System

Abstract

A system is provided.SOLUTION: The system includes an input means for a user to input a handwritten character, an image analysis means for analyzing the input handwritten character and extracting a feature, a selection means for the user to select an existing font, a generation means for generating a new font by combining the extracted feature and the selected font, and a provision means for providing the generated font to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In today's digital age, individuals and businesses seek unique fonts as a form of self-expression. However, converting one's own handwritten characters into beautiful digital fonts requires advanced technical skills, time, and cost. Furthermore, many users find their handwritten characters untidy and are reluctant to use them as fonts. To address these challenges, a system is needed that allows users to easily generate original fonts using their own handwritten characters. [Means for solving the problem]

[0005] This invention solves this problem with a system that includes an input means for a user to input handwritten characters, an image analysis means for analyzing the input handwritten characters and extracting their characteristics, a selection means for the user to select an existing font, a generation means for combining the extracted characteristics with the selected font to generate a new font, and a provision means for providing the generated font to the user. This allows users to easily create and use original fonts that are more beautifully shaped while preserving the unique characteristics of their handwritten characters. By utilizing optical character recognition technology and deep learning technology, this system can recognize the user's handwritten characters with high accuracy and adjust them to the selected font style. The generated font is provided in a standard font file format and can be used on a variety of digital platforms.

[0006] An "input means" is a device or method by which a user captures handwritten characters into a digital device.

[0007] "Image analysis means" refers to a technique or device that analyzes an image of handwritten characters captured by an input means and extracts the shape and characteristics of the characters.

[0008] A "selector" is an interface or device that allows a user to select an existing font style.

[0009] A "generator" is a technique or device that combines the analyzed characteristics of handwritten characters with the properties of a selected existing font to create a new font.

[0010] "Providing means" refers to the technology or method for providing the generated original font in a form that can be used by users. [Brief explanation of the drawings]

[0011] [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

[0012] 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.

[0013] First, the terms used in the following description will be explained.

[0014] 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).

[0015] 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.

[0016] 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.

[0017] 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.

[0018] 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."

[0019] [First embodiment]

[0020] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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."

[0032] A specific embodiment of the present invention will be described. This invention is a system for generating original fonts using characters handwritten by a user. The processing of the program will be explained in natural language below, with specific examples included.

[0033] System Program Processing

[0034] Handwritten character input

[0035] A user writes their own handwritten text on paper and takes a photo of it with the camera on their smartphone or tablet, or uses a scanner to digitize the text they have written on paper. This digitized image file is then uploaded to the system. For example, a user can write the word "hello" by hand and upload the image to the system.

[0036] Image analysis

[0037] The device sends the uploaded image file of the handwritten characters to the server. The server then uses image recognition technology to analyze the characteristics of the handwritten characters. In this process, OCR (optical character recognition) technology is used to extract the shape and characteristics of each character. For example, the softness of the curve of the character "ko" or the shape of the double curve of the character "n" are analyzed and the characteristics are obtained as data.

[0038] Font selection

[0039] The user selects the desired font from a list of existing font styles provided within the system, including serif, sans serif, handwritten fonts, etc. This information is sent from the terminal to the server.

[0040] Generating a new font

[0041] The server uses AI technology to combine the analyzed handwritten character features with the characteristics of the template font selected by the user. Specifically, it uses a deep learning model to convert the handwritten character features to match the style of the template font. For example, it preserves the soft curves of the user's handwritten characters while adjusting them to the straight lines and curves characteristic of the selected font. A generator defines the shape of each character and creates a path (drawing path) for the new font.

[0042] Font provision

[0043] The server generates a font file based on the path information of the new font. This file is created in a standard font format (e.g., .ttf, .otf). The server provides the generated font file to the user as a download link. The user can download the font file from this link and use it on various digital platforms and word processing software. For example, when typing "hello" using the generated original font, it will be displayed in a font that reflects the user's unique handwritten style.

[0044] In this way, users can easily create and use original fonts that take advantage of the characteristics of their own handwritten characters. This system is an effective means of creating high-quality digital fonts that meet the needs of individual users.

[0045] The processing flow will be explained below.

[0046] Step 1:

[0047] Users write their own handwriting on paper, take a photo of it with their smartphone or tablet camera, or use a scanner to digitize the writing on paper, and then upload this digitized image file to the system.

[0048] Step 2:

[0049] The terminal transmits the image file of the handwritten characters uploaded by the user to the server.

[0050] Step 3:

[0051] The server then passes the received image file to the image analysis module, which uses OCR (optical character recognition) technology to extract the shape and characteristics of the handwritten characters. For example, it obtains data on the softness of the curves in the character "ko" and the connection between the dots in the character "n."

[0052] Step 4:

[0053] The user selects the desired font style from a list of existing font styles displayed in the system. The choices include serif, sans serif, handwritten fonts, etc. This selection information is sent from the terminal to the server.

[0054] Step 5:

[0055] The server uses AI technology to combine the analyzed handwritten character characteristics with the characteristics of the template font selected by the user. Using a deep learning model, the server converts the handwritten character characteristics to match the style of the template font. Specifically, it adjusts the shape to match the consistency of the existing font while preserving the soft curves and unique line expression of the handwritten character.

[0056] Step 6:

[0057] The server defines the path (drawing path) for each character based on the adjusted features and Bézier curves. A font file is generated based on this path information. The font file is created in accordance with standard font formats (e.g., .ttf, .otf).

[0058] Step 7:

[0059] The server uploads the generated original font file to the user's download page.

[0060] Step 8:

[0061] Users click the download link to download the original font file, which they can then install on their PC or device.

[0062] Step 9:

[0063] Users can use the installed original fonts for document creation, graphic design, etc. The generated fonts are displayed in a beautiful and neat manner while retaining the individuality of the user's handwritten characters.

[0064] Example 1

[0065] 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."

[0066] Conventional font generation systems do not allow users to easily generate their own handwritten characters as digital fonts and use them individually. Furthermore, there is a lack of technology that faithfully reproduces the individual characteristics of handwritten characters while combining them with existing template fonts. This makes it difficult for users to digitize their own handwritten characters and use them across various platforms.

[0067] 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.

[0068] In this invention, the server includes means for uploading input handwritten characters to the system as an image file, means for transmitting the image file to the server, image analysis means for analyzing the input handwritten characters and extracting their characteristics, selection means for the user to select an existing font, generation means for generating a new font by combining the extracted characteristics with the selected font, and provision means for providing the generated font to the user. This enables users to easily generate original fonts that make use of the characteristics of their own handwritten characters and use them on various digital platforms and word processing software.

[0069] "Input means" refers to a means by which a user inputs handwritten characters and inputs them into the system in digital form.

[0070] "Uploading means" refers to a process or interface for a user to upload an image file of handwritten characters that has been photographed or digitized by the user to the system.

[0071] The "transmission means" is a means for transmitting the uploaded image file from the terminal to the server.

[0072] "Image analysis means" refers to the technology and algorithms used to analyze and extract features from input images of handwritten characters.

[0073] The "selection means" is a means for the user to select a desired font from a list of existing font styles provided within the system.

[0074] The "generation means" is a means for generating a new font based on the extracted handwritten character characteristics and the selected font style information.

[0075] "Providing means" refers to a method or interface for providing the generated new font to the user.

[0076] "Optical character recognition technology" is a technology that recognizes characters from scanned digital images of printed or handwritten characters and converts them into digital text.

[0077] "Deep learning technology" is a machine learning technology that uses multi-layered neural networks to perform pattern recognition and generation tasks.

[0078] This invention is a system that allows a user to create an original font using his / her own handwritten characters. This system is specifically implemented using the following hardware and software.

[0079] A user writes a letter by hand on a piece of paper, takes a photo of it with a smartphone or tablet camera, or uses a scanner to digitize it, and then uploads the digital image file of the handwritten letter to the system. For example, a user writes the word "hello" by hand and uploads the image to the system.

[0080] The device sends the uploaded image file of the handwritten characters to a server. The server then uses OCR (optical character recognition) technology to analyze the characteristics of the handwritten characters. This process uses software like Tesseract OCR to extract the shape and characteristics of each character. For example, the softness of the curve of the character "ko" and the shape of the double curve of the character "n" are analyzed and the characteristics are obtained as data.

[0081] The user selects a desired font, such as serif, sans serif, or handwritten font, from a list of existing font styles provided within the system. The selected font style information is sent from the terminal to the server.

[0082] The server uses AI technology to combine the analyzed handwritten character features with the characteristics of the template font selected by the user. Specifically, it uses deep learning models (e.g., GANs and Variational Autoencoders) to convert the handwritten character features to match the style of the template font. For example, it uses a method to preserve the soft curves of the user's handwritten characters while adjusting them to the straight lines and curves characteristic of the selected font. A generator defines the shape of each character and creates a path (drawing path) for the new font.

[0083] The server generates a font file based on the path information of the new font. This file is created in a standard font format (e.g., .ttf, .otf). The server provides the generated font file as a download link that users can access. Users can download the font file from this link and use it on various digital platforms and word processing software. For example, when you type "hello" using the generated original font, it will be displayed in a font that reflects the user's unique handwritten style.

[0084] As a concrete example, consider a case where a user writes "Thank you" by hand on a piece of paper, takes a photo of it with their smartphone, and uploads it to the system. The user selects a handwritten font, and once the new font is generated, the server provides a download link. The user downloads the font from the link, and when they type "Thank you" into a document, the original handwritten style is reflected.

[0085] An example of a prompt would be:

[0086] Upload your handwritten "Thank you" text, select a handwritten font, generate a new font file, and provide the download link for this font file.

[0087] This system allows users to easily create and use original fonts that take advantage of the characteristics of their handwritten characters.

[0088] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0089] Step 1:

[0090] Users can write by hand on paper and then take a photo of it with their smartphone or tablet camera, or use a scanner to digitize the text they have written on paper.

[0091] Input: Image file of handwritten characters

[0092] Output: Digital image file

[0093] What it does: A user writes the word "hello" by hand, takes a photo or scans the image, and then uploads the digital image file to the system.

[0094] Step 2:

[0095] The terminal transmits the digital image files uploaded by the user to the server.

[0096] Input: User-uploaded digital image files

[0097] Output: Digital image files sent to the server

[0098] Specific operation: The terminal automatically transmits the digital image file to the server through the upload form specified by the user.

[0099] Step 3:

[0100] The server receives the transmitted image file and uses OCR (optical character recognition) technology to analyze the handwritten characters and extract their features.

[0101] Input: Digital image files received by the server

[0102] Output: Analyzed character feature data

[0103] How it works: The server begins analyzing the image using software such as Tesseract OCR, analyzing the shape and characteristics of each character and extracting data such as the softness of the curve of "ko" and the shape of the double curve of "n."

[0104] Step 4:

[0105] The user selects the desired font from a list of existing font styles provided within the system.

[0106] Input: Selection information from the font style list

[0107] Output: User selected font information

[0108] Specific operation: The user uses the system interface to select the desired font, such as serif, sans serif, or handwritten font.

[0109] Step 5:

[0110] The terminal transmits the user's selection information to the server.

[0111] Input: User selected font information

[0112] Output: Font information sent to the server

[0113] Specific operation: The terminal automatically sends information about the font selected by the user to the server.

[0114] Step 6:

[0115] The server uses a deep learning model to generate a new font based on the analyzed handwritten character feature data and the font information selected by the user.

[0116] Input: Handwritten character feature data and user font selection information

[0117] Output: New font path information

[0118] How it works: The server uses deep learning techniques (e.g., GANs and Variational Autoencoders) to analyze the characteristics of handwritten characters and adjust them to match the style of the selected font. This process generates a new font that preserves the softness and curves of the handwritten characters.

[0119] Step 7:

[0120] The server creates a font file based on the path information of the generated font.

[0121] Input: Path information for the new font

[0122] Output: Generated font file (.ttf or .otf format)

[0123] Specific operation: The server uses a font generation library (e.g. FontForge) to create a font file based on the path information of the generated font.

[0124] Step 8:

[0125] The server generates a download link for providing the font file to the user and notifies the user.

[0126] Input: Generated font file

[0127] Output: Download link

[0128] Specific operation: The server stores the generated font file and notifies the user of the download link, through which the user can download the font file.

[0129] (Application example 1)

[0130] 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."

[0131] While conventional font generation systems can digitize a user's handwritten text and use it as a font, they lack the ability to replace and display text in real time. This makes it difficult for users to effectively use their handwritten text in advertisements and other visual content. Furthermore, there is a need for a system that can instantly apply a user's handwritten text style to enhance the individuality of advertisement content and visuals, providing a more personalized experience.

[0132] 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.

[0133] In this invention, the server includes an input means for a user to input handwritten characters, an image analysis means for analyzing the input handwritten characters and extracting features, a selection means for the user to select an existing font, a generation means for generating a new font by combining the extracted features with the selected font, a provision means for providing the generated font to the user, and a display means for replacing recognized characters with the user's handwritten font and displaying it in real time. This makes it possible to display an original font generated by a user using handwritten characters in real time and effectively utilize it in advertisements and other visual content.

[0134] "Input means" refers to a device or interface that allows a user to input handwritten characters into the system.

[0135] "Image analysis means" refers to the technology or device used to analyze input handwritten characters and extract their characteristics.

[0136] "Selection means" refers to a device or interface that allows a user to select an existing font from within the system.

[0137] "Generator" refers to the technology or device that combines the extracted features with the selected font to generate a new font.

[0138] "Providing means" refers to a device or interface for providing the generated font to the user.

[0139] The "display means" refers to a device or interface for converting the recognized characters into the user's handwritten font and displaying them in real time.

[0140] The present invention provides a system that allows users to generate original fonts using their own handwritten characters and apply them to advertisements and visual content in real time. Detailed embodiments of this system will be described below.

[0141] First, a user writes their own handwritten characters on a piece of paper and takes a photo of it with the camera on their smartphone or tablet. Alternatively, they can use a scanner to digitize the characters they have written on paper. This digitized image file is then uploaded to the server. For example, a user can write the word "hello" by hand and upload the image to the server.

[0142] The server receives the uploaded image file of the handwritten characters and uses image recognition technology to analyze the characteristics of the handwritten characters. In this process, OCR (Optical Character Recognition) technology is used to extract the shape and characteristics of each character. For example, the softness of the curve of the character "ko" or the shape of the double curve of the character "n" are analyzed and the characteristics are obtained as data.

[0143] The user then selects the desired font from a list of existing font styles provided within the system, including serif, sans serif, handwritten fonts, etc. This information is sent to the server.

[0144] The server then uses AI technology to combine the analyzed handwritten character characteristics with the characteristics of the template font selected by the user. Specifically, it uses a deep learning model to convert the handwritten character characteristics to match the style of the template font. For example, it preserves the soft curves of the user's handwritten character while adjusting them to the straight lines and curves characteristic of the selected font. A path (drawing path) for the generated new font is then created.

[0145] The new font is displayed in real time. Through a display device such as smart glasses, users can capture text on billboards or posters with their camera, and the recognized text is then replaced with the user's handwritten font. This allows users to apply their own handwritten style to advertisements, providing a personalized experience.

[0146] For example, when a user sees an advertising billboard at an event venue, the system can capture and analyze it with a camera and instantly display it in the user's handwritten font. An example of a prompt would be "Please display the following advertising text in my original font: Advertising text," and the system would follow the prompt and display it in the handwritten font.

[0147] This allows users to personalize advertisements and other visual content using their own handwritten characters, enhancing their visual individuality.The system is realized using hardware such as smart glasses (e.g., Google Glass), smartphones, and servers, and software such as Python, OpenCV, PIL, and pytesseract.

[0148] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0149] Step 1:

[0150] The user writes handwritten text on paper. The user takes a photo of their handwritten text with a smartphone or tablet camera or digitizes it using a scanner. The input is the written text on paper, and the output is a digital image file (e.g., JPEG, PNG).

[0151] Step 2:

[0152] The user uploads an image file of digitized handwritten characters to the server. The device (smartphone or tablet) provides an upload function and transfers the user's image file to the server. The input is a digital image file, and the output is image data on the server.

[0153] Step 3:

[0154] The server receives the uploaded image and uses image analysis to extract the characteristics of the handwritten characters. Specifically, it uses OCR (Optical Character Recognition) technology to detect the characters and obtain data on the shape and characteristics of each character (e.g., softness of curves, angle, line thickness). The input is a digital image file, and the output is character feature data.

[0155] Step 4:

[0156] The user selects the desired font from a list of font styles provided by the server. The terminal provides a font selection interface and sends the user's selection information to the server. The input is the user's selection action, and the output is the selected font information.

[0157] Step 5:

[0158] The server combines the extracted handwritten character features with the font style selected by the user to generate a new original font. A deep learning model is used to convert the handwritten character features to match the existing font style. The input is the feature data and the selected font information, and the output is the path information for the new font.

[0159] Step 6:

[0160] The server provides the generated font file (e.g., .ttf, .otf) to the user. As a means of providing the file, it generates a download link and notifies the user. The input is the path information of the font, and the output is the download link for the font file.

[0161] Step 7:

[0162] A user wears smart glasses and looks at an advertising billboard or poster in the city or at an event venue. The camera in the smart glasses captures the text of the advertisement and sends it to the server. The input is an image of the advertisement, and the output is the image data transferred to the server.

[0163] Step 8:

[0164] The server receives the captured image and replaces the recognized characters with the user's handwritten font. It analyzes the characters using image analysis means, applies the generated original font, and displays it in real time. The input is the advertisement image and the original font file, and the output is image data of the replaced characters.

[0165] Step 9:

[0166] The smart glasses display the replaced text to the user in real time, allowing the user to visually confirm that their own handwritten style is reflected in the advertisement. The input is image data of the replaced text, and the output is the text displayed on the smart glasses screen.

[0167] 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.

[0168] A specific embodiment of the present invention will be described. This invention combines a system that allows users to generate original fonts using their own handwritten characters with an emotion engine that recognizes the user's emotions. The processing of this program will be explained in natural language below, along with specific examples.

[0169] System Program Processing

[0170] Handwritten character input

[0171] A user writes their own handwritten text on paper and takes a photo of it with the camera on their smartphone or tablet, or uses a scanner to digitize the text they have written on paper. This digitized image file is then uploaded to the system. For example, a user can write "hello" and upload the image to the system.

[0172] Image analysis

[0173] The device sends the uploaded image file of the handwritten characters to the server. The server then passes the received image file to the image analysis module. This module uses OCR (optical character recognition) technology to extract the shape and characteristics of the handwritten characters. For example, it analyzes the softness of the curves in the character "ko" and the shape of the double curve in the character "n" and obtains the characteristics as data.

[0174] Font selection

[0175] The user selects the desired font from a list of existing font styles provided within the system, including serif, sans serif, handwritten fonts, etc. This information is sent from the terminal to the server.

[0176] Emotion recognition

[0177] The device captures or records the user's facial expressions and voice, and sends them to an emotion recognition engine. The emotion engine analyzes this data and identifies the user's current emotion. For example, if the user is smiling, it will be identified as "joy" or "enjoyment," and if the user has no expression, it will be identified as "neutral."

[0178] Generating a new font

[0179] The server uses AI technology to combine the analyzed handwritten character characteristics, the characteristics of the template font selected by the user, and the recognized emotion information. Using a deep learning model, the server converts the handwritten character characteristics to match the style of the template font. Furthermore, the server adjusts the font color and style (e.g., whether to use rounded, soft shapes or sharp shapes) depending on the emotion. For example, if the user is expressing the emotion "joy," the server adds bright colors and soft curves to the font.

[0180] Font provision

[0181] The server defines the path (drawing path) for each character based on the adjusted features and Bézier curves and generates a font file. This file is created in a standard font format (e.g., .ttf, .otf). The server provides the generated font file to the user as a download link. The user can download the font file from this link and use it on various digital platforms and word processing software.

[0182] example

[0183] For example, a user can handwrite "hello" and upload the image along with a photo of their own smiling face to the system. The server analyzes the characteristics of the handwritten characters and recognizes the emotion of "joy" from the smile. If the selected template font is a serif font, the server generates a font that preserves the soft curves of the handwritten characters while adding bright, cheerful colors. Users can download this original font and use it in their individual designs and writing.

[0184] This allows users to easily create and use original fonts that are more personalized based on their own handwritten characters and emotions.

[0185] The processing flow will be explained below.

[0186] Step 1:

[0187] Users write their own handwritten characters on paper, take a photo of it with the camera on their smartphone or tablet, or use a scanner to digitize the characters they have written on paper, and then upload this digitized image file to the system.

[0188] Step 2:

[0189] The terminal transmits the image file of the handwritten characters uploaded by the user to the server.

[0190] Step 3:

[0191] The server then passes the received image file to the image analysis module, which uses OCR (optical character recognition) technology to extract the shape and characteristics of the handwritten characters from the image file. Specifically, it obtains the outlines and curves of the characters as data.

[0192] Step 4:

[0193] The user selects the desired font from a list of existing font styles displayed in the system. For example, they can select serif, sans serif, handwritten font, etc. This selection information is sent from the terminal to the server.

[0194] Step 5:

[0195] The device captures the user's facial expressions and voice and sends them to the emotion engine on the server. For example, the device can capture the user's facial expressions with a smartphone camera and send them as they are.

[0196] Step 6:

[0197] The server's emotion engine analyzes the received facial and voice data to identify the user's emotion. For example, it recognizes the user's smile as the emotion of "joy."

[0198] Step 7:

[0199] The server generates a new font by combining the characteristics of handwritten characters and the selected font based on the emotion information obtained from the emotion engine. Using a deep learning model, the server preserves the characteristics of handwritten characters and adds design elements (e.g., color and style changes) according to the emotion.

[0200] Step 8:

[0201] The server defines the path (drawing path) for each character based on the adjusted features and Bézier curves, and generates a font file in a standard font format (e.g., .ttf, .otf).

[0202] Step 9:

[0203] The server will upload the generated original font file to the user's download page, where users can download the font file and install it on their PC or device.

[0204] Step 10:

[0205] Users can use the installed original fonts for document creation, graphic design, etc. For example, when they type "hello" using the generated font, it will be displayed in a unique font that reflects the user's handwriting and emotions.

[0206] Example 2

[0207] 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."

[0208] In conventional font generation systems using handwritten characters, it has been difficult to generate font styles that are linked to the user's emotions. In particular, it has been a technical challenge to automatically generate a font that matches the user's emotions while preserving the unique characteristics of handwritten characters. Furthermore, there has been no system that can recognize the user's emotions from their facial expressions or voice and reflect them in the font generation. This has resulted in the problem that users cannot easily generate more personalized original fonts.

[0209] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0210] In this invention, the server includes input means for a user to input handwritten characters, image analysis means for analyzing the input handwritten characters and extracting features, selection means for the user to select an existing font, emotion recognition means for identifying the user's emotion, generation means for generating a new font by combining the extracted features, the selected font, and the identified emotion information, and provision means for providing the generated font to the user. This makes it possible to automatically generate a font style corresponding to the user's emotion, allowing the user to easily create and use a more personalized original font linked to their emotion.

[0211] An "input means" is a means by which a user writes handwritten characters on paper and photographs them with the camera of a smartphone or tablet, or uses a scanner to upload the digitized image file to the system.

[0212] The "image analysis means" is a means for analyzing an uploaded image file of handwritten characters and extracting the shape and characteristics of the handwritten characters using OCR (optical character recognition) technology.

[0213] The "selection means" is a means by which a user selects a desired font from a list of existing font styles provided in the system and transmits the information to the system.

[0214] The "emotion recognition means" is a means for capturing the user's facial expressions and voice using a camera or microphone, analyzing the data, and identifying the user's current emotions.

[0215] The "generation means" is a means for generating a new font by combining the analyzed handwritten character characteristics, the selected font, and the identified emotional information using AI technology.

[0216] The "means for providing" is a means for generating a font file from the generated new font and providing the file to the user.

[0217] A specific embodiment of the present invention will be described. This invention combines a system that allows a user to generate an original font using their own handwritten characters with an emotion engine that recognizes the user's emotions.

[0218] System Configuration

[0219] This system is realized mainly by using the following hardware and software.

[0220] 1. Input method: The user inputs an image of handwritten characters using a device such as a smartphone, tablet, or scanner.

[0221] 2. Image analysis means: A server including an image analysis module using OCR (Optical Character Recognition) technology is used.

[0222] 3. Selection: The user selects an existing font using a web application or a dedicated system app.

[0223] 4. Emotion recognition means: using face recognition and voice analysis engines, for example devices with cameras and microphones.

[0224] 5. Generation method: A server that uses deep learning technology to generate new fonts.

[0225] 6. Provisioning means: A server that generates a download link to provide the generated font file to the user.

[0226] System Operation

[0227] Handwritten character input

[0228] A user writes their own handwritten characters on paper and takes a picture of the character using the camera on their smartphone or tablet. Alternatively, they can use a scanner to digitize the characters they have written on paper. This digitized image file is then uploaded to the system. Specifically, a user writes the word "hello" and uploads the image to the system.

[0229] Image analysis

[0230] The device sends the uploaded image file of the handwritten characters to the server. The server then passes the received image file to the image analysis module. This module uses OCR technology to extract the shape and characteristics of the handwritten characters. For example, it analyzes the softness of the curves in the character "ko" and the shape of the double curve in the character "n" and obtains the characteristics as data.

[0231] Font selection

[0232] The user selects the desired font from a list of existing font styles provided within the system, including serif, sans serif, handwritten fonts, etc. This information is sent from the terminal to the server.

[0233] Emotion recognition

[0234] The device captures or records the user's facial expressions and voice, and sends them to an emotion recognition engine. The emotion engine analyzes this data and identifies the user's current emotion. For example, if the user is smiling, it will be identified as "joy" or "enjoyment," and if the user has no expression, it will be identified as "neutral."

[0235] Generating a new font

[0236] The server uses AI technology to combine the analyzed handwritten character characteristics, the characteristics of the template font selected by the user, and the recognized emotion information. Using a deep learning model, the server converts the handwritten character characteristics to match the style of the template font. It also adjusts the font color and style according to the emotion. For example, if the user is expressing the emotion "joy," it adds bright colors and soft curves to the font.

[0237] Font provision

[0238] The server defines the path of each character based on the adjusted features and Bézier curves, and generates a font file. This file is created in a standard font format (e.g., .ttf, .otf). The server then provides the generated font file to the user as a download link. The user can download the font file from this link and use it on various digital platforms and word processing software.

[0239] Examples and prompts

[0240] For example, a user can handwrite "hello" and upload the image along with a photo of their smiling face to the system. The server analyzes the characteristics of the handwritten characters and recognizes the emotion of "joy" from the smile. If the selected template font is a serif font, the server generates a font that preserves the soft curves of the handwritten characters while adding bright, fun colors. Users can download this original font and use it in their own designs and writing.

[0241] An example of a prompt to input to a generative AI model is, "Please explain the process by which a user uploads an image of handwritten characters and emotional information to the system and generates an original font."

[0242] This allows users to easily create and use original fonts that are more personalized based on their own handwritten characters and emotions.

[0243] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0244] Step 1:

[0245] Users can write their own handwritten characters on paper and take a photo of it with their smartphone or tablet camera, or use a scanner to digitize the characters.

[0246] Input: Handwritten text on paper

[0247] Output: Digitized image files (e.g. JPEG, PNG)

[0248] What happens: The user writes "Hello" on a piece of paper and takes a photo of it with their smartphone camera.

[0249] Step 2:

[0250] The user uploads a photographed or scanned image file to the system.

[0251] Input: Digitized image file of handwritten characters

[0252] Output: Image file uploaded to the system

[0253] Specific operation: The user opens the system's upload screen, selects the "Hello" image file, and uploads it.

[0254] Step 3:

[0255] The terminal transmits the uploaded image file of the handwritten characters to the server.

[0256] Input: User uploaded image file

[0257] Output: Image file sent to the server

[0258] Specific operation: The device sends the "Hello" image file uploaded by the user to the server via Wi-Fi or mobile network.

[0259] Step 4:

[0260] The server passes the received image file to the image analysis module.

[0261] Input: Uploaded image file of handwritten characters

[0262] Output: Input data to the image analysis module

[0263] Specific operation: The server receives the image file and saves it in a directory for image analysis.

[0264] Step 5:

[0265] The server uses OCR technology to extract the shape and characteristics of handwritten characters.

[0266] Input: Image file of handwritten characters passed to the image analysis module

[0267] Output: Extracted character shapes and feature data

[0268] Specific operation: The server's OCR engine analyzes the image file and obtains data such as the softness of the curve of "ko" and the shape of the double curve of "n."

[0269] Step 6:

[0270] The user selects the desired font from a list of existing font styles provided within the system.

[0271] Input: List of font styles

[0272] Output: Selected font style data

[0273] What happens: The user selects a serif font from the system font selection screen.

[0274] Step 7:

[0275] The terminal transmits the font information selected by the user to the server.

[0276] Input: Selected font style data

[0277] Output: Font style data sent to the server

[0278] Specific operation: The device sends the serif font information selected by the user to the server.

[0279] Step 8:

[0280] The device captures or records the user's facial expressions and voice and sends them to an emotion recognition engine.

[0281] Input: User's facial image and voice data

[0282] Output: Input data to the emotion recognition engine

[0283] Specific operation: The device's camera captures the user's smile and the microphone records their voice.

[0284] Step 9:

[0285] The server's emotion recognition engine analyzes this data and identifies the user's current emotion.

[0286] Input: facial expression images and audio data

[0287] Output: Identified emotion information (e.g., joy, pleasure, neutral)

[0288] Specific operation: The emotion recognition engine analyzes smile image data and identifies the emotion of "happiness."

[0289] Step 10:

[0290] The server combines the analyzed handwritten character features, the selected font characteristics, and the recognized emotion information using AI technology.

[0291] Input: Handwritten character feature data, font style data, emotion information

[0292] Output: Data needed to generate a new font

[0293] What it does: Uses a deep learning model to adjust the softness of handwritten characters to match the characteristics of serif fonts.

[0294] Step 11:

[0295] The server adjusts the font color and style depending on the emotion.

[0296] Input: Emotion information, font generation data

[0297] Output: Emotion-aware font styles

[0298] What it does: Add bright colors and soft curves to fonts based on the emotion of "fun."

[0299] Step 12:

[0300] The server defines the path of each character based on the adjusted features and Bezier curves, and generates a font file.

[0301] Input: Adjusted character feature data, Bezier curve data

[0302] Output: Font file (e.g. .ttf, .otf)

[0303] What it does: It uses Bezier curves to define the path of each character and generates a font file.

[0304] Step 13:

[0305] The server provides the generated font file to the user as a download link.

[0306] Input: Generated font file

[0307] Output: Download link

[0308] What happens: The server uploads the font file to cloud storage and provides the link to the user.

[0309] (Application example 2)

[0310] 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."

[0311] Conventional font generation systems generate original fonts by combining the user's handwritten characters with template fonts. However, these systems do not take the user's emotions into account, which limits the user experience and makes it difficult to generate fonts that respond to individual emotions and situations. In addition, there is a lack of systems in physical stores that can provide information that responds to the individual needs and emotions of customers.

[0312] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0313] In this invention, the server includes an input means for a user to input handwritten characters, an image analysis means for analyzing the input handwritten characters and extracting features, a selection means for the user to select an existing font, an emotion recognition means for recognizing the user's emotion, a generation means for generating a new font by combining the extracted features, the selected font, and the recognized emotion, and a provision means for providing the generated font to the user. This allows a personalized font to be generated according to the customer's handwritten characters and emotions, making it possible to provide individual information in real time in a physical store.

[0314] A "user" is an individual or group who uses the system to input handwritten characters and generate an original font.

[0315] "Input means" refers to a device or interface that allows a user to digitize handwritten characters and provide them to the system. Examples include smartphones, tablets, scanners, etc.

[0316] "Image analysis means" is a component that has the function of analyzing the digital data of input handwritten characters and extracting their characteristics. Optical character recognition technology is often used.

[0317] The "selection means" refers to an interface or function that allows the user to select a desired template font from existing fonts in the system.

[0318] The "emotion recognition means" is a component that analyzes the user's facial expressions and voice data to identify the current emotion.

[0319] The "generator" is a component that generates a new original font using the extracted handwritten character features, the selected font characteristics, and the recognized emotion information. It often uses deep learning techniques.

[0320] "Providing means" refers to the interface and functionality for providing the generated font to the user and enabling download and use.

[0321] This invention combines a system that allows users to generate original fonts using their own handwritten characters with an emotion engine that recognizes the user's emotions. This system can be used in digital signage and in-store displays in brick-and-mortar stores to personalize customer experiences.

[0322] System Overview

[0323] The system includes the following components:

[0324] 1. An input method for users to input handwritten characters

[0325] 2. Image analysis method to analyze input handwritten characters and extract their features

[0326] 3. A means for the user to select an existing font

[0327] 4. Emotion Recognition Method to Recognize User Emotions

[0328] 5. A method for generating a new font by combining the extracted features, the selected font, and the recognized emotion.

[0329] 6. Means of providing the generated font to the user

[0330] Program processing

[0331] The system uses smartphones, digital signage (customer displays), cameras, and servers as its main hardware, and optical character recognition technology (Tesseract OCR) and an emotion recognition engine (Microsoft Azure Cognitive Services) as its software.

[0332] Handwritten character input

[0333] Users write their own handwritten characters on paper and take a photo of it with their smartphone camera, which is then uploaded to a server via a dedicated application.

[0334] Image analysis

[0335] The server receives the uploaded image file of the handwritten characters and uses Tesseract OCR to extract the shape and features of the handwritten characters.

[0336] Font selection

[0337] The user selects the desired font from a list of existing font styles provided in the system, and this information is sent from the terminal to the server.

[0338] Emotion recognition

[0339] The camera captures the user's facial expressions and sends the footage to a server, which uses Microsoft Azure Cognitive Services to analyze the facial expression data and identify the user's emotions.

[0340] Generating a new font

[0341] The server combines the extracted handwritten character features, the selected font characteristics, and the recognized emotion information using a deep learning model, converting the handwritten character features to match the style of the template font, and further adjusting the color and design based on the emotion.

[0342] Font provision

[0343] The server creates the generated font files in standard font formats (e.g., .ttf, .otf) and provides them to users as download links, which they can use in various digital platforms and word processing software.

[0344] Specific examples

[0345] For example, a user visiting a cafe handwrites "What's the coffee for today?" and takes a photo of it with their smartphone. At the same time, an in-store camera captures the user's smile, and the image is analyzed by the server. The handwritten text and smile are used to recognize the emotion of "enjoyment," and the handwritten text is converted into a casual style. The message "Today's coffee is a special blend!" is then displayed on the digital signage in a bright, friendly font.

[0346] Prompt Sentence Examples

[0347] Generate a friendly, bright, original font based on the user-provided handwritten characters and recognized emotion data. Apply colors and styles according to the emotion while preserving the characteristics of the handwritten characters.

[0348] This allows the system to generate original fonts based on the user's individual emotions and handwritten characters, personalizing information provided in physical stores and improving the customer experience.

[0349] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0350] Step 1:

[0351] The user writes their own handwritten characters on paper and takes a photo of them with their smartphone camera. This image is then uploaded to the server via a dedicated application. The input is an image file of the handwritten characters, and the output is the transfer of the image file to the server. Specifically, the user launches the application, follows the instructions to take a photo of the characters with the camera, and presses the upload button.

[0352] Step 2:

[0353] The server analyzes image files received from users using Tesseract OCR. The input is an image file of handwritten characters, and the output is data that extracts the shape and characteristics of the handwritten characters. Specifically, the server sends the image to the OCR module, which then captures the character's outline, line thickness, and other characteristics as digital data.

[0354] Step 3:

[0355] The user selects the desired font from a list of existing font styles provided within the system. The input is the user's font selection, and the output is the selected font style data. In concrete terms, the user selects a font from a drop-down list or thumbnail list within the application and presses the confirm button.

[0356] Step 4:

[0357] The camera captures the user's facial expression, and the video is sent to the server. The input is video data containing the user's facial expression, and the output is video data transferred to the server. Specifically, the camera installed in the physical store captures video at regular intervals and sends it to the server via the network.

[0358] Step 5:

[0359] The server uses Microsoft Azure Cognitive Services to analyze facial expression data and identify the user's emotions. The input is video data of the user's facial expressions, and the output is identified emotion data. Specifically, the server sends the video data to an emotion recognition engine, which identifies emotions such as "happiness" or "sadness" from the user's facial expressions and returns the results to the server as digital data.

[0360] Step 6:

[0361] The server combines the extracted handwritten character features, the selected font characteristics, and the recognized emotion information using a deep learning model. The input is the handwritten character feature data, the selected font style data, and the emotion data, and the output is the generation data for a new font. Specifically, the server inputs this data into the AI ​​model, and the model determines the font shape, color, and style based on it.

[0362] Step 7:

[0363] The server creates the generated font file in a standard font format (e.g., .ttf, .otf) and provides it to the user as a download link. The input is the generated font data, and the output is the download link. Specifically, the server converts the font data into a font file and sends a download link as a notification to the user's device.

[0364] This makes it clear how each processing step specifically operates and what data is input and output.

[0365] 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.

[0366] 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.

[0367] 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.

[0368] [Second embodiment]

[0369] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0370] 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.

[0371] 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).

[0372] 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.

[0373] 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.

[0374] 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).

[0375] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0376] 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.

[0377] 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.

[0378] 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.

[0379] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0380] 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."

[0381] A specific embodiment of the present invention will be described. This invention is a system for generating original fonts using characters handwritten by a user. The processing of the program will be explained in natural language below, with specific examples included.

[0382] System Program Processing

[0383] Handwritten character input

[0384] A user writes their own handwritten text on paper and takes a photo of it with the camera on their smartphone or tablet, or uses a scanner to digitize the text they have written on paper. This digitized image file is then uploaded to the system. For example, a user can write the word "hello" by hand and upload the image to the system.

[0385] Image analysis

[0386] The device sends the uploaded image file of the handwritten characters to the server. The server then uses image recognition technology to analyze the characteristics of the handwritten characters. In this process, OCR (optical character recognition) technology is used to extract the shape and characteristics of each character. For example, the softness of the curve of the character "ko" or the shape of the double curve of the character "n" are analyzed and the characteristics are obtained as data.

[0387] Font selection

[0388] The user selects the desired font from a list of existing font styles provided within the system, including serif, sans serif, handwritten fonts, etc. This information is sent from the terminal to the server.

[0389] Generating a new font

[0390] The server uses AI technology to combine the analyzed handwritten character features with the characteristics of the template font selected by the user. Specifically, it uses a deep learning model to convert the handwritten character features to match the style of the template font. For example, it preserves the soft curves of the user's handwritten characters while adjusting them to the straight lines and curves characteristic of the selected font. A generator defines the shape of each character and creates a path (drawing path) for the new font.

[0391] Font provision

[0392] The server generates a font file based on the path information of the new font. This file is created in a standard font format (e.g., .ttf, .otf). The server provides the generated font file to the user as a download link. The user can download the font file from this link and use it on various digital platforms and word processing software. For example, when typing "hello" using the generated original font, it will be displayed in a font that reflects the user's unique handwritten style.

[0393] In this way, users can easily create and use original fonts that take advantage of the characteristics of their own handwritten characters. This system is an effective means of creating high-quality digital fonts that meet the needs of individual users.

[0394] The processing flow will be explained below.

[0395] Step 1:

[0396] Users write their own handwriting on paper, take a photo of it with their smartphone or tablet camera, or use a scanner to digitize the writing on paper, and then upload this digitized image file to the system.

[0397] Step 2:

[0398] The terminal transmits the image file of the handwritten characters uploaded by the user to the server.

[0399] Step 3:

[0400] The server then passes the received image file to the image analysis module, which uses OCR (optical character recognition) technology to extract the shape and characteristics of the handwritten characters. For example, it obtains data on the softness of the curves in the character "ko" and the connection between the dots in the character "n."

[0401] Step 4:

[0402] The user selects the desired font style from a list of existing font styles displayed in the system. The choices include serif, sans serif, handwritten fonts, etc. This selection information is sent from the terminal to the server.

[0403] Step 5:

[0404] The server uses AI technology to combine the analyzed handwritten character characteristics with the characteristics of the template font selected by the user. Using a deep learning model, the server converts the handwritten character characteristics to match the style of the template font. Specifically, it adjusts the shape to match the consistency of the existing font while preserving the soft curves and unique line expression of the handwritten character.

[0405] Step 6:

[0406] The server defines the path (drawing path) for each character based on the adjusted features and Bézier curves. A font file is generated based on this path information. The font file is created in accordance with standard font formats (e.g., .ttf, .otf).

[0407] Step 7:

[0408] The server uploads the generated original font file to the user's download page.

[0409] Step 8:

[0410] Users click the download link to download the original font file, which they can then install on their PC or device.

[0411] Step 9:

[0412] Users can use the installed original fonts for document creation, graphic design, etc. The generated fonts are displayed in a beautiful and neat manner while retaining the individuality of the user's handwritten characters.

[0413] Example 1

[0414] 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."

[0415] Conventional font generation systems do not allow users to easily generate their own handwritten characters as digital fonts and use them individually. Furthermore, there is a lack of technology that faithfully reproduces the individual characteristics of handwritten characters while combining them with existing template fonts. This makes it difficult for users to digitize their own handwritten characters and use them across various platforms.

[0416] 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.

[0417] In this invention, the server includes means for uploading input handwritten characters to the system as an image file, means for transmitting the image file to the server, image analysis means for analyzing the input handwritten characters and extracting their characteristics, selection means for the user to select an existing font, generation means for generating a new font by combining the extracted characteristics with the selected font, and provision means for providing the generated font to the user. This enables users to easily generate original fonts that make use of the characteristics of their own handwritten characters and use them on various digital platforms and word processing software.

[0418] "Input means" refers to a means by which a user inputs handwritten characters and inputs them into the system in digital form.

[0419] "Uploading means" refers to a process or interface for a user to upload an image file of handwritten characters that has been photographed or digitized by the user to the system.

[0420] The "transmission means" is a means for transmitting the uploaded image file from the terminal to the server.

[0421] "Image analysis means" refers to the technology and algorithms used to analyze and extract features from input images of handwritten characters.

[0422] The "selection means" is a means for the user to select a desired font from a list of existing font styles provided within the system.

[0423] The "generation means" is a means for generating a new font based on the extracted handwritten character characteristics and the selected font style information.

[0424] "Providing means" refers to a method or interface for providing the generated new font to the user.

[0425] "Optical character recognition technology" is a technology that recognizes characters from scanned digital images of printed or handwritten characters and converts them into digital text.

[0426] "Deep learning technology" is a machine learning technology that uses multi-layered neural networks to perform pattern recognition and generation tasks.

[0427] This invention is a system that allows a user to create an original font using his / her own handwritten characters. This system is specifically implemented using the following hardware and software.

[0428] A user writes a letter by hand on a piece of paper, takes a photo of it with a smartphone or tablet camera, or uses a scanner to digitize it, and then uploads the digital image file of the handwritten letter to the system. For example, a user writes the word "hello" by hand and uploads the image to the system.

[0429] The device sends the uploaded image file of the handwritten characters to a server. The server then uses OCR (optical character recognition) technology to analyze the characteristics of the handwritten characters. This process uses software like Tesseract OCR to extract the shape and characteristics of each character. For example, the softness of the curve of the character "ko" and the shape of the double curve of the character "n" are analyzed and the characteristics are obtained as data.

[0430] The user selects a desired font, such as serif, sans serif, or handwritten font, from a list of existing font styles provided within the system. The selected font style information is sent from the terminal to the server.

[0431] The server uses AI technology to combine the analyzed handwritten character features with the characteristics of the template font selected by the user. Specifically, it uses deep learning models (e.g., GANs and Variational Autoencoders) to convert the handwritten character features to match the style of the template font. For example, it uses a method to preserve the soft curves of the user's handwritten characters while adjusting them to the straight lines and curves characteristic of the selected font. A generator defines the shape of each character and creates a path (drawing path) for the new font.

[0432] The server generates a font file based on the path information of the new font. This file is created in a standard font format (e.g., .ttf, .otf). The server provides the generated font file as a download link that users can access. Users can download the font file from this link and use it on various digital platforms and word processing software. For example, when you type "hello" using the generated original font, it will be displayed in a font that reflects the user's unique handwritten style.

[0433] As a concrete example, consider a case where a user writes "Thank you" by hand on a piece of paper, takes a photo of it with their smartphone, and uploads it to the system. The user selects a handwritten font, and once the new font is generated, the server provides a download link. The user downloads the font from the link, and when they type "Thank you" into a document, the original handwritten style is reflected.

[0434] An example of a prompt would be:

[0435] Upload your handwritten "Thank you" text, select a handwritten font, generate a new font file, and provide the download link for this font file.

[0436] This system allows users to easily create and use original fonts that take advantage of the characteristics of their handwritten characters.

[0437] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0438] Step 1:

[0439] Users can write by hand on paper and then take a photo of it with their smartphone or tablet camera, or use a scanner to digitize the text they have written on paper.

[0440] Input: Image file of handwritten characters

[0441] Output: Digital image file

[0442] What it does: A user writes the word "hello" by hand, takes a photo or scans the image, and then uploads the digital image file to the system.

[0443] Step 2:

[0444] The terminal transmits the digital image files uploaded by the user to the server.

[0445] Input: User-uploaded digital image files

[0446] Output: Digital image files sent to the server

[0447] Specific operation: The terminal automatically transmits the digital image file to the server through the upload form specified by the user.

[0448] Step 3:

[0449] The server receives the transmitted image file and uses OCR (optical character recognition) technology to analyze the handwritten characters and extract their features.

[0450] Input: Digital image files received by the server

[0451] Output: Analyzed character feature data

[0452] How it works: The server begins analyzing the image using software such as Tesseract OCR, analyzing the shape and characteristics of each character and extracting data such as the softness of the curve of "ko" and the shape of the double curve of "n."

[0453] Step 4:

[0454] The user selects the desired font from a list of existing font styles provided within the system.

[0455] Input: Selection information from the font style list

[0456] Output: User selected font information

[0457] Specific operation: The user uses the system interface to select the desired font, such as serif, sans serif, or handwritten font.

[0458] Step 5:

[0459] The terminal transmits the user's selection information to the server.

[0460] Input: User selected font information

[0461] Output: Font information sent to the server

[0462] Specific operation: The terminal automatically sends information about the font selected by the user to the server.

[0463] Step 6:

[0464] The server uses a deep learning model to generate a new font based on the analyzed handwritten character feature data and the font information selected by the user.

[0465] Input: Handwritten character feature data and user font selection information

[0466] Output: New font path information

[0467] How it works: The server uses deep learning techniques (e.g., GANs and Variational Autoencoders) to analyze the characteristics of handwritten characters and adjust them to match the style of the selected font. This process generates a new font that preserves the softness and curves of the handwritten characters.

[0468] Step 7:

[0469] The server creates a font file based on the path information of the generated font.

[0470] Input: Path information for the new font

[0471] Output: Generated font file (.ttf or .otf format)

[0472] Specific operation: The server uses a font generation library (e.g. FontForge) to create a font file based on the path information of the generated font.

[0473] Step 8:

[0474] The server generates a download link for providing the font file to the user and notifies the user.

[0475] Input: Generated font file

[0476] Output: Download link

[0477] Specific operation: The server stores the generated font file and notifies the user of the download link, through which the user can download the font file.

[0478] (Application example 1)

[0479] 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."

[0480] While conventional font generation systems can digitize a user's handwritten text and use it as a font, they lack the ability to replace and display text in real time. This makes it difficult for users to effectively use their handwritten text in advertisements and other visual content. Furthermore, there is a need for a system that can instantly apply a user's handwritten text style to enhance the individuality of advertisement content and visuals, providing a more personalized experience.

[0481] 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.

[0482] In this invention, the server includes an input means for a user to input handwritten characters, an image analysis means for analyzing the input handwritten characters and extracting features, a selection means for the user to select an existing font, a generation means for generating a new font by combining the extracted features with the selected font, a provision means for providing the generated font to the user, and a display means for replacing recognized characters with the user's handwritten font and displaying it in real time. This makes it possible to display an original font generated by a user using handwritten characters in real time and effectively utilize it in advertisements and other visual content.

[0483] "Input means" refers to a device or interface that allows a user to input handwritten characters into the system.

[0484] "Image analysis means" refers to the technology or device used to analyze input handwritten characters and extract their characteristics.

[0485] "Selection means" refers to a device or interface that allows a user to select an existing font from within the system.

[0486] "Generator" refers to the technology or device that combines the extracted features with the selected font to generate a new font.

[0487] "Providing means" refers to a device or interface for providing the generated font to the user.

[0488] The "display means" refers to a device or interface for converting the recognized characters into the user's handwritten font and displaying them in real time.

[0489] The present invention provides a system that allows users to generate original fonts using their own handwritten characters and apply them to advertisements and visual content in real time. Detailed embodiments of this system will be described below.

[0490] First, a user writes their own handwritten characters on a piece of paper and takes a photo of it with the camera on their smartphone or tablet. Alternatively, they can use a scanner to digitize the characters they have written on paper. This digitized image file is then uploaded to the server. For example, a user can write the word "hello" by hand and upload the image to the server.

[0491] The server receives the uploaded image file of the handwritten characters and uses image recognition technology to analyze the characteristics of the handwritten characters. In this process, OCR (Optical Character Recognition) technology is used to extract the shape and characteristics of each character. For example, the softness of the curve of the character "ko" or the shape of the double curve of the character "n" are analyzed and the characteristics are obtained as data.

[0492] The user then selects the desired font from a list of existing font styles provided within the system, including serif, sans serif, handwritten fonts, etc. This information is sent to the server.

[0493] The server then uses AI technology to combine the analyzed handwritten character characteristics with the characteristics of the template font selected by the user. Specifically, it uses a deep learning model to convert the handwritten character characteristics to match the style of the template font. For example, it preserves the soft curves of the user's handwritten character while adjusting them to the straight lines and curves characteristic of the selected font. A path (drawing path) for the generated new font is then created.

[0494] The new font is displayed in real time. Through a display device such as smart glasses, users can capture text on billboards or posters with their camera, and the recognized text is then replaced with the user's handwritten font. This allows users to apply their own handwritten style to advertisements, providing a personalized experience.

[0495] For example, when a user sees an advertising billboard at an event venue, the system can capture and analyze it with a camera and instantly display it in the user's handwritten font. An example of a prompt would be "Please display the following advertising text in my original font: Advertising text," and the system would follow the prompt and display it in the handwritten font.

[0496] This allows users to personalize advertisements and other visual content using their own handwritten characters, enhancing their visual individuality.The system is realized using hardware such as smart glasses (e.g., Google Glass), smartphones, and servers, and software such as Python, OpenCV, PIL, and pytesseract.

[0497] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0498] Step 1:

[0499] The user writes handwritten text on paper. The user takes a photo of their handwritten text with a smartphone or tablet camera or digitizes it using a scanner. The input is the written text on paper, and the output is a digital image file (e.g., JPEG, PNG).

[0500] Step 2:

[0501] The user uploads an image file of digitized handwritten characters to the server. The device (smartphone or tablet) provides an upload function and transfers the user's image file to the server. The input is a digital image file, and the output is image data on the server.

[0502] Step 3:

[0503] The server receives the uploaded image and uses image analysis to extract the characteristics of the handwritten characters. Specifically, it uses OCR (Optical Character Recognition) technology to detect the characters and obtain data on the shape and characteristics of each character (e.g., softness of curves, angle, line thickness). The input is a digital image file, and the output is character feature data.

[0504] Step 4:

[0505] The user selects the desired font from a list of font styles provided by the server. The terminal provides a font selection interface and sends the user's selection information to the server. The input is the user's selection action, and the output is the selected font information.

[0506] Step 5:

[0507] The server combines the extracted handwritten character features with the font style selected by the user to generate a new original font. A deep learning model is used to convert the handwritten character features to match the existing font style. The input is the feature data and the selected font information, and the output is the path information for the new font.

[0508] Step 6:

[0509] The server provides the generated font file (e.g., .ttf, .otf) to the user. As a means of providing the file, it generates a download link and notifies the user. The input is the path information of the font, and the output is the download link for the font file.

[0510] Step 7:

[0511] A user wears smart glasses and looks at an advertising billboard or poster in the city or at an event venue. The camera in the smart glasses captures the text of the advertisement and sends it to the server. The input is an image of the advertisement, and the output is the image data transferred to the server.

[0512] Step 8:

[0513] The server receives the captured image and replaces the recognized characters with the user's handwritten font. It analyzes the characters using image analysis means, applies the generated original font, and displays it in real time. The input is the advertisement image and the original font file, and the output is image data of the replaced characters.

[0514] Step 9:

[0515] The smart glasses display the replaced text to the user in real time, allowing the user to visually confirm that their own handwritten style is reflected in the advertisement. The input is image data of the replaced text, and the output is the text displayed on the smart glasses screen.

[0516] 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.

[0517] A specific embodiment of the present invention will be described. This invention combines a system that allows users to generate original fonts using their own handwritten characters with an emotion engine that recognizes the user's emotions. The processing of this program will be explained in natural language below, along with specific examples.

[0518] System Program Processing

[0519] Handwritten character input

[0520] A user writes their own handwritten text on paper and takes a photo of it with the camera on their smartphone or tablet, or uses a scanner to digitize the text they have written on paper. This digitized image file is then uploaded to the system. For example, a user can write "hello" and upload the image to the system.

[0521] Image analysis

[0522] The device sends the uploaded image file of the handwritten characters to the server. The server then passes the received image file to the image analysis module. This module uses OCR (optical character recognition) technology to extract the shape and characteristics of the handwritten characters. For example, it analyzes the softness of the curves in the character "ko" and the shape of the double curve in the character "n" and obtains the characteristics as data.

[0523] Font selection

[0524] The user selects the desired font from a list of existing font styles provided within the system, including serif, sans serif, handwritten fonts, etc. This information is sent from the terminal to the server.

[0525] Emotion recognition

[0526] The device captures or records the user's facial expressions and voice, and sends them to an emotion recognition engine. The emotion engine analyzes this data and identifies the user's current emotion. For example, if the user is smiling, it will be identified as "joy" or "enjoyment," and if the user has no expression, it will be identified as "neutral."

[0527] Generating a new font

[0528] The server uses AI technology to combine the analyzed handwritten character characteristics, the characteristics of the template font selected by the user, and the recognized emotion information. Using a deep learning model, the server converts the handwritten character characteristics to match the style of the template font. Furthermore, the server adjusts the font color and style (e.g., whether to use rounded, soft shapes or sharp shapes) depending on the emotion. For example, if the user is expressing the emotion "joy," the server adds bright colors and soft curves to the font.

[0529] Font provision

[0530] The server defines the path (drawing path) for each character based on the adjusted features and Bézier curves and generates a font file. This file is created in a standard font format (e.g., .ttf, .otf). The server provides the generated font file to the user as a download link. The user can download the font file from this link and use it on various digital platforms and word processing software.

[0531] example

[0532] For example, a user can handwrite "hello" and upload the image along with a photo of their own smiling face to the system. The server analyzes the characteristics of the handwritten characters and recognizes the emotion of "joy" from the smile. If the selected template font is a serif font, the server generates a font that preserves the soft curves of the handwritten characters while adding bright, cheerful colors. Users can download this original font and use it in their individual designs and writing.

[0533] This allows users to easily create and use original fonts that are more personalized based on their own handwritten characters and emotions.

[0534] The processing flow will be explained below.

[0535] Step 1:

[0536] Users write their own handwritten characters on paper, take a photo of it with the camera on their smartphone or tablet, or use a scanner to digitize the characters they have written on paper, and then upload this digitized image file to the system.

[0537] Step 2:

[0538] The terminal transmits the image file of the handwritten characters uploaded by the user to the server.

[0539] Step 3:

[0540] The server then passes the received image file to the image analysis module, which uses OCR (optical character recognition) technology to extract the shape and characteristics of the handwritten characters from the image file. Specifically, it obtains the outlines and curves of the characters as data.

[0541] Step 4:

[0542] The user selects the desired font from a list of existing font styles displayed in the system. For example, they can select serif, sans serif, handwritten font, etc. This selection information is sent from the terminal to the server.

[0543] Step 5:

[0544] The device captures the user's facial expressions and voice and sends them to the emotion engine on the server. For example, the device can capture the user's facial expressions with a smartphone camera and send them as they are.

[0545] Step 6:

[0546] The server's emotion engine analyzes the received facial and voice data to identify the user's emotion. For example, it recognizes the user's smile as the emotion of "joy."

[0547] Step 7:

[0548] The server generates a new font by combining the characteristics of handwritten characters and the selected font based on the emotion information obtained from the emotion engine. Using a deep learning model, the server preserves the characteristics of handwritten characters and adds design elements (e.g., color and style changes) according to the emotion.

[0549] Step 8:

[0550] The server defines the path (drawing path) for each character based on the adjusted features and Bézier curves, and generates a font file in a standard font format (e.g., .ttf, .otf).

[0551] Step 9:

[0552] The server will upload the generated original font file to the user's download page, where users can download the font file and install it on their PC or device.

[0553] Step 10:

[0554] Users can use the installed original fonts for document creation, graphic design, etc. For example, when they type "hello" using the generated font, it will be displayed in a unique font that reflects the user's handwriting and emotions.

[0555] Example 2

[0556] 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."

[0557] In conventional font generation systems using handwritten characters, it has been difficult to generate font styles that are linked to the user's emotions. In particular, it has been a technical challenge to automatically generate a font that matches the user's emotions while preserving the unique characteristics of handwritten characters. Furthermore, there has been no system that can recognize the user's emotions from their facial expressions or voice and reflect them in the font generation. This has resulted in the problem that users cannot easily generate more personalized original fonts.

[0558] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0559] In this invention, the server includes input means for a user to input handwritten characters, image analysis means for analyzing the input handwritten characters and extracting features, selection means for the user to select an existing font, emotion recognition means for identifying the user's emotion, generation means for generating a new font by combining the extracted features, the selected font, and the identified emotion information, and provision means for providing the generated font to the user. This makes it possible to automatically generate a font style corresponding to the user's emotion, allowing the user to easily create and use a more personalized original font linked to their emotion.

[0560] An "input means" is a means by which a user writes handwritten characters on paper and photographs them with the camera of a smartphone or tablet, or uses a scanner to upload the digitized image file to the system.

[0561] The "image analysis means" is a means for analyzing an uploaded image file of handwritten characters and extracting the shape and characteristics of the handwritten characters using OCR (optical character recognition) technology.

[0562] The "selection means" is a means by which a user selects a desired font from a list of existing font styles provided in the system and transmits the information to the system.

[0563] The "emotion recognition means" is a means for capturing the user's facial expressions and voice using a camera or microphone, analyzing the data, and identifying the user's current emotions.

[0564] The "generation means" is a means for generating a new font by combining the analyzed handwritten character characteristics, the selected font, and the identified emotional information using AI technology.

[0565] The "means for providing" is a means for generating a font file from the generated new font and providing the file to the user.

[0566] A specific embodiment of the present invention will be described. This invention combines a system that allows a user to generate an original font using their own handwritten characters with an emotion engine that recognizes the user's emotions.

[0567] System Configuration

[0568] This system is realized mainly by using the following hardware and software.

[0569] 1. Input method: The user inputs an image of handwritten characters using a device such as a smartphone, tablet, or scanner.

[0570] 2. Image analysis means: A server including an image analysis module using OCR (Optical Character Recognition) technology is used.

[0571] 3. Selection: The user selects an existing font using a web application or a dedicated system app.

[0572] 4. Emotion recognition means: using face recognition and voice analysis engines, for example devices with cameras and microphones.

[0573] 5. Generation method: A server that uses deep learning technology to generate new fonts.

[0574] 6. Provisioning means: A server that generates a download link to provide the generated font file to the user.

[0575] System Operation

[0576] Handwritten character input

[0577] A user writes their own handwritten characters on paper and takes a picture of the character using the camera on their smartphone or tablet. Alternatively, they can use a scanner to digitize the characters they have written on paper. This digitized image file is then uploaded to the system. Specifically, a user writes the word "hello" and uploads the image to the system.

[0578] Image analysis

[0579] The device sends the uploaded image file of the handwritten characters to the server. The server then passes the received image file to the image analysis module. This module uses OCR technology to extract the shape and characteristics of the handwritten characters. For example, it analyzes the softness of the curves in the character "ko" and the shape of the double curve in the character "n" and obtains the characteristics as data.

[0580] Font selection

[0581] The user selects the desired font from a list of existing font styles provided within the system, including serif, sans serif, handwritten fonts, etc. This information is sent from the terminal to the server.

[0582] Emotion recognition

[0583] The device captures or records the user's facial expressions and voice, and sends them to an emotion recognition engine. The emotion engine analyzes this data and identifies the user's current emotion. For example, if the user is smiling, it will be identified as "joy" or "enjoyment," and if the user has no expression, it will be identified as "neutral."

[0584] Generating a new font

[0585] The server uses AI technology to combine the analyzed handwritten character characteristics, the characteristics of the template font selected by the user, and the recognized emotion information. Using a deep learning model, the server converts the handwritten character characteristics to match the style of the template font. It also adjusts the font color and style according to the emotion. For example, if the user is expressing the emotion "joy," it adds bright colors and soft curves to the font.

[0586] Font provision

[0587] The server defines the path of each character based on the adjusted features and Bézier curves, and generates a font file. This file is created in a standard font format (e.g., .ttf, .otf). The server then provides the generated font file to the user as a download link. The user can download the font file from this link and use it on various digital platforms and word processing software.

[0588] Examples and prompts

[0589] For example, a user can handwrite "hello" and upload the image along with a photo of their smiling face to the system. The server analyzes the characteristics of the handwritten characters and recognizes the emotion of "joy" from the smile. If the selected template font is a serif font, the server generates a font that preserves the soft curves of the handwritten characters while adding bright, fun colors. Users can download this original font and use it in their own designs and writing.

[0590] An example of a prompt to input to a generative AI model is, "Please explain the process by which a user uploads an image of handwritten characters and emotional information to the system and generates an original font."

[0591] This allows users to easily create and use original fonts that are more personalized based on their own handwritten characters and emotions.

[0592] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0593] Step 1:

[0594] Users can write their own handwritten characters on paper and take a photo of it with their smartphone or tablet camera, or use a scanner to digitize the characters.

[0595] Input: Handwritten text on paper

[0596] Output: Digitized image files (e.g. JPEG, PNG)

[0597] What happens: The user writes "Hello" on a piece of paper and takes a photo of it with their smartphone camera.

[0598] Step 2:

[0599] The user uploads a photographed or scanned image file to the system.

[0600] Input: Digitized image file of handwritten characters

[0601] Output: Image file uploaded to the system

[0602] Specific operation: The user opens the system's upload screen, selects the "Hello" image file, and uploads it.

[0603] Step 3:

[0604] The terminal transmits the uploaded image file of the handwritten characters to the server.

[0605] Input: User uploaded image file

[0606] Output: Image file sent to the server

[0607] Specific operation: The device sends the "Hello" image file uploaded by the user to the server via Wi-Fi or mobile network.

[0608] Step 4:

[0609] The server passes the received image file to the image analysis module.

[0610] Input: Uploaded image file of handwritten characters

[0611] Output: Input data to the image analysis module

[0612] Specific operation: The server receives the image file and saves it in a directory for image analysis.

[0613] Step 5:

[0614] The server uses OCR technology to extract the shape and characteristics of handwritten characters.

[0615] Input: Image file of handwritten characters passed to the image analysis module

[0616] Output: Extracted character shapes and feature data

[0617] Specific operation: The server's OCR engine analyzes the image file and obtains data such as the softness of the curve of "ko" and the shape of the double curve of "n."

[0618] Step 6:

[0619] The user selects the desired font from a list of existing font styles provided within the system.

[0620] Input: List of font styles

[0621] Output: Selected font style data

[0622] What happens: The user selects a serif font from the system font selection screen.

[0623] Step 7:

[0624] The terminal transmits the font information selected by the user to the server.

[0625] Input: Selected font style data

[0626] Output: Font style data sent to the server

[0627] Specific operation: The device sends the serif font information selected by the user to the server.

[0628] Step 8:

[0629] The device captures or records the user's facial expressions and voice and sends them to an emotion recognition engine.

[0630] Input: User's facial image and voice data

[0631] Output: Input data to the emotion recognition engine

[0632] Specific operation: The device's camera captures the user's smile and the microphone records their voice.

[0633] Step 9:

[0634] The server's emotion recognition engine analyzes this data and identifies the user's current emotion.

[0635] Input: facial expression images and audio data

[0636] Output: Identified emotion information (e.g., joy, pleasure, neutral)

[0637] Specific operation: The emotion recognition engine analyzes smile image data and identifies the emotion of "happiness."

[0638] Step 10:

[0639] The server combines the analyzed handwritten character features, the selected font characteristics, and the recognized emotion information using AI technology.

[0640] Input: Handwritten character feature data, font style data, emotion information

[0641] Output: Data needed to generate a new font

[0642] What it does: Uses a deep learning model to adjust the softness of handwritten characters to match the characteristics of serif fonts.

[0643] Step 11:

[0644] The server adjusts the font color and style depending on the emotion.

[0645] Input: Emotion information, font generation data

[0646] Output: Emotion-aware font styles

[0647] What it does: Add bright colors and soft curves to fonts based on the emotion of "fun."

[0648] Step 12:

[0649] The server defines the path of each character based on the adjusted features and Bezier curves, and generates a font file.

[0650] Input: Adjusted character feature data, Bezier curve data

[0651] Output: Font file (e.g. .ttf, .otf)

[0652] What it does: It uses Bezier curves to define the path of each character and generates a font file.

[0653] Step 13:

[0654] The server provides the generated font file to the user as a download link.

[0655] Input: Generated font file

[0656] Output: Download link

[0657] What happens: The server uploads the font file to cloud storage and provides the link to the user.

[0658] (Application example 2)

[0659] 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."

[0660] Conventional font generation systems generate original fonts by combining the user's handwritten characters with template fonts. However, these systems do not take the user's emotions into account, which limits the user experience and makes it difficult to generate fonts that respond to individual emotions and situations. In addition, there is a lack of systems in physical stores that can provide information that responds to the individual needs and emotions of customers.

[0661] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0662] In this invention, the server includes an input means for a user to input handwritten characters, an image analysis means for analyzing the input handwritten characters and extracting features, a selection means for the user to select an existing font, an emotion recognition means for recognizing the user's emotion, a generation means for generating a new font by combining the extracted features, the selected font, and the recognized emotion, and a provision means for providing the generated font to the user. This allows a personalized font to be generated according to the customer's handwritten characters and emotions, making it possible to provide individual information in real time in a physical store.

[0663] A "user" is an individual or group who uses the system to input handwritten characters and generate an original font.

[0664] "Input means" refers to a device or interface that allows a user to digitize handwritten characters and provide them to the system. Examples include smartphones, tablets, scanners, etc.

[0665] "Image analysis means" is a component that has the function of analyzing the digital data of input handwritten characters and extracting their characteristics. Optical character recognition technology is often used.

[0666] The "selection means" refers to an interface or function that allows the user to select a desired template font from existing fonts in the system.

[0667] The "emotion recognition means" is a component that analyzes the user's facial expressions and voice data to identify the current emotion.

[0668] The "generator" is a component that generates a new original font using the extracted handwritten character features, the selected font characteristics, and the recognized emotion information. It often uses deep learning techniques.

[0669] "Providing means" refers to the interface and functionality for providing the generated font to the user and enabling download and use.

[0670] This invention combines a system that allows users to generate original fonts using their own handwritten characters with an emotion engine that recognizes the user's emotions. This system can be used in digital signage and in-store displays in brick-and-mortar stores to personalize customer experiences.

[0671] System Overview

[0672] The system includes the following components:

[0673] 1. An input method for users to input handwritten characters

[0674] 2. Image analysis method to analyze input handwritten characters and extract their features

[0675] 3. A means for the user to select an existing font

[0676] 4. Emotion Recognition Method to Recognize User Emotions

[0677] 5. A method for generating a new font by combining the extracted features, the selected font, and the recognized emotion.

[0678] 6. Means of providing the generated font to the user

[0679] Program processing

[0680] The system uses smartphones, digital signage (customer displays), cameras, and servers as its main hardware, and optical character recognition technology (Tesseract OCR) and an emotion recognition engine (Microsoft Azure Cognitive Services) as its software.

[0681] Handwritten character input

[0682] Users write their own handwritten characters on paper and take a photo of it with their smartphone camera, which is then uploaded to a server via a dedicated application.

[0683] Image analysis

[0684] The server receives the uploaded image file of the handwritten characters and uses Tesseract OCR to extract the shape and features of the handwritten characters.

[0685] Font selection

[0686] The user selects the desired font from a list of existing font styles provided in the system, and this information is sent from the terminal to the server.

[0687] Emotion recognition

[0688] The camera captures the user's facial expressions and sends the footage to a server, which uses Microsoft Azure Cognitive Services to analyze the facial expression data and identify the user's emotions.

[0689] Generating a new font

[0690] The server combines the extracted handwritten character features, the selected font characteristics, and the recognized emotion information using a deep learning model, converting the handwritten character features to match the style of the template font, and further adjusting the color and design based on the emotion.

[0691] Font provision

[0692] The server creates the generated font files in standard font formats (e.g., .ttf, .otf) and provides them to users as download links, which they can use in various digital platforms and word processing software.

[0693] Specific examples

[0694] For example, a user visiting a cafe handwrites "What's the coffee for today?" and takes a photo of it with their smartphone. At the same time, an in-store camera captures the user's smile, and the image is analyzed by the server. The handwritten text and smile are used to recognize the emotion of "enjoyment," and the handwritten text is converted into a casual style. The message "Today's coffee is a special blend!" is then displayed on the digital signage in a bright, friendly font.

[0695] Prompt Sentence Examples

[0696] Generate a friendly, bright, original font based on the user-provided handwritten characters and recognized emotion data. Apply colors and styles according to the emotion while preserving the characteristics of the handwritten characters.

[0697] This allows the system to generate original fonts based on the user's individual emotions and handwritten characters, personalizing information provided in physical stores and improving the customer experience.

[0698] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0699] Step 1:

[0700] The user writes their own handwritten characters on paper and takes a photo of them with their smartphone camera. This image is then uploaded to the server via a dedicated application. The input is an image file of the handwritten characters, and the output is the transfer of the image file to the server. Specifically, the user launches the application, follows the instructions to take a photo of the characters with the camera, and presses the upload button.

[0701] Step 2:

[0702] The server analyzes image files received from users using Tesseract OCR. The input is an image file of handwritten characters, and the output is data that extracts the shape and characteristics of the handwritten characters. Specifically, the server sends the image to the OCR module, which then captures the character's outline, line thickness, and other characteristics as digital data.

[0703] Step 3:

[0704] The user selects the desired font from a list of existing font styles provided within the system. The input is the user's font selection, and the output is the selected font style data. In concrete terms, the user selects a font from a drop-down list or thumbnail list within the application and presses the confirm button.

[0705] Step 4:

[0706] The camera captures the user's facial expression, and the video is sent to the server. The input is video data containing the user's facial expression, and the output is video data transferred to the server. Specifically, the camera installed in the physical store captures video at regular intervals and sends it to the server via the network.

[0707] Step 5:

[0708] The server uses Microsoft Azure Cognitive Services to analyze facial expression data and identify the user's emotions. The input is video data of the user's facial expressions, and the output is identified emotion data. Specifically, the server sends the video data to an emotion recognition engine, which identifies emotions such as "happiness" or "sadness" from the user's facial expressions and returns the results to the server as digital data.

[0709] Step 6:

[0710] The server combines the extracted handwritten character features, the selected font characteristics, and the recognized emotion information using a deep learning model. The input is the handwritten character feature data, the selected font style data, and the emotion data, and the output is the generation data for a new font. Specifically, the server inputs this data into the AI ​​model, and the model determines the font shape, color, and style based on it.

[0711] Step 7:

[0712] The server creates the generated font file in a standard font format (e.g., .ttf, .otf) and provides it to the user as a download link. The input is the generated font data, and the output is the download link. Specifically, the server converts the font data into a font file and sends a download link as a notification to the user's device.

[0713] This makes it clear how each processing step specifically operates and what data is input and output.

[0714] 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.

[0715] 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.

[0716] 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.

[0717] [Third embodiment]

[0718] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0719] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0720] 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).

[0721] 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.

[0722] 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.

[0723] 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).

[0724] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0725] 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.

[0726] 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.

[0727] 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.

[0728] 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.

[0729] 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."

[0730] A specific embodiment of the present invention will be described. This invention is a system for generating original fonts using characters handwritten by a user. The processing of the program will be explained in natural language below, with specific examples included.

[0731] System Program Processing

[0732] Handwritten character input

[0733] A user writes their own handwritten text on paper and takes a photo of it with the camera on their smartphone or tablet, or uses a scanner to digitize the text they have written on paper. This digitized image file is then uploaded to the system. For example, a user can write the word "hello" by hand and upload the image to the system.

[0734] Image analysis

[0735] The device sends the uploaded image file of the handwritten characters to the server. The server then uses image recognition technology to analyze the characteristics of the handwritten characters. In this process, OCR (optical character recognition) technology is used to extract the shape and characteristics of each character. For example, the softness of the curve of the character "ko" or the shape of the double curve of the character "n" are analyzed and the characteristics are obtained as data.

[0736] Font selection

[0737] The user selects the desired font from a list of existing font styles provided within the system, including serif, sans serif, handwritten fonts, etc. This information is sent from the terminal to the server.

[0738] Generating a new font

[0739] The server uses AI technology to combine the analyzed handwritten character features with the characteristics of the template font selected by the user. Specifically, it uses a deep learning model to convert the handwritten character features to match the style of the template font. For example, it preserves the soft curves of the user's handwritten characters while adjusting them to the straight lines and curves characteristic of the selected font. A generator defines the shape of each character and creates a path (drawing path) for the new font.

[0740] Font provision

[0741] The server generates a font file based on the path information of the new font. This file is created in a standard font format (e.g., .ttf, .otf). The server provides the generated font file to the user as a download link. The user can download the font file from this link and use it on various digital platforms and word processing software. For example, when typing "hello" using the generated original font, it will be displayed in a font that reflects the user's unique handwritten style.

[0742] In this way, users can easily create and use original fonts that take advantage of the characteristics of their own handwritten characters. This system is an effective means of creating high-quality digital fonts that meet the needs of individual users.

[0743] The processing flow will be explained below.

[0744] Step 1:

[0745] Users write their own handwriting on paper, take a photo of it with their smartphone or tablet camera, or use a scanner to digitize the writing on paper, and then upload this digitized image file to the system.

[0746] Step 2:

[0747] The terminal transmits the image file of the handwritten characters uploaded by the user to the server.

[0748] Step 3:

[0749] The server then passes the received image file to the image analysis module, which uses OCR (optical character recognition) technology to extract the shape and characteristics of the handwritten characters. For example, it obtains data on the softness of the curves in the character "ko" and the connection between the dots in the character "n."

[0750] Step 4:

[0751] The user selects the desired font style from a list of existing font styles displayed in the system. The choices include serif, sans serif, handwritten fonts, etc. This selection information is sent from the terminal to the server.

[0752] Step 5:

[0753] The server uses AI technology to combine the analyzed handwritten character characteristics with the characteristics of the template font selected by the user. Using a deep learning model, the server converts the handwritten character characteristics to match the style of the template font. Specifically, it adjusts the shape to match the consistency of the existing font while preserving the soft curves and unique line expression of the handwritten character.

[0754] Step 6:

[0755] The server defines the path (drawing path) for each character based on the adjusted features and Bézier curves. A font file is generated based on this path information. The font file is created in accordance with standard font formats (e.g., .ttf, .otf).

[0756] Step 7:

[0757] The server uploads the generated original font file to the user's download page.

[0758] Step 8:

[0759] Users click the download link to download the original font file, which they can then install on their PC or device.

[0760] Step 9:

[0761] Users can use the installed original fonts for document creation, graphic design, etc. The generated fonts are displayed in a beautiful and neat manner while retaining the individuality of the user's handwritten characters.

[0762] Example 1

[0763] 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."

[0764] Conventional font generation systems do not allow users to easily generate their own handwritten characters as digital fonts and use them individually. Furthermore, there is a lack of technology that faithfully reproduces the individual characteristics of handwritten characters while combining them with existing template fonts. This makes it difficult for users to digitize their own handwritten characters and use them across various platforms.

[0765] 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.

[0766] In this invention, the server includes means for uploading input handwritten characters to the system as an image file, means for transmitting the image file to the server, image analysis means for analyzing the input handwritten characters and extracting their characteristics, selection means for the user to select an existing font, generation means for generating a new font by combining the extracted characteristics with the selected font, and provision means for providing the generated font to the user. This enables users to easily generate original fonts that make use of the characteristics of their own handwritten characters and use them on various digital platforms and word processing software.

[0767] "Input means" refers to a means by which a user inputs handwritten characters and inputs them into the system in digital form.

[0768] "Uploading means" refers to a process or interface for a user to upload an image file of handwritten characters that has been photographed or digitized by the user to the system.

[0769] The "transmission means" is a means for transmitting the uploaded image file from the terminal to the server.

[0770] "Image analysis means" refers to the technology and algorithms used to analyze and extract features from input images of handwritten characters.

[0771] The "selection means" is a means for the user to select a desired font from a list of existing font styles provided within the system.

[0772] The "generation means" is a means for generating a new font based on the extracted handwritten character characteristics and the selected font style information.

[0773] "Providing means" refers to a method or interface for providing the generated new font to the user.

[0774] "Optical character recognition technology" is a technology that recognizes characters from scanned digital images of printed or handwritten characters and converts them into digital text.

[0775] "Deep learning technology" is a machine learning technology that uses multi-layered neural networks to perform pattern recognition and generation tasks.

[0776] This invention is a system that allows a user to create an original font using his / her own handwritten characters. This system is specifically implemented using the following hardware and software.

[0777] A user writes a letter by hand on a piece of paper, takes a photo of it with a smartphone or tablet camera, or uses a scanner to digitize it, and then uploads the digital image file of the handwritten letter to the system. For example, a user writes the word "hello" by hand and uploads the image to the system.

[0778] The device sends the uploaded image file of the handwritten characters to a server. The server then uses OCR (optical character recognition) technology to analyze the characteristics of the handwritten characters. This process uses software like Tesseract OCR to extract the shape and characteristics of each character. For example, the softness of the curve of the character "ko" and the shape of the double curve of the character "n" are analyzed and the characteristics are obtained as data.

[0779] The user selects a desired font, such as serif, sans serif, or handwritten font, from a list of existing font styles provided within the system. The selected font style information is sent from the terminal to the server.

[0780] The server uses AI technology to combine the analyzed handwritten character features with the characteristics of the template font selected by the user. Specifically, it uses deep learning models (e.g., GANs and Variational Autoencoders) to convert the handwritten character features to match the style of the template font. For example, it uses a method to preserve the soft curves of the user's handwritten characters while adjusting them to the straight lines and curves characteristic of the selected font. A generator defines the shape of each character and creates a path (drawing path) for the new font.

[0781] The server generates a font file based on the path information of the new font. This file is created in a standard font format (e.g., .ttf, .otf). The server provides the generated font file as a download link that users can access. Users can download the font file from this link and use it on various digital platforms and word processing software. For example, when you type "hello" using the generated original font, it will be displayed in a font that reflects the user's unique handwritten style.

[0782] As a concrete example, consider a case where a user writes "Thank you" by hand on a piece of paper, takes a photo of it with their smartphone, and uploads it to the system. The user selects a handwritten font, and once the new font is generated, the server provides a download link. The user downloads the font from the link, and when they type "Thank you" into a document, the original handwritten style is reflected.

[0783] An example of a prompt would be:

[0784] Upload your handwritten "Thank you" text, select a handwritten font, generate a new font file, and provide the download link for this font file.

[0785] This system allows users to easily create and use original fonts that take advantage of the characteristics of their handwritten characters.

[0786] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0787] Step 1:

[0788] Users can write by hand on paper and then take a photo of it with their smartphone or tablet camera, or use a scanner to digitize the text they have written on paper.

[0789] Input: Image file of handwritten characters

[0790] Output: Digital image file

[0791] What it does: A user writes the word "hello" by hand, takes a photo or scans the image, and then uploads the digital image file to the system.

[0792] Step 2:

[0793] The terminal transmits the digital image files uploaded by the user to the server.

[0794] Input: User-uploaded digital image files

[0795] Output: Digital image files sent to the server

[0796] Specific operation: The terminal automatically transmits the digital image file to the server through the upload form specified by the user.

[0797] Step 3:

[0798] The server receives the transmitted image file and uses OCR (optical character recognition) technology to analyze the handwritten characters and extract their features.

[0799] Input: Digital image files received by the server

[0800] Output: Analyzed character feature data

[0801] How it works: The server begins analyzing the image using software such as Tesseract OCR, analyzing the shape and characteristics of each character and extracting data such as the softness of the curve of "ko" and the shape of the double curve of "n."

[0802] Step 4:

[0803] The user selects the desired font from a list of existing font styles provided within the system.

[0804] Input: Selection information from the font style list

[0805] Output: User selected font information

[0806] Specific operation: The user uses the system interface to select the desired font, such as serif, sans serif, or handwritten font.

[0807] Step 5:

[0808] The terminal transmits the user's selection information to the server.

[0809] Input: User selected font information

[0810] Output: Font information sent to the server

[0811] Specific operation: The terminal automatically sends information about the font selected by the user to the server.

[0812] Step 6:

[0813] The server uses a deep learning model to generate a new font based on the analyzed handwritten character feature data and the font information selected by the user.

[0814] Input: Handwritten character feature data and user font selection information

[0815] Output: New font path information

[0816] How it works: The server uses deep learning techniques (e.g., GANs and Variational Autoencoders) to analyze the characteristics of handwritten characters and adjust them to match the style of the selected font. This process generates a new font that preserves the softness and curves of the handwritten characters.

[0817] Step 7:

[0818] The server creates a font file based on the path information of the generated font.

[0819] Input: Path information for the new font

[0820] Output: Generated font file (.ttf or .otf format)

[0821] Specific operation: The server uses a font generation library (e.g. FontForge) to create a font file based on the path information of the generated font.

[0822] Step 8:

[0823] The server generates a download link for providing the font file to the user and notifies the user.

[0824] Input: Generated font file

[0825] Output: Download link

[0826] Specific operation: The server stores the generated font file and notifies the user of the download link, through which the user can download the font file.

[0827] (Application example 1)

[0828] 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."

[0829] While conventional font generation systems can digitize a user's handwritten text and use it as a font, they lack the ability to replace and display text in real time. This makes it difficult for users to effectively use their handwritten text in advertisements and other visual content. Furthermore, there is a need for a system that can instantly apply a user's handwritten text style to enhance the individuality of advertisement content and visuals, providing a more personalized experience.

[0830] 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.

[0831] In this invention, the server includes an input means for a user to input handwritten characters, an image analysis means for analyzing the input handwritten characters and extracting features, a selection means for the user to select an existing font, a generation means for generating a new font by combining the extracted features with the selected font, a provision means for providing the generated font to the user, and a display means for replacing recognized characters with the user's handwritten font and displaying it in real time. This makes it possible to display an original font generated by a user using handwritten characters in real time and effectively utilize it in advertisements and other visual content.

[0832] "Input means" refers to a device or interface that allows a user to input handwritten characters into the system.

[0833] "Image analysis means" refers to the technology or device used to analyze input handwritten characters and extract their characteristics.

[0834] "Selection means" refers to a device or interface that allows a user to select an existing font from within the system.

[0835] "Generator" refers to the technology or device that combines the extracted features with the selected font to generate a new font.

[0836] "Providing means" refers to a device or interface for providing the generated font to the user.

[0837] The "display means" refers to a device or interface for converting the recognized characters into the user's handwritten font and displaying them in real time.

[0838] The present invention provides a system that allows users to generate original fonts using their own handwritten characters and apply them to advertisements and visual content in real time. Detailed embodiments of this system will be described below.

[0839] First, a user writes their own handwritten characters on a piece of paper and takes a photo of it with the camera on their smartphone or tablet. Alternatively, they can use a scanner to digitize the characters they have written on paper. This digitized image file is then uploaded to the server. For example, a user can write the word "hello" by hand and upload the image to the server.

[0840] The server receives the uploaded image file of the handwritten characters and uses image recognition technology to analyze the characteristics of the handwritten characters. In this process, OCR (Optical Character Recognition) technology is used to extract the shape and characteristics of each character. For example, the softness of the curve of the character "ko" or the shape of the double curve of the character "n" are analyzed and the characteristics are obtained as data.

[0841] The user then selects the desired font from a list of existing font styles provided within the system, including serif, sans serif, handwritten fonts, etc. This information is sent to the server.

[0842] The server then uses AI technology to combine the analyzed handwritten character characteristics with the characteristics of the template font selected by the user. Specifically, it uses a deep learning model to convert the handwritten character characteristics to match the style of the template font. For example, it preserves the soft curves of the user's handwritten character while adjusting them to the straight lines and curves characteristic of the selected font. A path (drawing path) for the generated new font is then created.

[0843] The new font is displayed in real time. Through a display device such as smart glasses, users can capture text on billboards or posters with their camera, and the recognized text is then replaced with the user's handwritten font. This allows users to apply their own handwritten style to advertisements, providing a personalized experience.

[0844] For example, when a user sees an advertising billboard at an event venue, the system can capture and analyze it with a camera and instantly display it in the user's handwritten font. An example of a prompt would be "Please display the following advertising text in my original font: Advertising text," and the system would follow the prompt and display it in the handwritten font.

[0845] This allows users to personalize advertisements and other visual content using their own handwritten characters, enhancing their visual individuality.The system is realized using hardware such as smart glasses (e.g., Google Glass), smartphones, and servers, and software such as Python, OpenCV, PIL, and pytesseract.

[0846] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0847] Step 1:

[0848] The user writes handwritten text on paper. The user takes a photo of their handwritten text with a smartphone or tablet camera or digitizes it using a scanner. The input is the written text on paper, and the output is a digital image file (e.g., JPEG, PNG).

[0849] Step 2:

[0850] The user uploads an image file of digitized handwritten characters to the server. The device (smartphone or tablet) provides an upload function and transfers the user's image file to the server. The input is a digital image file, and the output is image data on the server.

[0851] Step 3:

[0852] The server receives the uploaded image and uses image analysis to extract the characteristics of the handwritten characters. Specifically, it uses OCR (Optical Character Recognition) technology to detect the characters and obtain data on the shape and characteristics of each character (e.g., softness of curves, angle, line thickness). The input is a digital image file, and the output is character feature data.

[0853] Step 4:

[0854] The user selects the desired font from a list of font styles provided by the server. The terminal provides a font selection interface and sends the user's selection information to the server. The input is the user's selection action, and the output is the selected font information.

[0855] Step 5:

[0856] The server combines the extracted handwritten character features with the font style selected by the user to generate a new original font. A deep learning model is used to convert the handwritten character features to match the existing font style. The input is the feature data and the selected font information, and the output is the path information for the new font.

[0857] Step 6:

[0858] The server provides the generated font file (e.g., .ttf, .otf) to the user. As a means of providing the file, it generates a download link and notifies the user. The input is the path information of the font, and the output is the download link for the font file.

[0859] Step 7:

[0860] A user wears smart glasses and looks at an advertising billboard or poster in the city or at an event venue. The camera in the smart glasses captures the text of the advertisement and sends it to the server. The input is an image of the advertisement, and the output is the image data transferred to the server.

[0861] Step 8:

[0862] The server receives the captured image and replaces the recognized characters with the user's handwritten font. It analyzes the characters using image analysis means, applies the generated original font, and displays it in real time. The input is the advertisement image and the original font file, and the output is image data of the replaced characters.

[0863] Step 9:

[0864] The smart glasses display the replaced text to the user in real time, allowing the user to visually confirm that their own handwritten style is reflected in the advertisement. The input is image data of the replaced text, and the output is the text displayed on the smart glasses screen.

[0865] 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.

[0866] A specific embodiment of the present invention will be described. This invention combines a system that allows users to generate original fonts using their own handwritten characters with an emotion engine that recognizes the user's emotions. The processing of this program will be explained in natural language below, along with specific examples.

[0867] System Program Processing

[0868] Handwritten character input

[0869] A user writes their own handwritten text on paper and takes a photo of it with the camera on their smartphone or tablet, or uses a scanner to digitize the text they have written on paper. This digitized image file is then uploaded to the system. For example, a user can write "hello" and upload the image to the system.

[0870] Image analysis

[0871] The device sends the uploaded image file of the handwritten characters to the server. The server then passes the received image file to the image analysis module. This module uses OCR (optical character recognition) technology to extract the shape and characteristics of the handwritten characters. For example, it analyzes the softness of the curves in the character "ko" and the shape of the double curve in the character "n" and obtains the characteristics as data.

[0872] Font selection

[0873] The user selects the desired font from a list of existing font styles provided within the system, including serif, sans serif, handwritten fonts, etc. This information is sent from the terminal to the server.

[0874] Emotion recognition

[0875] The device captures or records the user's facial expressions and voice, and sends them to an emotion recognition engine. The emotion engine analyzes this data and identifies the user's current emotion. For example, if the user is smiling, it will be identified as "joy" or "enjoyment," and if the user has no expression, it will be identified as "neutral."

[0876] Generating a new font

[0877] The server uses AI technology to combine the analyzed handwritten character characteristics, the characteristics of the template font selected by the user, and the recognized emotion information. Using a deep learning model, the server converts the handwritten character characteristics to match the style of the template font. Furthermore, the server adjusts the font color and style (e.g., whether to use rounded, soft shapes or sharp shapes) depending on the emotion. For example, if the user is expressing the emotion "joy," the server adds bright colors and soft curves to the font.

[0878] Font provision

[0879] The server defines the path (drawing path) for each character based on the adjusted features and Bézier curves and generates a font file. This file is created in a standard font format (e.g., .ttf, .otf). The server provides the generated font file to the user as a download link. The user can download the font file from this link and use it on various digital platforms and word processing software.

[0880] example

[0881] For example, a user can handwrite "hello" and upload the image along with a photo of their own smiling face to the system. The server analyzes the characteristics of the handwritten characters and recognizes the emotion of "joy" from the smile. If the selected template font is a serif font, the server generates a font that preserves the soft curves of the handwritten characters while adding bright, cheerful colors. Users can download this original font and use it in their individual designs and writing.

[0882] This allows users to easily create and use original fonts that are more personalized based on their own handwritten characters and emotions.

[0883] The processing flow will be explained below.

[0884] Step 1:

[0885] Users write their own handwritten characters on paper, take a photo of it with the camera on their smartphone or tablet, or use a scanner to digitize the characters they have written on paper, and then upload this digitized image file to the system.

[0886] Step 2:

[0887] The terminal transmits the image file of the handwritten characters uploaded by the user to the server.

[0888] Step 3:

[0889] The server then passes the received image file to the image analysis module, which uses OCR (optical character recognition) technology to extract the shape and characteristics of the handwritten characters from the image file. Specifically, it obtains the outlines and curves of the characters as data.

[0890] Step 4:

[0891] The user selects the desired font from a list of existing font styles displayed in the system. For example, they can select serif, sans serif, handwritten font, etc. This selection information is sent from the terminal to the server.

[0892] Step 5:

[0893] The device captures the user's facial expressions and voice and sends them to the emotion engine on the server. For example, the device can capture the user's facial expressions with a smartphone camera and send them as they are.

[0894] Step 6:

[0895] The server's emotion engine analyzes the received facial and voice data to identify the user's emotion. For example, it recognizes the user's smile as the emotion of "joy."

[0896] Step 7:

[0897] The server generates a new font by combining the characteristics of handwritten characters and the selected font based on the emotion information obtained from the emotion engine. Using a deep learning model, the server preserves the characteristics of handwritten characters and adds design elements (e.g., color and style changes) according to the emotion.

[0898] Step 8:

[0899] The server defines the path (drawing path) for each character based on the adjusted features and Bézier curves, and generates a font file in a standard font format (e.g., .ttf, .otf).

[0900] Step 9:

[0901] The server will upload the generated original font file to the user's download page, where users can download the font file and install it on their PC or device.

[0902] Step 10:

[0903] Users can use the installed original fonts for document creation, graphic design, etc. For example, when they type "hello" using the generated font, it will be displayed in a unique font that reflects the user's handwriting and emotions.

[0904] Example 2

[0905] 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."

[0906] In conventional font generation systems using handwritten characters, it has been difficult to generate font styles that are linked to the user's emotions. In particular, it has been a technical challenge to automatically generate a font that matches the user's emotions while preserving the unique characteristics of handwritten characters. Furthermore, there has been no system that can recognize the user's emotions from their facial expressions or voice and reflect them in the font generation. This has resulted in the problem that users cannot easily generate more personalized original fonts.

[0907] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0908] In this invention, the server includes input means for a user to input handwritten characters, image analysis means for analyzing the input handwritten characters and extracting features, selection means for the user to select an existing font, emotion recognition means for identifying the user's emotion, generation means for generating a new font by combining the extracted features, the selected font, and the identified emotion information, and provision means for providing the generated font to the user. This makes it possible to automatically generate a font style corresponding to the user's emotion, allowing the user to easily create and use a more personalized original font linked to their emotion.

[0909] An "input means" is a means by which a user writes handwritten characters on paper and photographs them with the camera of a smartphone or tablet, or uses a scanner to upload the digitized image file to the system.

[0910] The "image analysis means" is a means for analyzing an uploaded image file of handwritten characters and extracting the shape and characteristics of the handwritten characters using OCR (optical character recognition) technology.

[0911] The "selection means" is a means by which a user selects a desired font from a list of existing font styles provided in the system and transmits the information to the system.

[0912] The "emotion recognition means" is a means for capturing the user's facial expressions and voice using a camera or microphone, analyzing the data, and identifying the user's current emotions.

[0913] The "generation means" is a means for generating a new font by combining the analyzed handwritten character characteristics, the selected font, and the identified emotional information using AI technology.

[0914] The "means for providing" is a means for generating a font file from the generated new font and providing the file to the user.

[0915] A specific embodiment of the present invention will be described. This invention combines a system that allows a user to generate an original font using their own handwritten characters with an emotion engine that recognizes the user's emotions.

[0916] System Configuration

[0917] This system is realized mainly by using the following hardware and software.

[0918] 1. Input method: The user inputs an image of handwritten characters using a device such as a smartphone, tablet, or scanner.

[0919] 2. Image analysis means: A server including an image analysis module using OCR (Optical Character Recognition) technology is used.

[0920] 3. Selection: The user selects an existing font using a web application or a dedicated system app.

[0921] 4. Emotion recognition means: using face recognition and voice analysis engines, for example devices with cameras and microphones.

[0922] 5. Generation method: A server that uses deep learning technology to generate new fonts.

[0923] 6. Provisioning means: A server that generates a download link to provide the generated font file to the user.

[0924] System Operation

[0925] Handwritten character input

[0926] A user writes their own handwritten characters on paper and takes a picture of the character using the camera on their smartphone or tablet. Alternatively, they can use a scanner to digitize the characters they have written on paper. This digitized image file is then uploaded to the system. Specifically, a user writes the word "hello" and uploads the image to the system.

[0927] Image analysis

[0928] The device sends the uploaded image file of the handwritten characters to the server. The server then passes the received image file to the image analysis module. This module uses OCR technology to extract the shape and characteristics of the handwritten characters. For example, it analyzes the softness of the curves in the character "ko" and the shape of the double curve in the character "n" and obtains the characteristics as data.

[0929] Font selection

[0930] The user selects the desired font from a list of existing font styles provided within the system, including serif, sans serif, handwritten fonts, etc. This information is sent from the terminal to the server.

[0931] Emotion recognition

[0932] The device captures or records the user's facial expressions and voice, and sends them to an emotion recognition engine. The emotion engine analyzes this data and identifies the user's current emotion. For example, if the user is smiling, it will be identified as "joy" or "enjoyment," and if the user has no expression, it will be identified as "neutral."

[0933] Generating a new font

[0934] The server uses AI technology to combine the analyzed handwritten character characteristics, the characteristics of the template font selected by the user, and the recognized emotion information. Using a deep learning model, the server converts the handwritten character characteristics to match the style of the template font. It also adjusts the font color and style according to the emotion. For example, if the user is expressing the emotion "joy," it adds bright colors and soft curves to the font.

[0935] Font provision

[0936] The server defines the path of each character based on the adjusted features and Bézier curves, and generates a font file. This file is created in a standard font format (e.g., .ttf, .otf). The server then provides the generated font file to the user as a download link. The user can download the font file from this link and use it on various digital platforms and word processing software.

[0937] Examples and prompts

[0938] For example, a user can handwrite "hello" and upload the image along with a photo of their smiling face to the system. The server analyzes the characteristics of the handwritten characters and recognizes the emotion of "joy" from the smile. If the selected template font is a serif font, the server generates a font that preserves the soft curves of the handwritten characters while adding bright, fun colors. Users can download this original font and use it in their own designs and writing.

[0939] An example of a prompt to input to a generative AI model is, "Please explain the process by which a user uploads an image of handwritten characters and emotional information to the system and generates an original font."

[0940] This allows users to easily create and use original fonts that are more personalized based on their own handwritten characters and emotions.

[0941] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0942] Step 1:

[0943] Users can write their own handwritten characters on paper and take a photo of it with their smartphone or tablet camera, or use a scanner to digitize the characters.

[0944] Input: Handwritten text on paper

[0945] Output: Digitized image files (e.g. JPEG, PNG)

[0946] What happens: The user writes "Hello" on a piece of paper and takes a photo of it with their smartphone camera.

[0947] Step 2:

[0948] The user uploads a photographed or scanned image file to the system.

[0949] Input: Digitized image file of handwritten characters

[0950] Output: Image file uploaded to the system

[0951] Specific operation: The user opens the system's upload screen, selects the "Hello" image file, and uploads it.

[0952] Step 3:

[0953] The terminal transmits the uploaded image file of the handwritten characters to the server.

[0954] Input: User uploaded image file

[0955] Output: Image file sent to the server

[0956] Specific operation: The device sends the "Hello" image file uploaded by the user to the server via Wi-Fi or mobile network.

[0957] Step 4:

[0958] The server passes the received image file to the image analysis module.

[0959] Input: Uploaded image file of handwritten characters

[0960] Output: Input data to the image analysis module

[0961] Specific operation: The server receives the image file and saves it in a directory for image analysis.

[0962] Step 5:

[0963] The server uses OCR technology to extract the shape and characteristics of handwritten characters.

[0964] Input: Image file of handwritten characters passed to the image analysis module

[0965] Output: Extracted character shapes and feature data

[0966] Specific operation: The server's OCR engine analyzes the image file and obtains data such as the softness of the curve of "ko" and the shape of the double curve of "n."

[0967] Step 6:

[0968] The user selects the desired font from a list of existing font styles provided within the system.

[0969] Input: List of font styles

[0970] Output: Selected font style data

[0971] What happens: The user selects a serif font from the system font selection screen.

[0972] Step 7:

[0973] The terminal transmits the font information selected by the user to the server.

[0974] Input: Selected font style data

[0975] Output: Font style data sent to the server

[0976] Specific operation: The device sends the serif font information selected by the user to the server.

[0977] Step 8:

[0978] The device captures or records the user's facial expressions and voice and sends them to an emotion recognition engine.

[0979] Input: User's facial image and voice data

[0980] Output: Input data to the emotion recognition engine

[0981] Specific operation: The device's camera captures the user's smile and the microphone records their voice.

[0982] Step 9:

[0983] The server's emotion recognition engine analyzes this data and identifies the user's current emotion.

[0984] Input: facial expression images and audio data

[0985] Output: Identified emotion information (e.g., joy, pleasure, neutral)

[0986] Specific operation: The emotion recognition engine analyzes smile image data and identifies the emotion of "happiness."

[0987] Step 10:

[0988] The server combines the analyzed handwritten character features, the selected font characteristics, and the recognized emotion information using AI technology.

[0989] Input: Handwritten character feature data, font style data, emotion information

[0990] Output: Data needed to generate a new font

[0991] What it does: Uses a deep learning model to adjust the softness of handwritten characters to match the characteristics of serif fonts.

[0992] Step 11:

[0993] The server adjusts the font color and style depending on the emotion.

[0994] Input: Emotion information, font generation data

[0995] Output: Emotion-aware font styles

[0996] What it does: Add bright colors and soft curves to fonts based on the emotion of "fun."

[0997] Step 12:

[0998] The server defines the path of each character based on the adjusted features and Bezier curves, and generates a font file.

[0999] Input: Adjusted character feature data, Bezier curve data

[1000] Output: Font file (e.g. .ttf, .otf)

[1001] What it does: It uses Bezier curves to define the path of each character and generates a font file.

[1002] Step 13:

[1003] The server provides the generated font file to the user as a download link.

[1004] Input: Generated font file

[1005] Output: Download link

[1006] What happens: The server uploads the font file to cloud storage and provides the link to the user.

[1007] (Application example 2)

[1008] 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."

[1009] Conventional font generation systems generate original fonts by combining the user's handwritten characters with template fonts. However, these systems do not take the user's emotions into account, which limits the user experience and makes it difficult to generate fonts that respond to individual emotions and situations. In addition, there is a lack of systems in physical stores that can provide information that responds to the individual needs and emotions of customers.

[1010] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1011] In this invention, the server includes an input means for a user to input handwritten characters, an image analysis means for analyzing the input handwritten characters and extracting features, a selection means for the user to select an existing font, an emotion recognition means for recognizing the user's emotion, a generation means for generating a new font by combining the extracted features, the selected font, and the recognized emotion, and a provision means for providing the generated font to the user. This allows a personalized font to be generated according to the customer's handwritten characters and emotions, making it possible to provide individual information in real time in a physical store.

[1012] A "user" is an individual or group who uses the system to input handwritten characters and generate an original font.

[1013] "Input means" refers to a device or interface that allows a user to digitize handwritten characters and provide them to the system. Examples include smartphones, tablets, scanners, etc.

[1014] "Image analysis means" is a component that has the function of analyzing the digital data of input handwritten characters and extracting their characteristics. Optical character recognition technology is often used.

[1015] The "selection means" refers to an interface or function that allows the user to select a desired template font from existing fonts in the system.

[1016] The "emotion recognition means" is a component that analyzes the user's facial expressions and voice data to identify the current emotion.

[1017] The "generator" is a component that generates a new original font using the extracted handwritten character features, the selected font characteristics, and the recognized emotion information. It often uses deep learning techniques.

[1018] "Providing means" refers to the interface and functionality for providing the generated font to the user and enabling download and use.

[1019] This invention combines a system that allows users to generate original fonts using their own handwritten characters with an emotion engine that recognizes the user's emotions. This system can be used in digital signage and in-store displays in brick-and-mortar stores to personalize customer experiences.

[1020] System Overview

[1021] The system includes the following components:

[1022] 1. An input method for users to input handwritten characters

[1023] 2. Image analysis method to analyze input handwritten characters and extract their features

[1024] 3. A means for the user to select an existing font

[1025] 4. Emotion Recognition Method to Recognize User Emotions

[1026] 5. A method for generating a new font by combining the extracted features, the selected font, and the recognized emotion.

[1027] 6. Means of providing the generated font to the user

[1028] Program processing

[1029] The system uses smartphones, digital signage (customer displays), cameras, and servers as its main hardware, and optical character recognition technology (Tesseract OCR) and an emotion recognition engine (Microsoft Azure Cognitive Services) as its software.

[1030] Handwritten character input

[1031] Users write their own handwritten characters on paper and take a photo of it with their smartphone camera, which is then uploaded to a server via a dedicated application.

[1032] Image analysis

[1033] The server receives the uploaded image file of the handwritten characters and uses Tesseract OCR to extract the shape and features of the handwritten characters.

[1034] Font selection

[1035] The user selects the desired font from a list of existing font styles provided in the system, and this information is sent from the terminal to the server.

[1036] Emotion recognition

[1037] The camera captures the user's facial expressions and sends the footage to a server, which uses Microsoft Azure Cognitive Services to analyze the facial expression data and identify the user's emotions.

[1038] Generating a new font

[1039] The server combines the extracted handwritten character features, the selected font characteristics, and the recognized emotion information using a deep learning model, converting the handwritten character features to match the style of the template font, and further adjusting the color and design based on the emotion.

[1040] Font provision

[1041] The server creates the generated font files in standard font formats (e.g., .ttf, .otf) and provides them to users as download links, which they can use in various digital platforms and word processing software.

[1042] Specific examples

[1043] For example, a user visiting a cafe handwrites "What's the coffee for today?" and takes a photo of it with their smartphone. At the same time, an in-store camera captures the user's smile, and the image is analyzed by the server. The handwritten text and smile are used to recognize the emotion of "enjoyment," and the handwritten text is converted into a casual style. The message "Today's coffee is a special blend!" is then displayed on the digital signage in a bright, friendly font.

[1044] Prompt Sentence Examples

[1045] Generate a friendly, bright, original font based on the user-provided handwritten characters and recognized emotion data. Apply colors and styles according to the emotion while preserving the characteristics of the handwritten characters.

[1046] This allows the system to generate original fonts based on the user's individual emotions and handwritten characters, personalizing information provided in physical stores and improving the customer experience.

[1047] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1048] Step 1:

[1049] The user writes their own handwritten characters on paper and takes a photo of them with their smartphone camera. This image is then uploaded to the server via a dedicated application. The input is an image file of the handwritten characters, and the output is the transfer of the image file to the server. Specifically, the user launches the application, follows the instructions to take a photo of the characters with the camera, and presses the upload button.

[1050] Step 2:

[1051] The server analyzes image files received from users using Tesseract OCR. The input is an image file of handwritten characters, and the output is data that extracts the shape and characteristics of the handwritten characters. Specifically, the server sends the image to the OCR module, which then captures the character's outline, line thickness, and other characteristics as digital data.

[1052] Step 3:

[1053] The user selects the desired font from a list of existing font styles provided within the system. The input is the user's font selection, and the output is the selected font style data. In concrete terms, the user selects a font from a drop-down list or thumbnail list within the application and presses the confirm button.

[1054] Step 4:

[1055] The camera captures the user's facial expression, and the video is sent to the server. The input is video data containing the user's facial expression, and the output is video data transferred to the server. Specifically, the camera installed in the physical store captures video at regular intervals and sends it to the server via the network.

[1056] Step 5:

[1057] The server uses Microsoft Azure Cognitive Services to analyze facial expression data and identify the user's emotions. The input is video data of the user's facial expressions, and the output is identified emotion data. Specifically, the server sends the video data to an emotion recognition engine, which identifies emotions such as "happiness" or "sadness" from the user's facial expressions and returns the results to the server as digital data.

[1058] Step 6:

[1059] The server combines the extracted handwritten character features, the selected font characteristics, and the recognized emotion information using a deep learning model. The input is the handwritten character feature data, the selected font style data, and the emotion data, and the output is the generation data for a new font. Specifically, the server inputs this data into the AI ​​model, and the model determines the font shape, color, and style based on it.

[1060] Step 7:

[1061] The server creates the generated font file in a standard font format (e.g., .ttf, .otf) and provides it to the user as a download link. The input is the generated font data, and the output is the download link. Specifically, the server converts the font data into a font file and sends a download link as a notification to the user's device.

[1062] This makes it clear how each processing step specifically operates and what data is input and output.

[1063] 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.

[1064] 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.

[1065] 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.

[1066] [Fourth embodiment]

[1067] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1068] 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.

[1069] 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).

[1070] 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.

[1071] 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.

[1072] 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).

[1073] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1074] 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.

[1075] 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.

[1076] 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.

[1077] 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.

[1078] 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.

[1079] 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."

[1080] A specific embodiment of the present invention will be described. This invention is a system for generating original fonts using characters handwritten by a user. The processing of the program will be explained in natural language below, with specific examples included.

[1081] System Program Processing

[1082] Handwritten character input

[1083] A user writes their own handwritten text on paper and takes a photo of it with the camera on their smartphone or tablet, or uses a scanner to digitize the text they have written on paper. This digitized image file is then uploaded to the system. For example, a user can write the word "hello" by hand and upload the image to the system.

[1084] Image analysis

[1085] The device sends the uploaded image file of the handwritten characters to the server. The server then uses image recognition technology to analyze the characteristics of the handwritten characters. In this process, OCR (optical character recognition) technology is used to extract the shape and characteristics of each character. For example, the softness of the curve of the character "ko" or the shape of the double curve of the character "n" are analyzed and the characteristics are obtained as data.

[1086] Font selection

[1087] The user selects the desired font from a list of existing font styles provided within the system, including serif, sans serif, handwritten fonts, etc. This information is sent from the terminal to the server.

[1088] Generating a new font

[1089] The server uses AI technology to combine the analyzed handwritten character features with the characteristics of the template font selected by the user. Specifically, it uses a deep learning model to convert the handwritten character features to match the style of the template font. For example, it preserves the soft curves of the user's handwritten characters while adjusting them to the straight lines and curves characteristic of the selected font. A generator defines the shape of each character and creates a path (drawing path) for the new font.

[1090] Font provision

[1091] The server generates a font file based on the path information of the new font. This file is created in a standard font format (e.g., .ttf, .otf). The server provides the generated font file to the user as a download link. The user can download the font file from this link and use it on various digital platforms and word processing software. For example, when typing "hello" using the generated original font, it will be displayed in a font that reflects the user's unique handwritten style.

[1092] In this way, users can easily create and use original fonts that take advantage of the characteristics of their own handwritten characters. This system is an effective means of creating high-quality digital fonts that meet the needs of individual users.

[1093] The processing flow will be explained below.

[1094] Step 1:

[1095] Users write their own handwriting on paper, take a photo of it with their smartphone or tablet camera, or use a scanner to digitize the writing on paper, and then upload this digitized image file to the system.

[1096] Step 2:

[1097] The terminal transmits the image file of the handwritten characters uploaded by the user to the server.

[1098] Step 3:

[1099] The server then passes the received image file to the image analysis module, which uses OCR (optical character recognition) technology to extract the shape and characteristics of the handwritten characters. For example, it obtains data on the softness of the curves in the character "ko" and the connection between the dots in the character "n."

[1100] Step 4:

[1101] The user selects the desired font style from a list of existing font styles displayed in the system. The choices include serif, sans serif, handwritten fonts, etc. This selection information is sent from the terminal to the server.

[1102] Step 5:

[1103] The server uses AI technology to combine the analyzed handwritten character characteristics with the characteristics of the template font selected by the user. Using a deep learning model, the server converts the handwritten character characteristics to match the style of the template font. Specifically, it adjusts the shape to match the consistency of the existing font while preserving the soft curves and unique line expression of the handwritten character.

[1104] Step 6:

[1105] The server defines the path (drawing path) for each character based on the adjusted features and Bézier curves. A font file is generated based on this path information. The font file is created in accordance with standard font formats (e.g., .ttf, .otf).

[1106] Step 7:

[1107] The server uploads the generated original font file to the user's download page.

[1108] Step 8:

[1109] Users click the download link to download the original font file, which they can then install on their PC or device.

[1110] Step 9:

[1111] Users can use the installed original fonts for document creation, graphic design, etc. The generated fonts are displayed in a beautiful and neat manner while retaining the individuality of the user's handwritten characters.

[1112] Example 1

[1113] 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."

[1114] Conventional font generation systems do not allow users to easily generate their own handwritten characters as digital fonts and use them individually. Furthermore, there is a lack of technology that faithfully reproduces the individual characteristics of handwritten characters while combining them with existing template fonts. This makes it difficult for users to digitize their own handwritten characters and use them across various platforms.

[1115] 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.

[1116] In this invention, the server includes means for uploading input handwritten characters to the system as an image file, means for transmitting the image file to the server, image analysis means for analyzing the input handwritten characters and extracting their characteristics, selection means for the user to select an existing font, generation means for generating a new font by combining the extracted characteristics with the selected font, and provision means for providing the generated font to the user. This enables users to easily generate original fonts that make use of the characteristics of their own handwritten characters and use them on various digital platforms and word processing software.

[1117] "Input means" refers to a means by which a user inputs handwritten characters and inputs them into the system in digital form.

[1118] "Uploading means" refers to a process or interface for a user to upload an image file of handwritten characters that has been photographed or digitized by the user to the system.

[1119] The "transmission means" is a means for transmitting the uploaded image file from the terminal to the server.

[1120] "Image analysis means" refers to the technology and algorithms used to analyze and extract features from input images of handwritten characters.

[1121] The "selection means" is a means for the user to select a desired font from a list of existing font styles provided within the system.

[1122] The "generation means" is a means for generating a new font based on the extracted handwritten character characteristics and the selected font style information.

[1123] "Providing means" refers to a method or interface for providing the generated new font to the user.

[1124] "Optical character recognition technology" is a technology that recognizes characters from scanned digital images of printed or handwritten characters and converts them into digital text.

[1125] "Deep learning technology" is a machine learning technology that uses multi-layered neural networks to perform pattern recognition and generation tasks.

[1126] This invention is a system that allows a user to create an original font using his / her own handwritten characters. This system is specifically implemented using the following hardware and software.

[1127] A user writes a letter by hand on a piece of paper, takes a photo of it with a smartphone or tablet camera, or uses a scanner to digitize it, and then uploads the digital image file of the handwritten letter to the system. For example, a user writes the word "hello" by hand and uploads the image to the system.

[1128] The device sends the uploaded image file of the handwritten characters to a server. The server then uses OCR (optical character recognition) technology to analyze the characteristics of the handwritten characters. This process uses software like Tesseract OCR to extract the shape and characteristics of each character. For example, the softness of the curve of the character "ko" and the shape of the double curve of the character "n" are analyzed and the characteristics are obtained as data.

[1129] The user selects a desired font, such as serif, sans serif, or handwritten font, from a list of existing font styles provided within the system. The selected font style information is sent from the terminal to the server.

[1130] The server uses AI technology to combine the analyzed handwritten character features with the characteristics of the template font selected by the user. Specifically, it uses deep learning models (e.g., GANs and Variational Autoencoders) to convert the handwritten character features to match the style of the template font. For example, it uses a method to preserve the soft curves of the user's handwritten characters while adjusting them to the straight lines and curves characteristic of the selected font. A generator defines the shape of each character and creates a path (drawing path) for the new font.

[1131] The server generates a font file based on the path information of the new font. This file is created in a standard font format (e.g., .ttf, .otf). The server provides the generated font file as a download link that users can access. Users can download the font file from this link and use it on various digital platforms and word processing software. For example, when you type "hello" using the generated original font, it will be displayed in a font that reflects the user's unique handwritten style.

[1132] As a concrete example, consider a case where a user writes "Thank you" by hand on a piece of paper, takes a photo of it with their smartphone, and uploads it to the system. The user selects a handwritten font, and once the new font is generated, the server provides a download link. The user downloads the font from the link, and when they type "Thank you" into a document, the original handwritten style is reflected.

[1133] An example of a prompt would be:

[1134] Upload your handwritten "Thank you" text, select a handwritten font, generate a new font file, and provide the download link for this font file.

[1135] This system allows users to easily create and use original fonts that take advantage of the characteristics of their handwritten characters.

[1136] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1137] Step 1:

[1138] Users can write by hand on paper and then take a photo of it with their smartphone or tablet camera, or use a scanner to digitize the text they have written on paper.

[1139] Input: Image file of handwritten characters

[1140] Output: Digital image file

[1141] What it does: A user writes the word "hello" by hand, takes a photo or scans the image, and then uploads the digital image file to the system.

[1142] Step 2:

[1143] The terminal transmits the digital image files uploaded by the user to the server.

[1144] Input: User-uploaded digital image files

[1145] Output: Digital image files sent to the server

[1146] Specific operation: The terminal automatically transmits the digital image file to the server through the upload form specified by the user.

[1147] Step 3:

[1148] The server receives the transmitted image file and uses OCR (optical character recognition) technology to analyze the handwritten characters and extract their features.

[1149] Input: Digital image files received by the server

[1150] Output: Analyzed character feature data

[1151] How it works: The server begins analyzing the image using software such as Tesseract OCR, analyzing the shape and characteristics of each character and extracting data such as the softness of the curve of "ko" and the shape of the double curve of "n."

[1152] Step 4:

[1153] The user selects the desired font from a list of existing font styles provided within the system.

[1154] Input: Selection information from the font style list

[1155] Output: User selected font information

[1156] Specific operation: The user uses the system interface to select the desired font, such as serif, sans serif, or handwritten font.

[1157] Step 5:

[1158] The terminal transmits the user's selection information to the server.

[1159] Input: User selected font information

[1160] Output: Font information sent to the server

[1161] Specific operation: The terminal automatically sends information about the font selected by the user to the server.

[1162] Step 6:

[1163] The server uses a deep learning model to generate a new font based on the analyzed handwritten character feature data and the font information selected by the user.

[1164] Input: Handwritten character feature data and user font selection information

[1165] Output: New font path information

[1166] How it works: The server uses deep learning techniques (e.g., GANs and Variational Autoencoders) to analyze the characteristics of handwritten characters and adjust them to match the style of the selected font. This process generates a new font that preserves the softness and curves of the handwritten characters.

[1167] Step 7:

[1168] The server creates a font file based on the path information of the generated font.

[1169] Input: Path information for the new font

[1170] Output: Generated font file (.ttf or .otf format)

[1171] Specific operation: The server uses a font generation library (e.g. FontForge) to create a font file based on the path information of the generated font.

[1172] Step 8:

[1173] The server generates a download link for providing the font file to the user and notifies the user.

[1174] Input: Generated font file

[1175] Output: Download link

[1176] Specific operation: The server stores the generated font file and notifies the user of the download link, through which the user can download the font file.

[1177] (Application example 1)

[1178] 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."

[1179] While conventional font generation systems can digitize a user's handwritten text and use it as a font, they lack the ability to replace and display text in real time. This makes it difficult for users to effectively use their handwritten text in advertisements and other visual content. Furthermore, there is a need for a system that can instantly apply a user's handwritten text style to enhance the individuality of advertisement content and visuals, providing a more personalized experience.

[1180] 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.

[1181] In this invention, the server includes an input means for a user to input handwritten characters, an image analysis means for analyzing the input handwritten characters and extracting features, a selection means for the user to select an existing font, a generation means for generating a new font by combining the extracted features with the selected font, a provision means for providing the generated font to the user, and a display means for replacing recognized characters with the user's handwritten font and displaying it in real time. This makes it possible to display an original font generated by a user using handwritten characters in real time and effectively utilize it in advertisements and other visual content.

[1182] "Input means" refers to a device or interface that allows a user to input handwritten characters into the system.

[1183] "Image analysis means" refers to the technology or device used to analyze input handwritten characters and extract their characteristics.

[1184] "Selection means" refers to a device or interface that allows a user to select an existing font from within the system.

[1185] "Generator" refers to the technology or device that combines the extracted features with the selected font to generate a new font.

[1186] "Providing means" refers to a device or interface for providing the generated font to the user.

[1187] The "display means" refers to a device or interface for converting the recognized characters into the user's handwritten font and displaying them in real time.

[1188] The present invention provides a system that allows users to generate original fonts using their own handwritten characters and apply them to advertisements and visual content in real time. Detailed embodiments of this system will be described below.

[1189] First, a user writes their own handwritten characters on a piece of paper and takes a photo of it with the camera on their smartphone or tablet. Alternatively, they can use a scanner to digitize the characters they have written on paper. This digitized image file is then uploaded to the server. For example, a user can write the word "hello" by hand and upload the image to the server.

[1190] The server receives the uploaded image file of the handwritten characters and uses image recognition technology to analyze the characteristics of the handwritten characters. In this process, OCR (Optical Character Recognition) technology is used to extract the shape and characteristics of each character. For example, the softness of the curve of the character "ko" or the shape of the double curve of the character "n" are analyzed and the characteristics are obtained as data.

[1191] The user then selects the desired font from a list of existing font styles provided within the system, including serif, sans serif, handwritten fonts, etc. This information is sent to the server.

[1192] The server then uses AI technology to combine the analyzed handwritten character characteristics with the characteristics of the template font selected by the user. Specifically, it uses a deep learning model to convert the handwritten character characteristics to match the style of the template font. For example, it preserves the soft curves of the user's handwritten character while adjusting them to the straight lines and curves characteristic of the selected font. A path (drawing path) for the generated new font is then created.

[1193] The new font is displayed in real time. Through a display device such as smart glasses, users can capture text on billboards or posters with their camera, and the recognized text is then replaced with the user's handwritten font. This allows users to apply their own handwritten style to advertisements, providing a personalized experience.

[1194] For example, when a user sees an advertising billboard at an event venue, the system can capture and analyze it with a camera and instantly display it in the user's handwritten font. An example of a prompt would be "Please display the following advertising text in my original font: Advertising text," and the system would follow the prompt and display it in the handwritten font.

[1195] This allows users to personalize advertisements and other visual content using their own handwritten characters, enhancing their visual individuality.The system is realized using hardware such as smart glasses (e.g., Google Glass), smartphones, and servers, and software such as Python, OpenCV, PIL, and pytesseract.

[1196] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1197] Step 1:

[1198] The user writes handwritten text on paper. The user takes a photo of their handwritten text with a smartphone or tablet camera or digitizes it using a scanner. The input is the written text on paper, and the output is a digital image file (e.g., JPEG, PNG).

[1199] Step 2:

[1200] The user uploads an image file of digitized handwritten characters to the server. The device (smartphone or tablet) provides an upload function and transfers the user's image file to the server. The input is a digital image file, and the output is image data on the server.

[1201] Step 3:

[1202] The server receives the uploaded image and uses image analysis to extract the characteristics of the handwritten characters. Specifically, it uses OCR (Optical Character Recognition) technology to detect the characters and obtain data on the shape and characteristics of each character (e.g., softness of curves, angle, line thickness). The input is a digital image file, and the output is character feature data.

[1203] Step 4:

[1204] The user selects the desired font from a list of font styles provided by the server. The terminal provides a font selection interface and sends the user's selection information to the server. The input is the user's selection action, and the output is the selected font information.

[1205] Step 5:

[1206] The server combines the extracted handwritten character features with the font style selected by the user to generate a new original font. A deep learning model is used to convert the handwritten character features to match the existing font style. The input is the feature data and the selected font information, and the output is the path information for the new font.

[1207] Step 6:

[1208] The server provides the generated font file (e.g., .ttf, .otf) to the user. As a means of providing the file, it generates a download link and notifies the user. The input is the path information of the font, and the output is the download link for the font file.

[1209] Step 7:

[1210] A user wears smart glasses and looks at an advertising billboard or poster in the city or at an event venue. The camera in the smart glasses captures the text of the advertisement and sends it to the server. The input is an image of the advertisement, and the output is the image data transferred to the server.

[1211] Step 8:

[1212] The server receives the captured image and replaces the recognized characters with the user's handwritten font. It analyzes the characters using image analysis means, applies the generated original font, and displays it in real time. The input is the advertisement image and the original font file, and the output is image data of the replaced characters.

[1213] Step 9:

[1214] The smart glasses display the replaced text to the user in real time, allowing the user to visually confirm that their own handwritten style is reflected in the advertisement. The input is image data of the replaced text, and the output is the text displayed on the smart glasses screen.

[1215] 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.

[1216] A specific embodiment of the present invention will be described. This invention combines a system that allows users to generate original fonts using their own handwritten characters with an emotion engine that recognizes the user's emotions. The processing of this program will be explained in natural language below, along with specific examples.

[1217] System Program Processing

[1218] Handwritten character input

[1219] A user writes their own handwritten text on paper and takes a photo of it with the camera on their smartphone or tablet, or uses a scanner to digitize the text they have written on paper. This digitized image file is then uploaded to the system. For example, a user can write "hello" and upload the image to the system.

[1220] Image analysis

[1221] The device sends the uploaded image file of the handwritten characters to the server. The server then passes the received image file to the image analysis module. This module uses OCR (optical character recognition) technology to extract the shape and characteristics of the handwritten characters. For example, it analyzes the softness of the curves in the character "ko" and the shape of the double curve in the character "n" and obtains the characteristics as data.

[1222] Font selection

[1223] The user selects the desired font from a list of existing font styles provided within the system, including serif, sans serif, handwritten fonts, etc. This information is sent from the terminal to the server.

[1224] Emotion recognition

[1225] The device captures or records the user's facial expressions and voice, and sends them to an emotion recognition engine. The emotion engine analyzes this data and identifies the user's current emotion. For example, if the user is smiling, it will be identified as "joy" or "enjoyment," and if the user has no expression, it will be identified as "neutral."

[1226] Generating a new font

[1227] The server uses AI technology to combine the analyzed handwritten character characteristics, the characteristics of the template font selected by the user, and the recognized emotion information. Using a deep learning model, the server converts the handwritten character characteristics to match the style of the template font. Furthermore, the server adjusts the font color and style (e.g., whether to use rounded, soft shapes or sharp shapes) depending on the emotion. For example, if the user is expressing the emotion "joy," the server adds bright colors and soft curves to the font.

[1228] Font provision

[1229] The server defines the path (drawing path) for each character based on the adjusted features and Bézier curves and generates a font file. This file is created in a standard font format (e.g., .ttf, .otf). The server provides the generated font file to the user as a download link. The user can download the font file from this link and use it on various digital platforms and word processing software.

[1230] example

[1231] For example, a user can handwrite "hello" and upload the image along with a photo of their own smiling face to the system. The server analyzes the characteristics of the handwritten characters and recognizes the emotion of "joy" from the smile. If the selected template font is a serif font, the server generates a font that preserves the soft curves of the handwritten characters while adding bright, cheerful colors. Users can download this original font and use it in their individual designs and writing.

[1232] This allows users to easily create and use original fonts that are more personalized based on their own handwritten characters and emotions.

[1233] The processing flow will be explained below.

[1234] Step 1:

[1235] Users write their own handwritten characters on paper, take a photo of it with the camera on their smartphone or tablet, or use a scanner to digitize the characters they have written on paper, and then upload this digitized image file to the system.

[1236] Step 2:

[1237] The terminal transmits the image file of the handwritten characters uploaded by the user to the server.

[1238] Step 3:

[1239] The server then passes the received image file to the image analysis module, which uses OCR (optical character recognition) technology to extract the shape and characteristics of the handwritten characters from the image file. Specifically, it obtains the outlines and curves of the characters as data.

[1240] Step 4:

[1241] The user selects the desired font from a list of existing font styles displayed in the system. For example, they can select serif, sans serif, handwritten font, etc. This selection information is sent from the terminal to the server.

[1242] Step 5:

[1243] The device captures the user's facial expressions and voice and sends them to the emotion engine on the server. For example, the device can capture the user's facial expressions with a smartphone camera and send them as they are.

[1244] Step 6:

[1245] The server's emotion engine analyzes the received facial and voice data to identify the user's emotion. For example, it recognizes the user's smile as the emotion of "joy."

[1246] Step 7:

[1247] The server generates a new font by combining the characteristics of handwritten characters and the selected font based on the emotion information obtained from the emotion engine. Using a deep learning model, the server preserves the characteristics of handwritten characters and adds design elements (e.g., color and style changes) according to the emotion.

[1248] Step 8:

[1249] The server defines the path (drawing path) for each character based on the adjusted features and Bézier curves, and generates a font file in a standard font format (e.g., .ttf, .otf).

[1250] Step 9:

[1251] The server will upload the generated original font file to the user's download page, where users can download the font file and install it on their PC or device.

[1252] Step 10:

[1253] Users can use the installed original fonts for document creation, graphic design, etc. For example, when they type "hello" using the generated font, it will be displayed in a unique font that reflects the user's handwriting and emotions.

[1254] Example 2

[1255] 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."

[1256] In conventional font generation systems using handwritten characters, it has been difficult to generate font styles that are linked to the user's emotions. In particular, it has been a technical challenge to automatically generate a font that matches the user's emotions while preserving the unique characteristics of handwritten characters. Furthermore, there has been no system that can recognize the user's emotions from their facial expressions or voice and reflect them in the font generation. This has resulted in the problem that users cannot easily generate more personalized original fonts.

[1257] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1258] In this invention, the server includes input means for a user to input handwritten characters, image analysis means for analyzing the input handwritten characters and extracting features, selection means for the user to select an existing font, emotion recognition means for identifying the user's emotion, generation means for generating a new font by combining the extracted features, the selected font, and the identified emotion information, and provision means for providing the generated font to the user. This makes it possible to automatically generate a font style corresponding to the user's emotion, allowing the user to easily create and use a more personalized original font linked to their emotion.

[1259] An "input means" is a means by which a user writes handwritten characters on paper and photographs them with the camera of a smartphone or tablet, or uses a scanner to upload the digitized image file to the system.

[1260] The "image analysis means" is a means for analyzing an uploaded image file of handwritten characters and extracting the shape and characteristics of the handwritten characters using OCR (optical character recognition) technology.

[1261] The "selection means" is a means by which a user selects a desired font from a list of existing font styles provided in the system and transmits the information to the system.

[1262] The "emotion recognition means" is a means for capturing the user's facial expressions and voice using a camera or microphone, analyzing the data, and identifying the user's current emotions.

[1263] The "generation means" is a means for generating a new font by combining the analyzed handwritten character characteristics, the selected font, and the identified emotional information using AI technology.

[1264] The "means for providing" is a means for generating a font file from the generated new font and providing the file to the user.

[1265] A specific embodiment of the present invention will be described. This invention combines a system that allows a user to generate an original font using their own handwritten characters with an emotion engine that recognizes the user's emotions.

[1266] System Configuration

[1267] This system is realized mainly by using the following hardware and software.

[1268] 1. Input method: The user inputs an image of handwritten characters using a device such as a smartphone, tablet, or scanner.

[1269] 2. Image analysis means: A server including an image analysis module using OCR (Optical Character Recognition) technology is used.

[1270] 3. Selection: The user selects an existing font using a web application or a dedicated system app.

[1271] 4. Emotion recognition means: using face recognition and voice analysis engines, for example devices with cameras and microphones.

[1272] 5. Generation method: A server that uses deep learning technology to generate new fonts.

[1273] 6. Provisioning means: A server that generates a download link to provide the generated font file to the user.

[1274] System Operation

[1275] Handwritten character input

[1276] A user writes their own handwritten characters on paper and takes a picture of the character using the camera on their smartphone or tablet. Alternatively, they can use a scanner to digitize the characters they have written on paper. This digitized image file is then uploaded to the system. Specifically, a user writes the word "hello" and uploads the image to the system.

[1277] Image analysis

[1278] The device sends the uploaded image file of the handwritten characters to the server. The server then passes the received image file to the image analysis module. This module uses OCR technology to extract the shape and characteristics of the handwritten characters. For example, it analyzes the softness of the curves in the character "ko" and the shape of the double curve in the character "n" and obtains the characteristics as data.

[1279] Font selection

[1280] The user selects the desired font from a list of existing font styles provided within the system, including serif, sans serif, handwritten fonts, etc. This information is sent from the terminal to the server.

[1281] Emotion recognition

[1282] The device captures or records the user's facial expressions and voice, and sends them to an emotion recognition engine. The emotion engine analyzes this data and identifies the user's current emotion. For example, if the user is smiling, it will be identified as "joy" or "enjoyment," and if the user has no expression, it will be identified as "neutral."

[1283] Generating a new font

[1284] The server uses AI technology to combine the analyzed handwritten character characteristics, the characteristics of the template font selected by the user, and the recognized emotion information. Using a deep learning model, the server converts the handwritten character characteristics to match the style of the template font. It also adjusts the font color and style according to the emotion. For example, if the user is expressing the emotion "joy," it adds bright colors and soft curves to the font.

[1285] Font provision

[1286] The server defines the path of each character based on the adjusted features and Bézier curves, and generates a font file. This file is created in a standard font format (e.g., .ttf, .otf). The server then provides the generated font file to the user as a download link. The user can download the font file from this link and use it on various digital platforms and word processing software.

[1287] Examples and prompts

[1288] For example, a user can handwrite "hello" and upload the image along with a photo of their smiling face to the system. The server analyzes the characteristics of the handwritten characters and recognizes the emotion of "joy" from the smile. If the selected template font is a serif font, the server generates a font that preserves the soft curves of the handwritten characters while adding bright, fun colors. Users can download this original font and use it in their own designs and writing.

[1289] An example of a prompt to input to a generative AI model is, "Please explain the process by which a user uploads an image of handwritten characters and emotional information to the system and generates an original font."

[1290] This allows users to easily create and use original fonts that are more personalized based on their own handwritten characters and emotions.

[1291] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1292] Step 1:

[1293] Users can write their own handwritten characters on paper and take a photo of it with their smartphone or tablet camera, or use a scanner to digitize the characters.

[1294] Input: Handwritten text on paper

[1295] Output: Digitized image files (e.g. JPEG, PNG)

[1296] What happens: The user writes "Hello" on a piece of paper and takes a photo of it with their smartphone camera.

[1297] Step 2:

[1298] The user uploads a photographed or scanned image file to the system.

[1299] Input: Digitized image file of handwritten characters

[1300] Output: Image file uploaded to the system

[1301] Specific operation: The user opens the system's upload screen, selects the "Hello" image file, and uploads it.

[1302] Step 3:

[1303] The terminal transmits the uploaded image file of the handwritten characters to the server.

[1304] Input: User uploaded image file

[1305] Output: Image file sent to the server

[1306] Specific operation: The device sends the "Hello" image file uploaded by the user to the server via Wi-Fi or mobile network.

[1307] Step 4:

[1308] The server passes the received image file to the image analysis module.

[1309] Input: Uploaded image file of handwritten characters

[1310] Output: Input data to the image analysis module

[1311] Specific operation: The server receives the image file and saves it in a directory for image analysis.

[1312] Step 5:

[1313] The server uses OCR technology to extract the shape and characteristics of handwritten characters.

[1314] Input: Image file of handwritten characters passed to the image analysis module

[1315] Output: Extracted character shapes and feature data

[1316] Specific operation: The server's OCR engine analyzes the image file and obtains data such as the softness of the curve of "ko" and the shape of the double curve of "n."

[1317] Step 6:

[1318] The user selects the desired font from a list of existing font styles provided within the system.

[1319] Input: List of font styles

[1320] Output: Selected font style data

[1321] What happens: The user selects a serif font from the system font selection screen.

[1322] Step 7:

[1323] The terminal transmits the font information selected by the user to the server.

[1324] Input: Selected font style data

[1325] Output: Font style data sent to the server

[1326] Specific operation: The device sends the serif font information selected by the user to the server.

[1327] Step 8:

[1328] The device captures or records the user's facial expressions and voice and sends them to an emotion recognition engine.

[1329] Input: User's facial image and voice data

[1330] Output: Input data to the emotion recognition engine

[1331] Specific operation: The device's camera captures the user's smile and the microphone records their voice.

[1332] Step 9:

[1333] The server's emotion recognition engine analyzes this data and identifies the user's current emotion.

[1334] Input: facial expression images and audio data

[1335] Output: Identified emotion information (e.g., joy, pleasure, neutral)

[1336] Specific operation: The emotion recognition engine analyzes smile image data and identifies the emotion of "happiness."

[1337] Step 10:

[1338] The server combines the analyzed handwritten character features, the selected font characteristics, and the recognized emotion information using AI technology.

[1339] Input: Handwritten character feature data, font style data, emotion information

[1340] Output: Data needed to generate a new font

[1341] What it does: Uses a deep learning model to adjust the softness of handwritten characters to match the characteristics of serif fonts.

[1342] Step 11:

[1343] The server adjusts the font color and style depending on the emotion.

[1344] Input: Emotion information, font generation data

[1345] Output: Emotion-aware font styles

[1346] What it does: Add bright colors and soft curves to fonts based on the emotion of "fun."

[1347] Step 12:

[1348] The server defines the path of each character based on the adjusted features and Bezier curves, and generates a font file.

[1349] Input: Adjusted character feature data, Bezier curve data

[1350] Output: Font file (e.g. .ttf, .otf)

[1351] What it does: It uses Bezier curves to define the path of each character and generates a font file.

[1352] Step 13:

[1353] The server provides the generated font file to the user as a download link.

[1354] Input: Generated font file

[1355] Output: Download link

[1356] What happens: The server uploads the font file to cloud storage and provides the link to the user.

[1357] (Application example 2)

[1358] 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."

[1359] Conventional font generation systems generate original fonts by combining the user's handwritten characters with template fonts. However, these systems do not take the user's emotions into account, which limits the user experience and makes it difficult to generate fonts that respond to individual emotions and situations. In addition, there is a lack of systems in physical stores that can provide information that responds to the individual needs and emotions of customers.

[1360] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1361] In this invention, the server includes an input means for a user to input handwritten characters, an image analysis means for analyzing the input handwritten characters and extracting features, a selection means for the user to select an existing font, an emotion recognition means for recognizing the user's emotion, a generation means for generating a new font by combining the extracted features, the selected font, and the recognized emotion, and a provision means for providing the generated font to the user. This allows a personalized font to be generated according to the customer's handwritten characters and emotions, making it possible to provide individual information in real time in a physical store.

[1362] A "user" is an individual or group who uses the system to input handwritten characters and generate an original font.

[1363] "Input means" refers to a device or interface that allows a user to digitize handwritten characters and provide them to the system. Examples include smartphones, tablets, scanners, etc.

[1364] "Image analysis means" is a component that has the function of analyzing the digital data of input handwritten characters and extracting their characteristics. Optical character recognition technology is often used.

[1365] The "selection means" refers to an interface or function that allows the user to select a desired template font from existing fonts in the system.

[1366] The "emotion recognition means" is a component that analyzes the user's facial expressions and voice data to identify the current emotion.

[1367] The "generator" is a component that generates a new original font using the extracted handwritten character features, the selected font characteristics, and the recognized emotion information. It often uses deep learning techniques.

[1368] "Providing means" refers to the interface and functionality for providing the generated font to the user and enabling download and use.

[1369] This invention combines a system that allows users to generate original fonts using their own handwritten characters with an emotion engine that recognizes the user's emotions. This system can be used in digital signage and in-store displays in brick-and-mortar stores to personalize customer experiences.

[1370] System Overview

[1371] The system includes the following components:

[1372] 1. An input method for users to input handwritten characters

[1373] 2. Image analysis method to analyze input handwritten characters and extract their features

[1374] 3. A means for the user to select an existing font

[1375] 4. Emotion Recognition Method to Recognize User Emotions

[1376] 5. A method for generating a new font by combining the extracted features, the selected font, and the recognized emotion.

[1377] 6. Means of providing the generated font to the user

[1378] Program processing

[1379] The system uses smartphones, digital signage (customer displays), cameras, and servers as its main hardware, and optical character recognition technology (Tesseract OCR) and an emotion recognition engine (Microsoft Azure Cognitive Services) as its software.

[1380] Handwritten character input

[1381] Users write their own handwritten characters on paper and take a photo of it with their smartphone camera, which is then uploaded to a server via a dedicated application.

[1382] Image analysis

[1383] The server receives the uploaded image file of the handwritten characters and uses Tesseract OCR to extract the shape and features of the handwritten characters.

[1384] Font selection

[1385] The user selects the desired font from a list of existing font styles provided in the system, and this information is sent from the terminal to the server.

[1386] Emotion recognition

[1387] The camera captures the user's facial expressions and sends the footage to a server, which uses Microsoft Azure Cognitive Services to analyze the facial expression data and identify the user's emotions.

[1388] Generating a new font

[1389] The server combines the extracted handwritten character features, the selected font characteristics, and the recognized emotion information using a deep learning model, converting the handwritten character features to match the style of the template font, and further adjusting the color and design based on the emotion.

[1390] Font provision

[1391] The server creates the generated font files in standard font formats (e.g., .ttf, .otf) and provides them to users as download links, which they can use in various digital platforms and word processing software.

[1392] Specific examples

[1393] For example, a user visiting a cafe handwrites "What's the coffee for today?" and takes a photo of it with their smartphone. At the same time, an in-store camera captures the user's smile, and the image is analyzed by the server. The handwritten text and smile are used to recognize the emotion of "enjoyment," and the handwritten text is converted into a casual style. The message "Today's coffee is a special blend!" is then displayed on the digital signage in a bright, friendly font.

[1394] Prompt Sentence Examples

[1395] Generate a friendly, bright, original font based on the user-provided handwritten characters and recognized emotion data. Apply colors and styles according to the emotion while preserving the characteristics of the handwritten characters.

[1396] This allows the system to generate original fonts based on the user's individual emotions and handwritten characters, personalizing information provided in physical stores and improving the customer experience.

[1397] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1398] Step 1:

[1399] The user writes their own handwritten characters on paper and takes a photo of them with their smartphone camera. This image is then uploaded to the server via a dedicated application. The input is an image file of the handwritten characters, and the output is the transfer of the image file to the server. Specifically, the user launches the application, follows the instructions to take a photo of the characters with the camera, and presses the upload button.

[1400] Step 2:

[1401] The server analyzes image files received from users using Tesseract OCR. The input is an image file of handwritten characters, and the output is data that extracts the shape and characteristics of the handwritten characters. Specifically, the server sends the image to the OCR module, which then captures the character's outline, line thickness, and other characteristics as digital data.

[1402] Step 3:

[1403] The user selects the desired font from a list of existing font styles provided within the system. The input is the user's font selection, and the output is the selected font style data. In concrete terms, the user selects a font from a drop-down list or thumbnail list within the application and presses the confirm button.

[1404] Step 4:

[1405] The camera captures the user's facial expression, and the video is sent to the server. The input is video data containing the user's facial expression, and the output is video data transferred to the server. Specifically, the camera installed in the physical store captures video at regular intervals and sends it to the server via the network.

[1406] Step 5:

[1407] The server uses Microsoft Azure Cognitive Services to analyze facial expression data and identify the user's emotions. The input is video data of the user's facial expressions, and the output is identified emotion data. Specifically, the server sends the video data to an emotion recognition engine, which identifies emotions such as "happiness" or "sadness" from the user's facial expressions and returns the results to the server as digital data.

[1408] Step 6:

[1409] The server combines the extracted handwritten character features, the selected font characteristics, and the recognized emotion information using a deep learning model. The input is the handwritten character feature data, the selected font style data, and the emotion data, and the output is the generation data for a new font. Specifically, the server inputs this data into the AI ​​model, and the model determines the font shape, color, and style based on it.

[1410] Step 7:

[1411] The server creates the generated font file in a standard font format (e.g., .ttf, .otf) and provides it to the user as a download link. The input is the generated font data, and the output is the download link. Specifically, the server converts the font data into a font file and sends a download link as a notification to the user's device.

[1412] This makes it clear how each processing step specifically operates and what data is input and output.

[1413] 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.

[1414] 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.

[1415] 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.

[1416] 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.

[1417] 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.

[1418] 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.

[1419] 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).

[1420] 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.

[1421] 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."

[1422] 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.

[1423] 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).

[1424] 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.

[1425] 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.

[1426] 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.

[1427] 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.

[1428] 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.

[1429] 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.

[1430] 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.

[1431] 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.

[1432] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1433] 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.

[1434] The following is further disclosed regarding the above embodiment.

[1435] (Claim 1)

[1436] an input means for a user to input handwritten characters;

[1437] an image analysis means for analyzing the input handwritten characters and extracting their features;

[1438] a selection means for a user to select an existing font;

[1439] a generating means for combining the extracted features with the selected font to generate a new font;

[1440] The system includes a providing means for providing the generated font to a user.

[1441] (Claim 2)

[1442] 10. The system of claim 1, wherein the image analysis means uses optical character recognition techniques.

[1443] (Claim 3)

[1444] The system of claim 1, wherein the generating means uses deep learning technology to extract features of handwritten characters and convert them to match the style of the selected font.

[1445] (Claim 4)

[1446] 2. The system according to claim 1, wherein the providing means provides the generated font in a font file format.

[1447] "Example 1"

[1448] (Claim 1)

[1449] an input means for a user to input handwritten characters;

[1450] A means for uploading the input handwritten characters to the system as an image file;

[1451] means for transmitting the image file to a server;

[1452] an image analysis means for analyzing the input handwritten characters and extracting their features;

[1453] a selection means for a user to select an existing font;

[1454] a generating means for combining the extracted features with the selected font to generate a new font;

[1455] The system includes a providing means for providing the generated font to a user.

[1456] (Claim 2)

[1457] 10. The system of claim 1, wherein the image analysis means uses optical character recognition techniques.

[1458] (Claim 3)

[1459] The system of claim 1, wherein the generating means uses deep learning technology to extract features of handwritten characters and convert them to match the style of the selected font.

[1460] "Application Example 1"

[1461] (Claim 1)

[1462] an input means for a user to input handwritten characters;

[1463] an image analysis means for analyzing the input handwritten characters and extracting their features;

[1464] a selection means for a user to select an existing font;

[1465] a generating means for combining the extracted features with the selected font to generate a new font;

[1466] providing means for providing the generated font to a user;

[1467] a display means for displaying the recognized characters in real time by replacing them with a user's handwritten font;

[1468] A system including:

[1469] (Claim 2)

[1470] 10. The system of claim 1, wherein the image analysis means uses optical character recognition techniques.

[1471] (Claim 3)

[1472] The system of claim 1, wherein the generating means uses deep learning technology to extract features of handwritten characters and convert them to match the style of the selected font.

[1473] "Example 2: Combining Emotion Engines"

[1474] (Claim 1)

[1475] an input means for a user to input handwritten characters;

[1476] an image analysis means for analyzing the input handwritten characters and extracting their features;

[1477] a selection means for a user to select an existing font;

[1478] emotion recognition means for identifying an emotion of a user;

[1479] a generating means for generating a new font by combining the extracted features, the selected font, and the identified emotion information;

[1480] The system includes a providing means for providing the generated font to a user.

[1481] (Claim 2)

[1482] 10. The system of claim 1, wherein the image analysis means uses optical character recognition techniques.

[1483] (Claim 3)

[1484] The system of claim 1, wherein the generating means uses deep learning technology to extract features of handwritten characters and convert them to match the style of the selected font.

[1485] "Application example 2 when combining emotion engines"

[1486] (Claim 1)

[1487] an input means for a user to input handwritten characters;

[1488] an image analysis means for analyzing the input handwritten characters and extracting their features;

[1489] a selection means for a user to select an existing font;

[1490] emotion recognition means for recognizing an emotion of a user;

[1491] a generating means for generating a new font by combining the extracted features, the selected font, and the recognized emotion;

[1492] The system includes a providing means for providing the generated font to a user.

[1493] (Claim 2)

[1494] 10. The system of claim 1, wherein the image analysis means uses optical character recognition techniques.

[1495] (Claim 3)

[1496] The system of claim 1, wherein the generating means uses deep learning techniques to extract features of handwritten characters, convert them to match the style of the selected font, and further adjust them based on emotion. [Explanation of symbols]

[1497] 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. an input means for a user to input handwritten characters; an image analysis means for analyzing the input handwritten characters and extracting their features; a selection means for a user to select an existing font; a generating means for combining the extracted features with the selected font to generate a new font; The system includes a providing means for providing the generated font to a user.

2. 10. The system of claim 1, wherein the image analysis means uses optical character recognition techniques.

3. The system of claim 1 , wherein the generating means uses deep learning technology to extract features of handwritten characters and convert them to match the style of the selected font.

4. 2. The system according to claim 1, wherein the providing means provides the generated font in a font file format.

Images