text entered by the user and recognizes emotions from it. For example, the "Sentiment Analysis API" is used. This emotion engine learns the emotional patterns inherent in the user's text and reflects them when generating text. At the same time, it analyzes the user's emotional state in real time and suggests appropriate writing styles and expressions.

[1060] The user inputs the content of the document they want to create through the system interface. For example, they input the content of a New Year's greeting card or an apology letter. The input data is then sent to the server.

[1061] The server converts the specified content into a sentence in the user's writing style based on the input content and the learned stylistic features and emotional information, and then converts it into a handwritten-style sentence using a handwriting font to generate a preview image.

[1062] The terminal displays these handwritten-style documents to the user in a preview format. The user can check the contents of the document and edit them as necessary. For example, the user can edit it to say, "Happy New Year! I hope you stay healthy this year." Once editing is complete, the server outputs the final handwritten-style document in a specified format (e.g., PDF or JPEG) and sends it to the terminal. The user can download it, print it, and use it as an actual New Year's card.

[1063] As a specific example, a scene where a user creates a New Year's greeting card as a New Year's greeting is shown. The user uploads a handwritten New Year's greeting card sample created in the past and a text file of the text of the message to the system. The New Year's greeting card sample contains greeting messages such as "A very happy new year".

[1064] The server recognizes the New Year's greeting card sample using OCR technology and extracts the character shapes of "A very happy new year" and "Please take care of me this year" as digital data. Next, a user-specific font is generated based on the shape pattern of the handwritten characters. As a result, the unique handwriting of characters such as "明" and "年" is saved as a digital font.

[1065] In addition, the server analyzes the text file of the message using NLP technology and learns expression patterns such as "I'm sorry for the trouble" and the sentence structure. The emotion engine analyzes the emotion from the user's input content and past messages and adjusts the appropriate expression and style.

[1066] The user inputs a "New Year's greeting message" through the interface and requests the system to generate a New Year's greeting card. The server generates a sentence such as "A very happy new year. Please take care of me this year." based on the input content, learned style characteristics, and emotion information, and creates a handwritten-style New Year's greeting card using the user-specific font.

[1067] The terminal previews and displays the generated New Year's greeting card, and the user checks the content. If the user is satisfied with the document, the server generates the final New Year's greeting card in PDF format and sends it to the terminal. The user downloads this and prints it for use as a New Year's greeting card.

[1068] With this system, the user can generate a high-quality handwritten-style document that utilizes their own handwritten characters and style, while also appropriately reflecting emotion, without the hassle of handwriting.

[1069] The flow of the specific process in Example 2 will be described using FIGURE 13.

[1070] Step 1:

[1071] Input: The user uploads handwriting samples (scanned images, digital handwriting photographs) and text samples (text files) to the system.

[1072] Specific operation: The user uploads these samples through the system interface. For example, they provide the system with a scanned image of a handwritten New Year's card that reads "Happy New Year" and a corresponding text file.

[1073] Output: The uploaded sample data is saved on the server.

[1074] Step 2:

[1075] Input: The server converts handwritten samples into digital data using OCR technology.

[1076] How it works: The server analyzes the uploaded handwritten image and converts it into digital data, using, for example, Tesseract OCR. It extracts the outline and shape of each character.

[1077] Output: Digital data of handwritten characters is obtained.

[1078] Step 3:

[1079] Input: Digital data of handwritten characters.

[1080] What it does: The server runs an algorithm to generate a custom font for the user based on the extracted character contours and shapes.

[1081] Output: Font data is generated that reproduces the user's unique handwriting.

[1082] Step 4:

[1083] Input: A text file of user-supplied text.

[1084] What it does: The server uses NLP technology (e.g., "spaCy" or "NLTK") to analyze the text sample, extracting stylistic features, punctuation usage, sentence structure, etc.

[1085] Output: Data that has learned the user's unique writing style characteristics is obtained.

[1086] Step 5:

[1087] Input: Parsed text content data.

[1088] How it works: The emotion recognition engine built into the server analyzes emotional patterns. For example, to identify emotional states such as "positive" or "negative," the "Sentiment Analysis API" is used.

[1089] Output: The emotional information inherent in the text is analyzed, and data is generated to reflect this in the writing style.

[1090] Step 6:

[1091] Input: The user inputs the content of the document they want to generate into the system.

[1092] Specific operation: The user inputs the content of the "New Year's greeting message" through the interface, for example, "Happy New Year. I look forward to working with you again this year."

[1093] Output: The input data is sent to the server.

[1094] Step 7:

[1095] Input: Input content data, learned stylistic features, and sentiment information.

[1096] Specific operation: The server runs a text generation algorithm based on this data. The generated text reflects the user's writing style and emotions and is converted to look handwritten using a handwriting font.

[1097] Output: A handwritten document is generated and output as a preview image.

[1098] Step 8:

[1099] Input: The generated handwritten document.

[1100] Specific operation: The device displays a preview of the generated handwritten-style document to the user. The user can check the preview and edit it as needed, for example, by changing the content or style of the text.

[1101] Output: The corrected document data.

[1102] Step 9:

[1103] Input: The edited document data.

[1104] Specific operation: The server generates the final handwritten-style document in the specified format (e.g., PDF, JPEG), and sends the generated document to the device.

[1105] Output: The final handwritten document file is provided to the user.

[1106] Through these steps, the system can automatically generate high-quality handwritten documents that reflect the user's handwriting, writing style, and emotions.

[1107] (Application example 2)

[1108] 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."

[1109] In modern digital communication, there is a growing demand for personalized messages. However, generating handwritten-style messages is time-consuming and there are limited easy-to-use methods. Furthermore, it is difficult to generate documents that reflect the user's emotions, and they tend to be bland because they rely on a single template. Therefore, there is a need for a system that can easily generate personalized handwritten-style messages and provide high-quality documents that reflect the user's emotions.

[1110] 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 converting handwritten character samples provided by a user into digital data using optical character recognition technology; means for generating a user-specific font from the handwritten character digital data; means for analyzing the user-provided text sample and learning stylistic features using natural language processing technology; means for automatically generating handwritten-style text based on content specified by the user using the generated font and the learned stylistic features; means for analyzing the user's emotions based on the content of the generated text and reflecting them in the text; means for displaying the automatically generated handwritten-style text to the user and making it editable; and means for outputting the edited text in a specific format and transmitting it to a communication terminal. This makes it possible to easily generate personalized handwritten-style messages and provide high-quality documents that reflect the user's emotions.

[1111] A "handwriting sample" is an image or digital data of a user's own handwriting that the user provides to the system.

[1112] "Optical character recognition technology" is a technology that reads character information from an image and converts it into digital data.

[1113] "Digital data" refers to data that has been converted from analog information into digital form.

[1114] A "user-specific font" is a font that is generated to reproduce the characteristics of a user's handwriting.

[1115] "Sample text" is data of text written in the past that the user provides to the system.

[1116] "Stylistic features" are specific styles such as expression patterns, use of punctuation, and sentence structure in writing.

[1117] "Natural language processing technology" is a technology for understanding and analyzing human language.

[1118] "Handwritten text" is text in digital form that reproduces the style of a user's handwriting.

[1119] "Means for analyzing emotions and reflecting them in writing" refers to a method for analyzing the emotional state of a user from the text they have entered or from past writing, and adjusting the expression and writing style based on that.

[1120] The "means for enabling editing" is a function that allows the user to check the generated handwritten-style text and make corrections or changes as necessary.

[1121] "Means for outputting in a specific format and transmitting to a communication terminal" refers to a method for generating the final handwritten-style text in a specified format (such as PDF or JPEG) and transmitting it to the user's device.

[1122] MODE FOR CARRYING OUT THE INVENTION

[1123] The present invention is a system for generating personalized handwritten messages in a specific format and sending them to a user's communication device. The system is designed to utilize handwriting samples and sentence samples to generate a user's personalized font and writing style, and further analyze and reflect sentiment in the sentences. Specific examples are described below.

[1124] Program processing explanation

[1125] Hardware and software used

[1126] Hardware: Servers, user devices (PCs, smartphones, tablets)

[1127] Software: OCR technology (pytesseract), natural language processing technology (NLP tools), emotion engine (emotion_engine)

[1128] 1. Upload your handwritten sample:

[1129] Users upload their own handwritten character samples (images, digital data) and past writing samples (text files) to the system, which converts the samples into digital data using OCR technology (pytesseract), and extracts the outline and shape characteristics of the characters.

[1130] 2. Font generation:

[1131] The server uses the extracted character features to generate a user-specific font that faithfully reproduces the user's handwriting style.

[1132] 3. Analysis of stylistic features:

[1133] The server uses natural language processing technology to analyze user-provided text samples and learn stylistic features, identifying expression patterns and sentence structures within the text and defining the user's unique writing style.

[1134] 4. Emotion analysis:

[1135] The emotion engine analyzes the content of the user's text and recognizes emotions from it, allowing the user's emotional state to be reflected in the text.

[1136] 5. Message Creation:

[1137] The user inputs the message content they want to generate using the system interface. The input data is automatically generated as handwritten text based on the generated font and the learned stylistic features and emotional information.

[1138] 6. Preview and edit:

[1139] The server generates a preview image of the generated handwritten text and sends it to the user's terminal. The user can check the preview and edit it as necessary.

[1140] 7. Final output and transmission:

[1141] Once editing is complete, the server generates the final handwritten-style text in the specified format (PDF, JPEG) and sends it to the user's device, where the user can download it and use it as needed.

[1142] Specific examples

[1143] For example, if a user wants to purchase a special gift from a virtual store and want to include a handwritten message, the user can upload handwriting samples and past writings to the system, which will then generate a personalized message.

[1144] Prompt Sentence Examples

[1145] "Generate personalized handwritten messages using your handwriting and text samples."

[1146] Examples:

[1147] Handwritten character samples: "sample1.png", "sample2.png"

[1148] Text samples: "sample_text1.txt", "sample_text2.txt"

[1149] Message we want to generate: "Thank you for shopping with us! Your support means a lot to us."

[1150] This allows users to easily create personalized handwritten messages and provide a special customer experience in virtual stores.

[1151] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1152] Step 1:

[1153] (Sample upload)

[1154] Users upload sample images of their own handwritten characters and sample text files of past sentences to the system, which receives the images of handwritten characters and the text files of sentences as input and sends them to the server.

[1155] Input: Sample image of handwritten characters, sample text file

[1156] Output: Sample images of handwritten characters transferred to the server, sample text files

[1157] Step 2:

[1158] (OCR processing of handwritten characters)

[1159] The server converts sample images of handwritten characters into digital data using OCR technology (pytesseract), and then processes the images to recognize characters and extract their outlines and shapes.

[1160] Input: Sample image of handwritten characters

[1161] Data processing / data calculation: Character recognition and contour extraction using OCR

[1162] Output: Digital data of handwritten characters

[1163] Step 3:

[1164] (font generation)

[1165] The server generates a user-specific font based on the digital data of handwritten characters obtained through OCR processing. It creates a font file using the character feature information.

[1166] Input: Digital data of handwritten characters

[1167] Data processing / data calculation: character feature modeling and font file generation

[1168] Output: User-specific font file

[1169] Step 4:

[1170] (Analysis of stylistic features)

[1171] The server analyzes the sample text provided by the user using natural language processing technology (NLP tools) to learn stylistic features, extracting sentence structure and expression patterns, and creating a unique stylistic model for the user.

[1172] Input: Sample text file of sentences

[1173] Data processing / data calculation: Extraction and learning of stylistic features using NLP

[1174] Output: User-specific writing style model

[1175] Step 5:

[1176] (emotional analysis)

[1177] The emotion engine recognizes emotions from the content of the text provided by the user and applies algorithms to recommend appropriate writing styles and expressions based on that. It learns emotional patterns and reflects them in the generated text.

[1178] Input: Text content

[1179] Data processing / data calculation: Applying emotion recognition models and learning emotion patterns

[1180] Output: Sentiment analysis results and recommended writing style

[1181] Step 6:

[1182] (Message generation)

[1183] Based on the message content entered by the user into the system interface, the server automatically generates handwritten-style text using the generated font, learned stylistic features, and emotional information. Finally, the handwritten-style text is created using the digital font.

[1184] Input: Message content, user-specific font, writing style model, sentiment analysis results

[1185] Data processing / data calculation: style model, sentence generation based on emotion information, font application

[1186] Output: Handwritten message

[1187] Step 7:

[1188] (Preview and Edit)

[1189] The server generates a preview image of the handwritten text and sends it to the user's terminal. The user can check the preview and edit it as necessary.

[1190] Input: Handwritten message

[1191] Data processing / data calculation: Preview image generation

[1192] Output: Preview image

[1193] Step 8:

[1194] (Final output and transmission)

[1195] After the user has completed editing, the server generates the final handwritten-style text in the specified format (PDF, JPEG) and sends it to the user's device, where the user can download the file and use it.

[1196] Input: Edited handwritten message

[1197] Data processing / data calculation: Generate files in specified format

[1198] Output: Final PDF or JPEG file

[1199] These processing steps allow users to easily create personalized handwritten messages, providing high-quality documents that reflect their emotions.

[1200] 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.

[1201] 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.

[1202] 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.

[1203] [Fourth embodiment]

[1204] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1205] 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.

[1206] 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).

[1207] 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.

[1208] 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.

[1209] 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).

[1210] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1211] 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.

[1212] 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.

[1213] 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.

[1214] 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.

[1215] 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.

[1216] 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."

[1217] The present invention provides a system that automatically generates handwritten-style documents by learning the characteristics of handwritten characters and writing styles provided by a user. This system is configured as follows.

[1218] First, the user provides the system with samples of their handwriting and previous writing, which can be in the form of scanned images, digital photographs of their handwriting, or text files.

[1219] The server converts the handwritten sample provided by the user into digital data using OCR (optical character recognition) technology, which involves detailed analysis of the contours and shape characteristics of each character and then generates a unique font for the user.

[1220] The server then analyzes the user-provided text samples using natural language processing (NLP) techniques, learning the user's writing style, including their grammar, punctuation, and sentence structure.

[1221] The user inputs the content of the document they want to create through the system interface, and the terminal checks the input and sends it to the server.

[1222] The server converts the input content into text in the user's writing style based on the input content and the learned writing style characteristics.The server then converts the generated text into a handwritten-style text using the user's unique font and generates a preview image.

[1223] The device displays a preview of the generated handwritten document to the user, who can check the content and appearance of the document and make edits as needed. Once editing is complete, the server outputs the final handwritten document in the specified format (e.g., PDF, JPEG) and sends it to the device.

[1224] Specific examples

[1225] As a specific example, a scene in which a user creates a New Year's card as a New Year's greeting will be described.

[1226] 1. Providing handwritten data

[1227] The user uploads to the system a handwritten New Year's card sample created in the past and a text file of an apology letter. The handwritten sample contains greetings such as "A Happy New Year to You".

[1228] 2. Recognition of Handwritten Characters

[1229] The server recognizes the uploaded New Year's card sample using OCR technology and extracts the shape patterns of each character in "A Happy New Year to You" and "Please give me your continued support this year" as digital data.

[1230] 3. Font Generation

[1231] The server generates a user-specific font based on the recognized shape patterns of the handwritten characters. For example, it saves the unique handwriting of characters such as "明" and "年" as fonts.

[1232] 4. Learning of Writing Styles

[1233] The server analyzes the text file of the apology letter using NLP technology and learns expression patterns and sentence structures such as "I'm very sorry for the trouble I've caused you".

[1234] 5. Input of Generation Requirements

[1235] The user inputs "Please write a New Year's greeting" through the interface. Specifically, a sentence such as "A Happy New Year! Please give me your continued support this year." is requested.

[1236] 6. Automatic Generation of Documents

[1237] The server generates a sentence such as "A Happy New Year! Please give me your continued support this year." based on the input content and the learned writing style features. Then, it creates a handwritten-style New Year's card using the user-specific font.

[1238] 7. Viewing and Editing Results

[1239] The terminal displays a preview of the generated handwritten New Year's card, and the user can confirm it. For example, the user can edit it to say, "Happy New Year! I hope you are healthy this year."

[1240] 8. Document Output

[1241] Once the user is satisfied with the document, the server generates the final handwritten New Year's card in PDF format and sends it to the device, where the user can print it and use it as an actual New Year's card.

[1242] This system allows users to easily generate high-quality handwritten-looking documents that utilize their own handwritten characters and writing style, while eliminating the need for handwriting.

[1243] The processing flow will be explained below.

[1244] Step 1: User provides handwritten data

[1245] Through the system's interface, users upload handwriting samples and past writing samples, which can include scanned images, digital photographs of handwriting, or text files.

[1246] Step 2: The server recognizes the handwritten characters

[1247] The server converts the uploaded handwritten samples into digital data using OCR (optical character recognition) technology, which extracts the outline and shape of each character and generates a digital representation of the character.

[1248] Step 3: The server generates the font

[1249] The server generates a user-specific font from the digital data of the handwritten characters, which involves detailed analysis of the character shape patterns to create a font that reflects the user's unique handwriting and style.

[1250] Step 4: The server learns the writing style

[1251] The server analyzes the sample text provided by the user using NLP (natural language processing) technology, specifically extracting and learning stylistic features such as expression patterns, punctuation, and sentence structure used in the text.

[1252] Step 5: User enters generation request

[1253] The user uses the system's interface to input the content of the document they want to generate, which may include entering a specific phrase or sentence.

[1254] Step 6: The server automatically generates the document

[1255] The server converts the specified content into text in the user's writing style based on the input content and the learned writing style characteristics. For example, it generates a document based on the input content, such as a greeting for a New Year's card or an apology letter.

[1256] Step 7: The server converts it to handwritten style

[1257] The server converts the generated text into a handwritten style using the user's own font, resulting in a visual representation of the text as if it were handwritten.

[1258] Step 8: Your device will display the results

[1259] The device displays a preview of the generated handwritten document to the user, allowing the user to check the content and appearance of the document and make edits as needed.

[1260] Step 9: User edits the document

[1261] The user can edit the generated document through the system interface, for example by correcting parts of the text or inserting additional messages.

[1262] Step 10: The server outputs the final document

[1263] The server generates a document in the specified format (PDF, JPEG, etc.) that has been finalized by the user, and sends the generated document to the terminal so that the user can download or print it.

[1264] Example 1

[1265] 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."

[1266] In recent years, there has been an increasing need for users to efficiently generate documents that look handwritten. However, with conventional technologies, it has been difficult to efficiently convert handwritten characters and writing styles into digital data and automatically generate documents that look handwritten. In particular, generating a user-specific font or learning a writing style from past documents and reflecting it in new documents requires advanced technology, making it difficult to achieve.

[1267] 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.

[1268] In this invention, the server includes means for converting handwritten character samples provided by a user into digital data using optical character recognition technology, means for generating a user-specific font from the digital handwritten character data, and means for analyzing the user-provided text samples and learning stylistic features using natural language processing technology, thereby enabling the user to automatically generate handwritten-look documents that reflect their own handwriting and writing style.

[1269] "Handwriting sample" refers to a user-provided material containing handwritten characters, and refers to digital data such as a scanned image or digital photograph.

[1270] "Optical character recognition technology" is a technology that analyzes the character information contained in images and photographs and converts it into text data.

[1271] "Digital data" refers to information that has been converted into a form that a computer can understand and process.

[1272] A "user-specific font" is a font that reproduces the characteristics of the user's handwritten characters, and refers to font data that includes individual character shapes and handwriting characteristics.

[1273] "Sample writing" refers to digital data of previously written writing provided by the user, which is a text file for learning stylistic features.

[1274] "Natural language processing technology" refers to technology that enables computers to understand, analyze, and generate human language.

[1275] "Interface" refers to the screen and operating environment through which a user interacts with a system and inputs information.

[1276] "Handwritten-style text" refers to a digital document that is generated using a font that reproduces the user's handwriting and that looks handwritten.

[1277] "Preview" refers to a function that displays the shape and content of the final generated document in a state that allows the user to check it.

[1278] "Format" refers to the output format of the digital document, including file formats such as PDF and JPEG.

[1279] The present invention is a system that learns handwritten characters and writing styles provided by a user and automatically generates handwritten-style documents based on them. This system is composed of a server, a terminal, and a user interface, and is implemented using the following hardware and software.

[1280] The server converts handwritten samples provided by the user into digital data using optical character recognition technology (OCR). The specific OCR software used is "Tesseract OCR." This allows for detailed analysis of the contours and shapes of the handwritten characters, and the server uses the results to generate a digital font unique to the user. The font creation tool "FontForge" is used to generate the font.

[1281] The server then analyzes the user-provided text samples using natural language processing (NLP) techniques, such as "spaCy" and "NLTK." Through this analysis, the server learns the user's writing style, including their grammar, punctuation, and sentence structure.

[1282] The user inputs the content of the document they want to generate through the interface. Specifically, the user uses a terminal to input a prompt such as "Please write a New Year's greeting." This input is sent from the terminal to the server. The server generates a sentence based on the input content and pre-trained stylistic features. The generated sentence is then converted to look like handwriting using a user-specific font.

[1283] The terminal displays the generated handwritten document preview to the user. The user can check the preview content on the terminal and edit it as necessary. For example, the sentence "Happy New Year. I hope you will continue to work hard this year." can be changed to "Happy New Year. I hope you will be healthy this year."

[1284] Finally, the server outputs the edited text in the specified format (e.g., PDF, JPEG) and sends it to the terminal, where the user can download the final document, print it, and use it as an actual New Year's card, etc.

[1285] A concrete example of a prompt is as follows:

[1286] "Please upload a handwriting sample."

[1287] "Please upload a sample of your previous writing."

[1288] Please write a New Year's greeting.

[1289] Please review the preview and make any edits you need.

[1290] This system allows users to efficiently generate high-quality handwritten documents that reflect their own handwriting style and idiom.

[1291] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1292] Step 1: User provides handwriting and writing samples

[1293] The user accesses the system interface and uploads an image file (e.g., PNG, JPEG) of a handwriting sample and a text file (e.g., TXT, DOC) of a writing sample. The image file and the text file are provided to the system as input. These files are sent to the server as output, ready for the next analysis step.

[1294] Step 2: The server analyzes the handwriting sample using OCR technology

[1295] The server receives the handwritten sample image uploaded by the user and begins analyzing it using "Tesseract OCR." It receives the image file of the handwritten sample as input and uses OCR technology to analyze the outlines and shapes of the characters. Digital data for each character is extracted as output and stored on the server. The server then uses this data to prepare for font generation.

[1296] Step 3: The server generates a font from the handwritten characters

[1297] The server uses the font creation tool "FontForge" to generate a user-specific font based on the digital data of handwritten characters obtained using OCR technology. Using the digital data of the characters as input, it runs an algorithm to extract the shape patterns of the characters. The output is a user-specific font file (e.g., .ttf or .otf), which is stored on the server.

[1298] Step 4: The server analyzes the text sample using natural language processing

[1299] The server uses "spaCy" or "NLTK" to analyze the text samples uploaded by users. It receives the text sample as input and uses natural language processing technology to analyze the sentence's expression patterns, punctuation usage, sentence structure, etc. The output is stylistic features extracted and stored on the server. The server uses this data to prepare the document to be generated.

[1300] Step 5: The user enters the generation request in the interface.

[1301] The user uses the system's interface to input the content of the document they want to generate. Specifically, they type "Please write a New Year's greeting" and click the send button. This sends the generation request content as input to the server. The server then receives the input and is ready to proceed to the next step.

[1302] Step 6: The server generates the document content and converts it to a handwritten style.

[1303] The server uses natural language processing technology to generate text based on the generation request received from the user and the stylistic features it has previously learned. The generation request and stylistic feature data are used as input. A font specific to the user is used to convert the generated text into a handwritten-style text. The document converted into a handwritten-style text is generated as output as preview image data and saved on the server.

[1304] Step 7: The device will display a preview of the generated document

[1305] The terminal displays a handwritten document preview sent from the server to the user. It receives preview image data from the server as input and displays it on the interface. The user can check the displayed preview and is ready to make corrections if necessary.

[1306] Step 8: User reviews and edits the document

[1307] The user checks the preview document displayed on the terminal and edits the text content as necessary. For example, they edit the text from "Happy New Year. Thank you for your continued support this year." to "Happy New Year. I hope you stay healthy this year." The corrected text content is sent as input to the server. The server generates a new preview image reflecting the edited content as output.

[1308] Step 9: The server outputs the final document and sends it to the device.

[1309] The server receives final confirmation of the edits from the user and generates the final document in the specified format (e.g. PDF, JPEG). It receives the edited text data as input. It saves the final document as output in PDF or JPEG format and sends it to the terminal. The user can download the final document from the terminal and print it to use as an actual New Year's card, etc.

[1310] The above is the specific processing flow of the program.

[1311] (Application example 1)

[1312] 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."

[1313] Previous systems for generating handwritten-style messages not only required a complex and time-consuming process for training handwriting samples and writing styles, but also resulted in messages that were not effectively integrated into product images. Furthermore, there was a lack of an easy way for users to add handwritten-style messages when customizing their orders.

[1314] 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.

[1315] In this invention, the server includes means for converting handwritten sample text provided by a user into digital data using optical character recognition technology, means for generating a user-specific font from the digital data of the handwritten text, means for analyzing the user-provided text sample and learning stylistic features using natural language processing technology, means for automatically generating handwritten-style text based on content specified by the user using the generated font and the learned stylistic features, means for displaying the automatically generated handwritten-style text to the user and making it editable, means for generating a handwritten-style message to accompany a product and combining it with a product image, and means for outputting the edited text in a specified format. This allows users to easily create and edit handwritten-style messages and integrate them with product images when placing orders.

[1316] "Handwritten characters" refer to characters written by a user by hand.

[1317] A "sample" is a portion of handwritten text or a sentence that is provided to the system for learning.

[1318] "Optical character recognition technology" is a technology that converts character information acquired as an image into digital data.

[1319] "Digital data" refers to the digitized form of analog data used to process and store information electronically.

[1320] A "font" is a set of characters created according to specific design rules.

[1321] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[1322] "Stylistic features" refer to the characteristics of individual writing styles, such as sentence expression patterns, use of punctuation, and sentence structure.

[1323] "Handwritten-style text" refers to text that is generated from digital data in a format that looks like handwriting.

[1324] "User" refers to an individual or corporation that uses the system.

[1325] The "means for enabling editing" refers to an interface that allows the user to check the generated handwritten-style text and make changes as necessary.

[1326] The "specified format" refers to the format in which the final handwritten-style text is output, such as PDF or JPEG.

[1327] "Products" means goods and services sold in the Virtual Store.

[1328] "Message" refers to system-generated text that a user can accompany with an item.

[1329] "Product images" refers to visual data that represents a product, including photographs and illustrations.

[1330] "Synthesis" refers to the process of combining multiple pieces of data into one.

[1331] This invention relates to a system that learns a user's handwriting and writing style, generates handwritten-style messages, and combines them with product images. As an embodiment of the invention, a specific description will be given using an application example in which handwritten-style messages are attached to products in a virtual store.

[1332] System Configuration

[1333] Providing a sample of handwriting

[1334] Users upload handwriting samples to the system as image data captured using a smartphone or scanner, which is then sent to a server where it is converted into digital data using optical character recognition (OCR) technology.

[1335] Font Generation

[1336] The server uses OCR technology to generate a unique font for each user from the digital data of handwritten characters. Software such as Tesseract and OpenCV are used to extract the outline and shape characteristics of the characters to create a unique font.

[1337] Studying writing style

[1338] Users provide the system with text files containing examples of their writing. The text is then sent to a server, where natural language processing (NLP) techniques are used to learn stylistic features. This process uses NLP libraries such as NLTK and SpaCy.

[1339] Message Generation

[1340] The user inputs a handwritten-style message to accompany the product order through the system interface. The server converts the input message into a handwritten-style sentence using the user's writing style and a custom font. The generated handwritten-style message is then superimposed onto the product image. The image processing used here includes "PIL" and "OpenCV."

[1341] Viewing and editing messages

[1342] The generated handwritten message is previewed on the user's device, and the user can review the preview and edit the message as needed. The edited content is then resent to the server and converted back into handwritten text.

[1343] Final Output

[1344] The final handwritten message is then generated in a specified format (PDF, JPEG, etc.) and sent to the user's device. The user can then download this data and use it to order products.

[1345] Specific examples of use

[1346] For example, consider a scenario in which a user orders a birthday gift from a virtual store and creates a handwritten message saying "Happy Birthday" to accompany the gift. The user uploads images of handwritten text such as "Thank you" and "Congratulations" to the system as samples, and provides a text file of a blog post they previously wrote as a writing style sample. After that, by entering "Happy Birthday," the server combines the handwritten message with the product image and displays a preview. After the user has confirmed and edited the message, the final message is output in the specified format, completing the ordering process.

[1347] Prompt Sentence Examples

[1348] "I want to create handwritten birthday messages. Develop an application that learns the user's handwriting and writing style to generate 'Happy Birthday' messages."

[1349] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1350] Step 1: Provide a sample of your handwriting

[1351] Users use their smartphones or scanners to capture sample images of handwritten characters and upload them to the system, which then sends the image data to the server.

[1352] Input: Handwritten text image file (JPEG or PNG format)

[1353] Output: Image data stored on the server

[1354] Step 2: Recognizing handwriting

[1355] The server analyzes the received sample image of handwritten characters using OCR technology. Specifically, it reads the image using OpenCV and recognizes the characters using Tesseract.

[1356] Input: Image data of handwritten characters

[1357] Output: Character string converted into digital data by OCR

[1358] Step 3: Font generation

[1359] The server extracts the outline and shape characteristics of the characters recognized by OCR and generates a unique font using an outline extraction algorithm.

[1360] Input: Character string data obtained by OCR

[1361] Output: User-specific font data

[1362] Step 4: Provide a writing sample

[1363] Users upload samples of their writing (text files) to the system, and this data is sent to the server.

[1364] Input: Text file (TXT format)

[1365] Output: Text data stored on the server

[1366] Step 5: Learning stylistic features

[1367] The server analyzes the received text samples using natural language processing technology to learn the user's writing style. This analysis involves analyzing the structure of the sentences using NLTK and SpaCy.

[1368] Input: Text sample data

[1369] Output: Stylistic feature data

[1370] Step 6: Enter a Message Generation Request

[1371] The user inputs the message content they want to create through the system interface, and this content is sent to the server.

[1372] Input: Message content (e.g. "Happy Birthday")

[1373] Output: Message content saved on the server

[1374] Step 7: Create a handwritten message

[1375] The server converts the input message content into a handwritten-like form using the user's writing style and a custom font, and then generates an image of the message using PIL (Python Imaging Library).

[1376] Input: Message content, style feature data, unique font data

[1377] Output: Handwritten message image

[1378] Step 8: Adding a message to product images

[1379] The server synthesizes the generated handwritten message onto the product image using OpenCV and PIL.

[1380] Input: Handwritten message image, product image

[1381] Output: Product image with message

[1382] Step 9: Review and edit your message

[1383] The terminal displays a preview of the product image with the generated handwritten message to the user, who can check it and edit the message as necessary.

[1384] Input: Product image with message

[1385] Output: Message content edited by the user

[1386] Step 10: Final output

[1387] The server outputs the final edited handwritten-style message in a specified format (PDF, JPEG, etc.) and sends the final data to the terminal.

[1388] Input: Edited message content, product image

[1389] Output: Final output data in the specified format

[1390] 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.

[1391] The present invention is a system that automatically generates handwritten-style documents by learning the characteristics of handwritten characters and sentence samples provided by the user, and further combines it with an emotion engine that recognizes the user's emotions. The system consists of the following components:

[1392] First, users upload samples of their handwriting and previous writing to the system, which can include scanned images, digital photographs of their handwriting, text files, etc. The samples are used as the basis for recognizing the shape of the handwriting.

[1393] The server converts the uploaded handwritten text samples into digital data using OCR (optical character recognition) technology, extracting the outline and shape of each character and generating a custom font for the user. This creates the foundation for digitally reproducing handwritten text.

[1394] The server then analyzes the user-provided text samples using natural language processing (NLP) techniques. This analysis extracts stylistic features such as expression patterns, punctuation usage, and sentence structure within the text, and learns the user's unique writing style. This allows the generated documents to faithfully reproduce the user's writing style.

[1395] Furthermore, an emotion engine is built into the server, which analyzes the text entered by the user and recognizes emotions from it. The emotion engine learns the emotional patterns inherent in the user's text and reflects these when generating text. At the same time, it analyzes the user's emotional state in real time and suggests appropriate writing styles and expressions.

[1396] The user can input the content of the document they want to create through the system interface. For example, they can input the content of a New Year's greeting card or an apology letter. The input data is then sent to the server.

[1397] The server converts the specified content into a sentence in the user's writing style based on the input content and the learned stylistic features and emotional information, and then converts it into a handwritten-style sentence using a handwriting font to generate a preview image.

[1398] The terminal displays these handwritten-look documents to the user in a preview format. The user can check the document contents and edit them as necessary. Once editing is complete, the server outputs the final handwritten-look document in the specified format (e.g., PDF, JPEG) and sends it to the terminal.

[1399] Specific examples

[1400] As a specific example, a scene in which a user creates a New Year's card as a New Year's greeting will be described.

[1401] 1. The user uploads a sample handwritten New Year's card they have created in the past and a text file containing the text. The sample New Year's card contains greetings such as "Happy New Year."

[1402] 2. The server uses OCR technology to recognize the New Year's card samples and extracts the character shapes of "Akemashite Omedetou Gozaimasu" and "Kotoshi mo Yoroshiku Onegai Shimasu" as digital data.

[1403] 3. The server generates a user - specific font based on the shape pattern of handwritten characters. As a result, the unique handwriting of characters such as "Aki" and "Toshi" is saved as a digital font.

[1404] 4. The server analyzes the text file of the article using NLP technology and learns expression patterns such as "Moushiwake Arimasen ga, Go Meiwaku wo Oka ke shite itashimashita" and the sentence structure.

[1405] 5. The emotion engine analyzes the emotion from the user's input content and past articles and adjusts appropriate expressions and styles.

[1406] 6. The user inputs a "New Year greeting sentence" through the interface and requests the system to generate a New Year's card.

[1407] 7. Based on the input content, learned style features, and emotion information, the server generates a sentence such as "Akemashite Omedetou Gozaimasu. Kotoshi mo Yoroshiku Onegai Itashimasu." Furthermore, it creates a handwritten - style New Year's card using the user - specific font.

[1408] 8. The terminal previews and displays the generated handwritten - style New Year's card for the user to check the content. For example, it can be edited to "Akemashite Omedetou Gozaimasu. Kotoshi mo Douzo Go Kenkou de Arimasu You ni."

[1409] 9. When the user is satisfied with the document, the server generates the final handwritten - style New Year's card in PDF format and sends it to the terminal. The user can download this and print it for use as an actual New Year's card. ​​This system allows users to generate high-quality handwritten-looking documents that utilize their own unique handwriting and writing style, while reducing the effort required for handwriting, and that also appropriately reflect emotions.

[1411] The processing flow will be explained below.

[1412] Step 1: User provides handwritten data

[1413] Through the system's interface, users upload handwriting samples and samples of previous writing, including scanned images, digital photographs, and text files, and the system begins to recognize the user's unique writing and writing characteristics.

[1414] Step 2: The server recognizes the handwritten characters

[1415] The server receives the uploaded handwritten samples and converts them into digital data using OCR (optical character recognition) technology. The server extracts the outline and shape of each character and generates a digital representation of the character, which provides the basis for creating a handwritten font.

[1416] Step 3: The server generates the font

[1417] The server generates a user-specific font based on the digital data of handwritten characters, based on a detailed analysis of the shape patterns of each character. The generated font preserves the user's unique handwriting and style and is used for subsequent text generation.

[1418] Step 4: The server learns the writing style

[1419] The server uses natural language processing (NLP) technology to analyze the sample text provided by the user, extracting stylistic features such as expression patterns, punctuation usage, and sentence structure. This allows the system to learn the user's unique writing style and reflect it in the text it generates.

[1420] Step 5: The server recognizes the emotion

[1421] The server's built-in emotion engine recognizes emotions from user-provided text and real-time input. The server performs sentiment analysis based on keywords and expressions contained in the text and adds this to the user's writing style data.

[1422] Step 6: User enters generation request

[1423] The user uses the system interface to input the content of the document they want to generate, for example, by making a request such as "Please write a New Year's greeting." The input is then sent to the server.

[1424] Step 7: The server automatically generates the document

[1425] The server converts the specified content into a sentence in the user's writing style based on the content entered by the user, the learned stylistic features, and emotional information. The generated sentence is then converted into a handwritten-style sentence using the user's own font.

[1426] Step 8: Your device will display the results

[1427] The device displays a preview of the handwritten document to the user. The user can check the content and appearance of the document and edit it as needed. For example, they can edit the greeting text in a New Year's card or an apology letter.

[1428] Step 9: User edits the document

[1429] The user edits the generated document through the system interface, for example, by changing part of the greeting or inserting an additional message. Once the editing is complete, the user performs a final confirmation.

[1430] Step 10: The server outputs the final document

[1431] The server generates a document in the specified format (PDF, JPEG, etc.) that has been finalized by the user, and the generated document is sent to the terminal, where the user can download or print it.

[1432] Through the above processing steps, users can reduce the effort required for handwriting, automatically generate documents that make use of their own handwritten characters and writing style, and can also take advantage of high-quality handwritten-style documents that appropriately reflect emotions using an emotion engine.

[1433] Example 2

[1434] 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."

[1435] Conventional handwritten-style document generation systems have difficulty fully reflecting the characteristics and style of a user's handwriting, and lack the ability to recognize the emotion of the user's input text and reflect it in the document. This makes it difficult to automatically generate documents personalized for each user. Furthermore, the generated documents cannot be easily previewed or edited, resulting in a problem of low user convenience.

[1436] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for converting handwritten character samples provided by the user into digital data using optical character recognition technology, means for generating a user-specific font from the handwritten character digital data, means for analyzing text samples provided by the user and using natural language processing technology to learn stylistic features, and means for analyzing the content of text entered by the user, recognizing emotions, and reflecting the emotions in document generation. This enables the automatic generation of personalized handwritten-style documents that reflect the user's handwritten character features, style, and emotions.

[1437] A "handwriting sample" is user-provided handwriting data in digital form or as a scanned image.

[1438] "Optical character recognition technology" is a technology for converting characters in an image into digital data.

[1439] "Digital data" is data in a format that can be processed by a computer.

[1440] A "user-specific font" is a special font that is generated based on the characteristics of the user's handwriting.

[1441] "Natural language processing technology" is a technology that analyzes text data and understands and processes stylistic features and content.

[1442] "Style characteristics" are characteristics such as expression patterns, use of punctuation marks, sentence structure, etc., when a particular user writes a sentence.

[1443] "Emotion recognition" is a technology that analyzes and identifies emotions from the content of a user's writing.

[1444] "Handwritten text" is text in digital form that reflects a user's handwriting and writing style.

[1445] "Automatic generation" means that the system creates an artifact through a specific process without human intervention.

[1446] A "preview format" is a temporary or provisional display format used to confirm the final result.

[1447] "Making it editable" means allowing a user to make changes or modifications to the generated document.

[1448] "Specified format" refers to the particular format in which a document or data is saved (e.g., PDF, JPEG).

[1449] This invention is a system that automatically generates handwritten documents by allowing a user to provide handwritten and written samples, and further combines it with an emotion engine that recognizes the user's emotions. The system includes multiple means for digitally reproducing the user's handwriting and writing style and automatically generating documents that reflect the user's emotions.

[1450] Users first upload samples of their handwriting and previous writing to the system, which can include scanned images, digital photographs of their handwriting, text files, etc. These samples are used as the basis for recognizing the shape of the handwriting.

[1451] The server converts the uploaded handwritten sample into digital data using OCR (Optical Character Recognition) technology. Specifically, the OCR technology used is "Tesseract OCR." The outline and shape of each character are extracted and a custom font is generated based on this. This font reflects the user's unique handwriting.

[1452] The server then analyzes the user-provided text samples using NLP (natural language processing) technology, specifically technologies such as "spaCy" and "NLTK." This analysis extracts stylistic features such as expression patterns, punctuation usage, and sentence structure within the text, and learns the user's unique writing style.

[1453] Furthermore, an emotion engine is built into the server, which analyzes the text entered by the user and recognizes emotions from it. For example, the "Sentiment Analysis API" is used. This emotion engine learns the emotional patterns inherent in the user's text and reflects them when generating text. At the same time, it analyzes the user's emotional state in real time and suggests appropriate writing styles and expressions.

[1454] The user inputs the content of the document they want to create through the system interface. For example, they input the content of a New Year's greeting card or an apology letter. The input data is then sent to the server.

[1455] The server converts the specified content into a sentence in the user's writing style based on the input content and the learned stylistic features and emotional information, and then converts it into a handwritten-style sentence using a handwriting font to generate a preview image.

[1456] The terminal displays these handwritten-style documents to the user in a preview format. The user can check the contents of the document and edit them as necessary. For example, the user can edit it to say, "Happy New Year! I hope you stay healthy this year." Once editing is complete, the server outputs the final handwritten-style document in a specified format (e.g., PDF or JPEG) and sends it to the terminal. The user can download it, print it, and use it as an actual New Year's card.

[1457] As a specific example, a scene where a user creates a New Year greeting card is shown. The user uploads a handwritten New Year greeting card sample created in the past and a text file of the text of the message to the system. The New Year greeting card sample contains greeting messages such as "A very happy new year".

[1458] The server recognizes the New Year greeting card sample using OCR technology and extracts the character shapes of "A very happy new year" and "Please take care of me this year" as digital data. Next, a user-specific font is generated based on the shape pattern of the handwritten characters. As a result, the unique handwriting of characters such as "明" and "年" is saved as a digital font.

[1459] Also, the server analyzes the text file of the message using NLP technology and learns expression patterns such as "Sorry for the trouble" and the sentence structure. The emotion engine analyzes the emotion from the user's input content and past messages and adjusts the appropriate expression and style.

[1460] The user inputs "New Year greeting message" through the interface and requests the system to generate a New Year greeting card. The server generates a sentence "A very happy new year. Please take care of me this year." based on the input content, learned style characteristics, and emotion information, and creates a handwritten-style New Year greeting card using the user-specific font.

[1461] The terminal previews and displays the generated New Year greeting card, and the user checks the content. If the user is satisfied with the document, the server generates the final New Year greeting card in PDF format and sends it to the terminal. The user downloads this and prints it for use as a New Year greeting card.

[1462] With this system, the user can generate a high-quality handwritten-style document that utilizes the user's own handwritten characters and style, while also appropriately reflecting emotions, saving the trouble of handwriting.

[1463] The flow of the specific process in Example 2 will be described using FIG. 13.

[1464] Step 1:

[1465] Input: The user uploads handwriting samples (scanned images, digital handwriting photographs) and text samples (text files) to the system.

[1466] Specific operation: The user uploads these samples through the system interface. For example, they provide the system with a scanned image of a handwritten New Year's card that reads "Happy New Year" and a corresponding text file.

[1467] Output: The uploaded sample data is saved on the server.

[1468] Step 2:

[1469] Input: The server converts handwritten samples into digital data using OCR technology.

[1470] How it works: The server analyzes the uploaded handwritten image and converts it into digital data, using, for example, Tesseract OCR. It extracts the outline and shape of each character.

[1471] Output: Digital data of handwritten characters is obtained.

[1472] Step 3:

[1473] Input: Digital data of handwritten characters.

[1474] What it does: The server runs an algorithm to generate a custom font for the user based on the extracted character contours and shapes.

[1475] Output: Font data is generated that reproduces the user's unique handwriting.

[1476] Step 4:

[1477] Input: A text file of user-supplied text.

[1478] What it does: The server uses NLP technology (e.g., "spaCy" or "NLTK") to analyze the text sample, extracting stylistic features, punctuation usage, sentence structure, etc.

[1479] Output: Data that has learned the user's unique writing style characteristics is obtained.

[1480] Step 5:

[1481] Input: Parsed text content data.

[1482] How it works: The emotion recognition engine built into the server analyzes emotional patterns. For example, to identify emotional states such as "positive" or "negative," the "Sentiment Analysis API" is used.

[1483] Output: The emotional information inherent in the text is analyzed, and data is generated to reflect this in the writing style.

[1484] Step 6:

[1485] Input: The user inputs the content of the document they want to generate into the system.

[1486] Specific operation: The user inputs the content of the "New Year's greeting message" through the interface, for example, "Happy New Year. I look forward to working with you again this year."

[1487] Output: The input data is sent to the server.

[1488] Step 7:

[1489] Input: Input content data, learned stylistic features, and sentiment information.

[1490] Specific operation: The server runs a text generation algorithm based on this data. The generated text reflects the user's writing style and emotions and is converted to look handwritten using a handwriting font.

[1491] Output: A handwritten document is generated and output as a preview image.

[1492] Step 8:

[1493] Input: The generated handwritten document.

[1494] Specific operation: The device displays a preview of the generated handwritten-style document to the user. The user can check the preview and edit it as needed, for example, by changing the content or style of the text.

[1495] Output: The corrected document data.

[1496] Step 9:

[1497] Input: The edited document data.

[1498] Specific operation: The server generates the final handwritten-style document in the specified format (e.g., PDF, JPEG), and sends the generated document to the device.

[1499] Output: The final handwritten document file is provided to the user.

[1500] Through these steps, the system can automatically generate high-quality handwritten documents that reflect the user's handwriting, writing style, and emotions.

[1501] (Application example 2)

[1502] 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."

[1503] In modern digital communication, there is a growing demand for personalized messages. However, generating handwritten-style messages is time-consuming and there are limited easy-to-use methods. Furthermore, it is difficult to generate documents that reflect the user's emotions, and they tend to be bland because they rely on a single template. Therefore, there is a need for a system that can easily generate personalized handwritten-style messages and provide high-quality documents that reflect the user's emotions.

[1504] 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 converting handwritten character samples provided by a user into digital data using optical character recognition technology; means for generating a user-specific font from the handwritten character digital data; means for analyzing the user-provided text sample and learning stylistic features using natural language processing technology; means for automatically generating handwritten-style text based on content specified by the user using the generated font and the learned stylistic features; means for analyzing the user's emotions based on the content of the generated text and reflecting them in the text; means for displaying the automatically generated handwritten-style text to the user and making it editable; and means for outputting the edited text in a specific format and transmitting it to a communication terminal. This makes it possible to easily generate personalized handwritten-style messages and provide high-quality documents that reflect the user's emotions.

[1505] A "handwriting sample" is an image or digital data of a user's own handwriting that the user provides to the system.

[1506] "Optical character recognition technology" is a technology that reads character information from an image and converts it into digital data.

[1507] "Digital data" refers to data that has been converted from analog information into digital form.

[1508] A "user-specific font" is a font that is generated to reproduce the characteristics of a user's handwriting.

[1509] "Sample text" is data of text written in the past that the user provides to the system.

[1510] "Stylistic features" are specific styles such as expression patterns, use of punctuation, and sentence structure in writing.

[1511] "Natural language processing technology" is a technology for understanding and analyzing human language.

[1512] "Handwritten text" is text in digital form that reproduces the style of a user's handwriting.

[1513] "Means for analyzing emotions and reflecting them in writing" refers to a method for analyzing the emotional state of a user from the text they have entered or from past writing, and adjusting the expression and writing style based on that.

[1514] The "means for enabling editing" is a function that allows the user to check the generated handwritten-style text and make corrections or changes as necessary.

[1515] "Means for outputting in a specific format and transmitting to a communication terminal" refers to a method for generating the final handwritten-style text in a specified format (such as PDF or JPEG) and transmitting it to the user's device.

[1516] MODE FOR CARRYING OUT THE INVENTION

[1517] The present invention is a system for generating personalized handwritten messages in a specific format and sending them to a user's communication device. The system is designed to utilize handwriting samples and sentence samples to generate a user's personalized font and writing style, and further analyze and reflect sentiment in the sentences. Specific examples are described below.

[1518] Program processing explanation

[1519] Hardware and software used

[1520] Hardware: Servers, user devices (PCs, smartphones, tablets)

[1521] Software: OCR technology (pytesseract), natural language processing technology (NLP tools), emotion engine (emotion_engine)

[1522] 1. Upload your handwritten sample:

[1523] Users upload their own handwritten character samples (images, digital data) and past writing samples (text files) to the system, which converts the samples into digital data using OCR technology (pytesseract), and extracts the outline and shape characteristics of the characters.

[1524] 2. Font generation:

[1525] The server uses the extracted character features to generate a user-specific font that faithfully reproduces the user's handwriting style.

[1526] 3. Analysis of stylistic features:

[1527] The server uses natural language processing technology to analyze user-provided text samples and learn stylistic features, identifying expression patterns and sentence structures within the text and defining the user's unique writing style.

[1528] 4. Emotion analysis:

[1529] The emotion engine analyzes the content of the user's text and recognizes emotions from it, allowing the user's emotional state to be reflected in the text.

[1530] 5. Message Creation:

[1531] The user inputs the message content they want to generate using the system interface. The input data is automatically generated as handwritten text based on the generated font and the learned stylistic features and emotional information.

[1532] 6. Preview and edit:

[1533] The server generates a preview image of the generated handwritten text and sends it to the user's terminal. The user can check the preview and edit it as necessary.

[1534] 7. Final output and transmission:

[1535] Once editing is complete, the server generates the final handwritten-style text in the specified format (PDF, JPEG) and sends it to the user's device, where the user can download it and use it as needed.

[1536] Specific examples

[1537] For example, if a user wants to purchase a special gift from a virtual store and want to include a handwritten message, the user can upload handwriting samples and past writings to the system, which will then generate a personalized message.

[1538] Prompt Sentence Examples

[1539] "Generate personalized handwritten messages using your handwriting and text samples."

[1540] Examples:

[1541] Handwritten character samples: "sample1.png", "sample2.png"

[1542] Text samples: "sample_text1.txt", "sample_text2.txt"

[1543] Message we want to generate: "Thank you for shopping with us! Your support means a lot to us."

[1544] This allows users to easily create personalized handwritten messages and provide a special customer experience in virtual stores.

[1545] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1546] Step 1:

[1547] (Sample upload)

[1548] Users upload sample images of their own handwritten characters and sample text files of past sentences to the system, which receives the images of handwritten characters and the text files of sentences as input and sends them to the server.

[1549] Input: Sample image of handwritten characters, sample text file

[1550] Output: Sample images of handwritten characters transferred to the server, sample text files

[1551] Step 2:

[1552] (OCR processing of handwritten characters)

[1553] The server converts sample images of handwritten characters into digital data using OCR technology (pytesseract), and then processes the images to recognize characters and extract their outlines and shapes.

[1554] Input: Sample image of handwritten characters

[1555] Data processing / data calculation: Character recognition and contour extraction using OCR

[1556] Output: Digital data of handwritten characters

[1557] Step 3:

[1558] (font generation)

[1559] The server generates a user-specific font based on the digital data of handwritten characters obtained through OCR processing. It creates a font file using the character feature information.

[1560] Input: Digital data of handwritten characters

[1561] Data processing / data calculation: character feature modeling and font file generation

[1562] Output: User-specific font file

[1563] Step 4:

[1564] (Analysis of stylistic features)

[1565] The server analyzes the sample text provided by the user using natural language processing technology (NLP tools) to learn stylistic features, extracting sentence structure and expression patterns, and creating a unique stylistic model for the user.

[1566] Input: Sample text file of sentences

[1567] Data processing / data calculation: Extraction and learning of stylistic features using NLP

[1568] Output: User-specific writing style model

[1569] Step 5:

[1570] (emotional analysis)

[1571] The emotion engine recognizes emotions from the content of the text provided by the user and applies algorithms to recommend appropriate writing styles and expressions based on that. It learns emotional patterns and reflects them in the generated text.

[1572] Input: Text content

[1573] Data processing / data calculation: Applying emotion recognition models and learning emotion patterns

[1574] Output: Sentiment analysis results and recommended writing style

[1575] Step 6:

[1576] (Message generation)

[1577] Based on the message content entered by the user into the system interface, the server automatically generates handwritten-style text using the generated font, learned stylistic features, and emotional information. Finally, the handwritten-style text is created using the digital font.

[1578] Input: Message content, user-specific font, writing style model, sentiment analysis results

[1579] Data processing / data calculation: style model, sentence generation based on emotion information, font application

[1580] Output: Handwritten message

[1581] Step 7:

[1582] (Preview and Edit)

[1583] The server generates a preview image of the handwritten text and sends it to the user's terminal. The user can check the preview and edit it as necessary.

[1584] Input: Handwritten message

[1585] Data processing / data calculation: Preview image generation

[1586] Output: Preview image

[1587] Step 8:

[1588] (Final output and transmission)

[1589] After the user has completed editing, the server generates the final handwritten-style text in the specified format (PDF, JPEG) and sends it to the user's device, where the user can download the file and use it.

[1590] Input: Edited handwritten message

[1591] Data processing / data calculation: Generate files in specified format

[1592] Output: Final PDF or JPEG file

[1593] These processing steps allow users to easily create personalized handwritten messages, providing high-quality documents that reflect their emotions.

[1594] 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.

[1595] 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.

[1596] 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.

[1597] 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.

[1598] 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.

[1599] 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.

[1600] 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).

[1601] 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.

[1602] 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."

[1603] 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.

[1604] 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).

[1605] 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.

[1606] 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.

[1607] 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.

[1608] 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.

[1609] 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.

[1610] 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.

[1611] 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.

[1612] 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.

[1613] 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, in order to avoid confusion and to 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.

[1614] 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.

[1615] The following is further disclosed regarding the above embodiment.

[1616] (Claim 1)

[1617] means for converting a user-provided handwriting sample into digital data using optical character recognition technology;

[1618] means for generating a user-specific font from the digital data of the handwritten characters;

[1619] means for using natural language processing techniques to analyze the user-provided writing samples and learn stylistic features;

[1620] means for automatically generating handwritten-style text from content designated by a user using the generated font and the learned style features;

[1621] means for displaying the automatically generated handwritten-style text to a user and allowing the user to edit the text;

[1622] means for outputting the edited text in a specified format;

[1623] A system including:

[1624] (Claim 2)

[1625] 2. The system according to claim 1, wherein the means for generating a font from the digital data of handwritten characters uses an algorithm for extracting the contour and shape characteristics of the characters.

[1626] (Claim 3)

[1627] 2. The system according to claim 1, wherein the means for using natural language processing technology includes an algorithm for identifying expression patterns based on the user's writing style and reflecting these in the generated text.

[1628] "Example 1"

[1629] (Claim 1)

[1630] means for converting a user-provided handwriting sample into digital data using optical character recognition technology;

[1631] means for generating a user-specific font from the digital data of the handwritten characters;

[1632] means for using natural language processing techniques to analyze the user-provided writing samples and learn stylistic features;

[1633] means for automatically generating handwritten-style text from content designated by a user using the generated font and the learned style features;

[1634] means for the user to input a generation request into an interface via a terminal;

[1635] means for displaying the automatically generated handwritten-style text to a user and allowing the user to edit the text;

[1636] means for outputting the edited text in a specified format;

[1637] A system including:

[1638] (Claim 2)

[1639] 2. The system according to claim 1, wherein the means for generating a font from the digital data of handwritten characters uses an algorithm for extracting the contour and shape characteristics of the characters.

[1640] (Claim 3)

[1641] 2. The system according to claim 1, wherein the means for using natural language processing technology includes an algorithm for identifying expression patterns based on the user's writing style and reflecting these in the generated text.

[1642] "Application Example 1"

[1643] (Claim 1)

[1644] means for converting a user-provided handwriting sample into digital data using optical character recognition technology;

[1645] means for generating a user-specific font from the digital data of the handwritten characters;

[1646] means for using natural language processing techniques to analyze the user-provided writing samples and learn stylistic features;

[1647] means for automatically generating handwritten-style text from content designated by a user using the generated font and the learned style features;

[1648] means for displaying the automatically generated handwritten-style text to a user and allowing the user to edit the text;

[1649] A means for generating a handwritten message to be attached to the product and synthesizing it with the product image;

[1650] means for outputting the edited text in a specified format;

[1651] A system including:

[1652] (Claim 2)

[1653] 2. The system according to claim 1, wherein the means for generating a font from the digital data of handwritten characters uses an algorithm for extracting the contour and shape characteristics of the characters.

[1654] (Claim 3)

[1655] 2. The system according to claim 1, wherein the means for using natural language processing technology includes an algorithm for identifying expression patterns based on the user's writing style and reflecting these in the generated text.

[1656] "Example 2: Combining Emotion Engines"

[1657] (Claim 1)

[1658] means for converting a user-provided handwriting sample into digital data using optical character recognition technology;

[1659] means for generating a user-specific font from the digital data of the handwritten characters;

[1660] means for using natural language processing techniques to analyze the user-provided writing samples and learn stylistic features;

[1661] means for automatically generating handwritten-style text from content designated by a user using the generated font and the learned style features;

[1662] means for displaying the automatically generated handwritten-style text to a user and allowing the user to edit the text;

[1663] means for outputting the edited text in a specified format;

[1664] means for analyzing the content of the text input by the user, recognizing emotions, and reflecting the emotions in document generation;

[1665] A system including:

[1666] (Claim 2)

[1667] 2. The system according to claim 1, wherein the means for generating a font from the digital data of handwritten characters uses an algorithm for extracting the contour and shape characteristics of the characters.

[1668] (Claim 3)

[1669] 2. The system according to claim 1, wherein the means for using natural language processing technology includes an algorithm for identifying expression patterns based on the user's writing style and reflecting these in the generated text.

[1670] "Application example 2 when combining emotion engines"

[1671] (Claim 1)

[1672] means for converting a user-provided handwriting sample into digital data using optical character recognition technology;

[1673] means for generating a user-specific font from the digital data of the handwritten characters;

[1674] means for using natural language processing techniques to analyze the user-provided writing samples and learn stylistic features;

[1675] means for automatically generating handwritten-style text from content designated by a user using the generated font and the learned style features;

[1676] means for analyzing a user's feelings based on the content of the generated sentence and reflecting the feelings in the sentence;

[1677] means for displaying the automatically generated handwritten-style text to a user and allowing the user to edit the text;

[1678] means for outputting the edited text in a specific format and transmitting it to a communication terminal;

[1679] A system including:

[1680] (Claim 2)

[1681] 2. The system according to claim 1, wherein the means for generating a font from the digital data of handwritten characters uses an algorithm for extracting the contour and shape characteristics of the characters.

[1682] (Claim 3)

[1683] 2. The system according to claim 1, wherein the means for using natural language processing technology includes an algorithm for identifying expression patterns based on the user's writing style and reflecting these in the generated text. [Explanation of symbols]

[1684] 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. means for converting a user-provided handwriting sample into digital data using optical character recognition technology; means for generating a user-specific font from the digital data of the handwritten characters; means for using natural language processing techniques to analyze the user-provided writing samples and learn stylistic features; means for automatically generating handwritten-style text from content designated by a user using the generated font and the learned style features; means for displaying the automatically generated handwritten-style text to a user and allowing the user to edit the text; means for outputting the edited text in a specified format; A system including:

2. 2. The system according to claim 1, wherein the means for generating a font from the digital data of handwritten characters uses an algorithm for extracting the contour and shape characteristics of the characters.

3. 2. The system according to claim 1, wherein the means for using natural language processing technology includes an algorithm for identifying expression patterns based on the user's writing style and reflecting these in the generated text.

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