System

Abstract

A system is provided.SOLUTION: The system includes a means for collecting past handwritten data and analyzing the data to learn features of handwritten characters, a means for receiving character data selected by a user using a line-of-sight input device, a means for converting the received character data into a handwritten style based on the learned features of the handwritten characters, and a means for outputting the converted handwritten character data.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] Handwritten characters are often used as a means of self-expression, and are particularly effective in letters and messages, as they convey warmth and individuality to the reader. However, people with weakened muscles, such as those with muscular dystrophy, have difficulty writing by hand. While it is possible to input characters using an eye-gaze input device, the fonts that can be used are limited, making it difficult to express the warmth and individuality of handwriting. To solve this problem, there is a need to develop a system that converts handwritten character data into handwritten-style characters using eye-gaze input. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means. This system includes a means for collecting and analyzing past handwritten data to learn the characteristics of handwritten characters, a means for receiving character data selected by a user using an eye-gaze input device, a means for converting the received character data into a handwritten-style character based on the learned handwritten character characteristics, and a means for outputting the converted handwritten character data. This allows individuals such as muscular dystrophy patients to generate their own handwritten-style characters using only eye-gaze input, preserving their warmth and individuality. Furthermore, by including a means for adding fluctuations to the pen pressure and curve angle to the converted handwritten character data, it is possible to consistently generate fresh handwritten-style characters without creating unique character shapes. Furthermore, by including a means for converting the received character data into output data in bitmap or vector format, the system can be used in a variety of applications.

[0006] "Handwritten data" refers to digital images or data of characters or drawings that a user has previously handwritten on paper or a digital device.

[0007] "Analysis" is the process of analyzing handwritten data and extracting features such as character shape, writing pressure, and curve angle.

[0008] An "eye gaze input device" is a device that tracks the movement of a user's eye gaze and recognizes it as input information.

[0009] "Character data" is digital information representing a specific character string selected by the user using the eye-gaze input device.

[0010] A "generative AI model" is an artificial intelligence algorithm that learns the characteristics of handwritten data and converts given character data into something that looks handwritten.

[0011] "Conversion" is the process of replacing character data with user-written style characters based on the analysis and learning results.

[0012] "Fluctuation" is a technique that adds random variations to the pen pressure and curve angle to prevent the generated character shapes from being unique.

[0013] A "bitmap" is a digital image format that represents image data pixel by pixel.

[0014] A "vector format" is a digital image format that expresses image data using mathematical formulas and coordinate information such as points, lines, and shapes.

[0015] "Pen pressure" refers to the strength of the force that a pen or brush applies to the surface of paper or a digital device when writing by hand. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

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

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] This invention is a system that enables users who have difficulty writing by hand, such as those with muscular dystrophy, to generate handwritten-style characters using eye gaze input, thereby expressing the warmth and individuality of handwriting without compromising the convenience of eye gaze input. The system of this invention analyzes and learns from past handwritten data, and makes it possible to convert character data acquired through eye gaze input into handwritten-style characters.

[0038] System Overview

[0039] The system consists of the following main components:

[0040] 1. Handwritten data analysis and learning (server)

[0041] 2. Character selection by eye gaze input (user, device)

[0042] 3. Generating handwritten-style characters (server)

[0043] 4. Display and save handwritten characters (device, user)

[0044] Program processing

[0045] Learning Phase

[0046] Handwriting data collection and learning (server)

[0047] Data that a user has previously handwritten is scanned or digitally imported into the terminal.

[0048] The device sends handwritten data to a server, which stores it in a database and a generative AI model analyzes and learns features such as character shape, pressure, and curve angle.

[0049] This training data serves as a reference for generating user-style character shapes for new input data.

[0050] Gaze input phase

[0051] Character selection (user, terminal)

[0052] A user uses an eye-gaze input device to select characters on the screen by gazing at them.

[0053] The device analyzes the gaze input data and sends the selected string (e.g., "Hello") to the server.

[0054] Handwritten character generation phase

[0055] Handwritten character generation (server)

[0056] Based on the character data received by the server, the generative AI model generates handwritten-style characters, incorporating the characteristics of handwritten data it has learned from the past.

[0057] The server adds fluctuations to the pen pressure and curve angle to prevent the character shape from being unique.

[0058] The generated handwritten character data is sent back to the terminal in bitmap or vector format.

[0059] Result output phase

[0060] Display and save handwritten style characters (device, user)

[0061] The terminal receives the handwritten character data and displays it on the screen.

[0062] The user can review the displayed handwriting and save or print it if desired.

[0063] Specific examples

[0064] Example 1: Writing a birthday message

[0065] 1. Learning handwritten data (server)

[0066] The system scans a handwritten message written by the user in the past, such as "Happy Birthday," and sends it from the device to the server.

[0067] The server analyzes this handwritten data, and the generative AI model learns features such as character shape and writing pressure.

[0068] 2. Character selection (user, terminal)

[0069] The user uses gaze input to select the string they want to enter: "Happy Birthday."

[0070] The device sends the results of the gaze input to the server.

[0071] 3. Generating handwritten-style characters (server)

[0072] The server converts the string "Happy Birthday" selected through eye gaze input into handwritten text, adjusting the pen pressure and curve as needed based on past learning data.

[0073] The generated handwritten character data is sent to the terminal.

[0074] 4. Display and storage (device, user)

[0075] The terminal displays the handwritten-style characters on the display, and if the user is satisfied after checking them, they can save or print them.

[0076] The finalized message can be sent as a letter or an electronic message.

[0077] In this way, the system of the present invention allows users to easily generate handwritten-style characters using an eye-gaze input device, allowing them to create messages that retain the warmth and individuality of handwriting.

[0078] The processing flow will be explained below.

[0079] Step 1: Collect handwritten data (user)

[0080] Scan or photograph letters or messages that the user has previously written.

[0081] The user imports scanned data or photos into the device.

[0082] Step 2: Send handwritten data (device)

[0083] The handwritten data captured by the terminal is sent to the server.

[0084] When sending, the data is sent with the user ID and timestamp attached.

[0085] Step 3: Storing and analyzing handwritten data (server)

[0086] The server stores the received handwritten data in a database.

[0087] The server analyzes the handwritten data and extracts features such as character shape, pen pressure, and curve angle.

[0088] Step 4: Feature Learning (Server)

[0089] The server uses a generative AI model to learn the characteristics of the extracted handwritten data.

[0090] The learned features are stored in a database and used as a basis for generating handwritten-style characters for new input data.

[0091] Step 5: Acquiring Gaze Input Data (User)

[0092] A user uses an eye-gaze input device to select characters on the screen by gazing at them.

[0093] The user selects a string of characters (for example, "hello") by eye gaze input.

[0094] Step 6: Sending gaze input data (device)

[0095] The device analyzes the gaze input data and sends the selected string "Hello" to the server.

[0096] Step 7: Receiving a request to generate handwritten characters (server)

[0097] The server receives the text data "Hello" from the terminal.

[0098] The server starts processing as a request to generate handwritten-style characters based on this character data.

[0099] Step 8: Generate handwritten characters (server)

[0100] The server uses the generated AI model to convert the received text data "hello" into a handwritten version.

[0101] The server applies fluctuations to the pen pressure and curve angle to ensure that the generated handwritten characters are not unique.

[0102] Step 9: Determine the output format of the generated data (server)

[0103] The server converts the generated handwritten characters into output data in bitmap or vector format.

[0104] Define the output format and create the data to send back to the terminal.

[0105] Step 10: Returning handwritten character data (server)

[0106] The server returns the generated handwritten character data to the terminal.

[0107] Step 11: Displaying Handwritten Characters (Device)

[0108] The terminal receives the returned handwritten character data and displays it on the screen.

[0109] The user checks the displayed handwriting.

[0110] Step 12: Saving and using handwritten characters (user)

[0111] The user reviews the displayed handwriting and, if satisfied, saves or prints it.

[0112] Send it as a letter or message if necessary.

[0113] Example 1

[0114] 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."

[0115] For people with muscular dystrophy or those who have difficulty writing by hand, eye gaze input alone can be used to select and input characters, but it can lose the warmth and individuality of handwriting. Furthermore, existing eye gaze input systems do not provide an appropriate means for generating handwritten-style characters, making it difficult to reproduce the feel of handwriting.

[0116] 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.

[0117] In this invention, the server includes means for collecting past handwritten data and analyzing it to learn the characteristics of handwritten characters, means for receiving character data selected by a user using an eye-gaze input device, means for converting the received character data into a handwritten-style character based on the learned characteristics of handwritten characters, and means for displaying and saving the converted handwritten character data. This makes it possible to generate characters that have the warmth and individuality of handwriting without losing the convenience of eye-gaze input.

[0118] "Previous handwritten data" refers to character information previously handwritten by a user, and is data captured in a scanned or digital format.

[0119] "Analysis" refers to the process of extracting features such as character shape, pen pressure, and curve angle from handwritten data, allowing the generative AI model to learn.

[0120] "Characteristics of handwritten characters" refers to the unique expressive elements of handwriting, such as the character shape, writing pressure, and angle of curve contained in the handwritten data.

[0121] An "eye gaze input device" is a device that tracks the user's gaze and can use the gaze movement as data.

[0122] "Character data" refers to character information selected or input by the user through the eye-gaze input device.

[0123] "Conversion" refers to the process of regenerating received character data into a handwritten style based on the learned characteristics of handwritten characters.

[0124] "Display and save" refers to the function of displaying the generated handwritten-style character data on the device screen and, if necessary, saving the data.

[0125] "Adding fluctuations to the pen pressure and curve angle" refers to the process of adding small changes to the generated handwritten-style character data to give the characters a handwritten feel.

[0126] "Bitmap format" refers to a format that represents image data in pixel units.

[0127] "Vector format" refers to a format in which image data is expressed using mathematical formulas and geometric elements.

[0128] A "generative AI model" refers to a model that uses artificial intelligence technology to learn the characteristics of handwritten characters and generate new data.

[0129] The present invention is a system that uses an eye-gaze input device and a generative AI model to generate handwritten-style characters, providing the convenience of eye-gaze input without losing the warmth and individuality of handwriting. This system is specifically implemented using the following hardware and software.

[0130] Hardware

[0131] Scanner: A device that allows a user to convert past handwritten data into digital form.

[0132] Device (PC, tablet, etc.): A device used to capture handwritten data, analyze eye-gaze input data, and display and save handwritten-style character data.

[0133] Server: A device that analyzes and learns from handwritten data and generates handwritten-style characters.

[0134] Eye-gaze input device: A device that tracks the user's gaze and captures their gaze movements as data (e.g., Tobii Eye Tracker).

[0135] Printer: A device for printing the generated handwritten-style character data.

[0136] software

[0137] Database (e.g., MySQL (registered trademark)): Software for storing handwritten data and learning data.

[0138] Generative AI model (e.g., Python TENSORFLOW (registered trademark)): A model that learns the features of handwritten data and generates handwritten-style characters.

[0139] Eye-gaze applications (e.g., Tobii Pro Lab): Applications that analyze data from eye-gaze devices and select characters.

[0140] Image viewer (e.g. Adobe Reader): Software for displaying the generated handwritten-style character data.

[0141] System Overview

[0142] The system consists of the following main components:

[0143] 1. Handwriting data collection and learning (server)

[0144] A scanner captures data that a user has previously handwritten and stores it on the device. The device then sends the handwritten data to a server, which analyzes and learns from it. A generative AI model analyzes and learns features such as character shape, pen pressure, and curve angle. This learning data serves as the basis for generating user-style character shapes for new data.

[0145] 2. Character selection by eye gaze input (user, device)

[0146] The user uses an eye-gaze input device to select characters on the screen. The device analyzes the eye-gaze input data and sends the selected character string (e.g., "Hello") to the server.

[0147] 3. Generating handwritten-style characters (server)

[0148] Based on the character data received by the server, the generative AI model generates handwritten-style characters. The characteristics of handwritten data learned in the past are reflected. The server adds fluctuations to the pen pressure and curve angle to prevent the character shapes from becoming unique. The generated handwritten-style character data is returned to the device in bitmap or vector format.

[0149] 4. Display and save handwritten characters (device, user)

[0150] The terminal receives the handwritten-style character data and displays it on the screen. The user can check the displayed handwritten characters and save or print them as needed.

[0151] Specific examples

[0152] Example 1: Writing a birthday message

[0153] 1. Learning handwritten data (server)

[0154] The system scans a handwritten message written by the user in the past, such as "Happy Birthday," and sends it from the device to the server.

[0155] The server analyzes this handwritten data, and the generative AI model learns features such as character shape and writing pressure.

[0156] 2. Character selection (user, terminal)

[0157] The user uses gaze input to select the string they want to enter: "Happy Birthday."

[0158] The device sends the results of the gaze input to the server.

[0159] 3. Generating handwritten-style characters (server)

[0160] The server converts the string "Happy Birthday" selected through eye gaze input into handwritten text, adjusting the pen pressure and curve as needed based on past learning data.

[0161] The generated handwritten character data is sent to the terminal.

[0162] 4. Display and storage (device, user)

[0163] The terminal displays the handwritten-style characters on the display, and if the user is satisfied after checking them, they can save or print them.

[0164] The finalized message can be sent as a letter or an electronic message.

[0165] In this way, the system of the present invention allows a user to easily generate handwritten-style characters using an eye-gaze input device, and create messages that retain the warmth and individuality of handwriting.

[0166] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0167] Step 1: Collecting and training handwriting data

[0168] Input: A scanned image file of data previously handwritten by a user (e.g., "Happy Birthday").

[0169] Operation:

[0170] 1. The user uses a scanner to capture handwritten data as a digital image on the device.

[0171] 2. The device sends the image file to the server. Specifically, the image file is uploaded to the server using the HTTP protocol.

[0172] Output: Handwritten data stored on the server.

[0173] Specific behavior:

[0174] The server stores the received image file in a database.

[0175] The server's generative AI model analyzes this data and learns characteristics such as character shape, pen pressure, and curve angle. This learning data is then used to generate new data.

[0176] Step 2: Select text with gaze input

[0177] Input: Gaze data captured from the user's eye tracking device.

[0178] Operation:

[0179] 1. The user uses an eye-gaze input device (e.g., Tobii Eye Tracker) to select text on the screen.

[0180] 2. The eye-gaze input device tracks the user's gaze and detects character selection.

[0181] 3. The device analyzes the gaze data and identifies the selected string (e.g., "Hello").

[0182] 4. The device sends the selected string to the server using the HTTP protocol.

[0183] Output: The string data received by the server.

[0184] Specific behavior:

[0185] The device uses a Python script to analyze the gaze data and extract the selected string.

[0186] The terminal transmits the extracted character string data to the server as an HTTP request.

[0187] Step 3: Generate handwritten characters

[0188] Input: Character data received by the server (e.g. "Hello").

[0189] Operation:

[0190] 1. The server uses a generative AI model to generate handwritten-style characters based on the character data received.

[0191] 2. The server uses the learned data to convert the characters into handwritten style, adding fluctuations to the pen pressure and curve angle.

[0192] 3. Convert the generated handwritten character data into bitmap or vector format.

[0193] 4. The server sends the generated handwritten-style character data to the device using the HTTP protocol.

[0194] Output: Generated handwritten character data.

[0195] Specific behavior:

[0196] The server runs a generative AI model (e.g., Python TensorFlow) and generates handwritten-style characters based on the input character data.

[0197] The server converts the generated handwritten-style character data into bitmap or vector format and sends it to the terminal as an HTTP response.

[0198] Step 4: Display and save handwritten text

[0199] Input: Handwritten-style character data received by the device.

[0200] Operation:

[0201] 1. The device displays the handwritten character data received from the server. Specifically, an image viewer (e.g., Adobe Reader) is used.

[0202] 2. The user checks the displayed handwritten characters and, if satisfied, saves or prints them.

[0203] 3. If you choose to save, the device will save the data to local storage or a cloud service. If you choose to print, the device will print it out.

[0204] Output: Saved handwriting data or printed handwriting.

[0205] Specific behavior:

[0206] The terminal displays the received handwritten-style character data using an image viewer.

[0207] The user clicks the save button to save the data or the print button to print it out on a printer.

[0208] Through the specific actions performed at each step, users can generate handwritten-style characters using only their gaze, easily creating messages that retain the warmth and individuality of handwriting.

[0209] (Application example 1)

[0210] 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."

[0211] In systems that generate handwritten-style characters using eye gaze input, users can easily create characters that have the warmth and individuality of handwriting. However, the lack of a function to apply this handwritten-style character to video or image content makes it difficult for users to easily create practical content. Therefore, there is a need not only for generating handwritten-style characters using eye gaze input, but also for an efficient means to apply them to video and images.

[0212] 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.

[0213] In this invention, the server includes means for collecting past handwritten data and analyzing it to learn the characteristics of handwritten characters, means for receiving character data selected by a user using an eye-gaze input device, means for converting the received character data into a handwritten-style character based on the learned characteristics of the handwritten characters, means for outputting the converted handwritten character data, and means for applying handwritten-style characters to image or video content based on the eye-gaze data collected using the eye-gaze input device. This allows a user to easily apply handwritten-style characters created using eye-gaze input to video or image content, thereby enabling the creation of original, unique content.

[0214] "Past handwritten data" refers to character information previously handwritten by a user, and is data that the learning model uses to analyze handwriting characteristics.

[0215] An "eye gaze input device" is a device that detects the movement of a user's eye gaze and uses that information as input data.

[0216] "Handwritten character features" refer to the individual attributes of handwritten characters, such as character shape, pen pressure, and curve angle, and by analyzing and learning these, it becomes possible to generate handwritten-style characters.

[0217] "Received character data" refers to character information selected by the user that is acquired from the eye-gaze input device, and handwritten-style characters are generated based on this data.

[0218] "Converting to handwritten style" refers to converting received character data into a character format that has the warmth and individuality unique to handwriting, based on past handwritten data.

[0219] "Handwritten-style character data" refers to character information that has been converted to look like handwritten text by a generative AI model that reflects the characteristics of past handwritten data.

[0220] "Gaze data" refers to the movement and position information of a user's gaze collected by an eye-gaze input device, and is used for character selection, etc.

[0221] "Image or video content" refers to media content in the form of still images or videos, to which handwritten-style characters can be added.

[0222] The present invention is a system that uses eye gaze input to generate handwritten-style characters and apply these characters to image or video content. Each component of the present invention and its operation are described in detail below.

[0223] System configuration

[0224] The system consists of the following main components:

[0225] 1. Handwriting data collection and learning

[0226] 2. Character selection using eye gaze input

[0227] 3. Generating handwritten-style characters

[0228] 4. Applying handwritten text to content

[0229] 5. Output and Saving

[0230] Handwriting data collection and learning

[0231] Users scan or digitally import previously written handwriting data into their device. The device then sends this handwriting data to a cloud server. The server stores the handwriting data in a database, and the generative AI model analyzes and learns features such as character shape, pen pressure, and curve angle. This learning data serves as a reference for generating user-style character shapes for new gaze input data.

[0232] Selecting characters using eye gaze input

[0233] The user selects characters on the screen by guiding their gaze using an eye-gaze input device (e.g., a smartphone camera). The device analyzes the eye-gaze input and transmits the selected character data to a cloud server.

[0234] Generating handwritten characters

[0235] The server uses a generative AI model to generate handwritten-style characters based on the received character data. The server incorporates the characteristics of previous handwritten data, adding fluctuations to the pen pressure and curve angle. The generated handwritten-style character data is then sent back to the device in bitmap or vector format.

[0236] Applying handwritten text to content

[0237] The device applies the received handwritten-style character data to the image or video content specified by the user, thereby generating content containing a unique handwritten-style message.

[0238] Output and save

[0239] The generated content is displayed on the device's display, and the user can check the displayed content and save or share it if satisfied.

[0240] Hardware and software used

[0241] Camera: Uses your phone's camera to detect your gaze.

[0242] Cloud server: A server that learns handwritten data and processes the generation of handwritten-style characters, and also includes a generative AI model.

[0243] Generative AI model: A model that generates handwritten-style characters using deep learning frameworks such as TensorFlow.

[0244] Examples of concrete examples and prompts

[0245] Examples:

[0246] The user uses an eye-gaze input device to select the word "Thank you." The selected text is sent to a cloud server, where a generative AI model converts it into handwritten-style text based on past handwriting data. This handwritten-style text is then applied to the video message the user is creating, and the "Thank you" part is displayed in handwritten style.

[0247] Example prompt sentence:

[0248] "Using the gaze-selected string 'Thank you', generate handwritten-style characters based on past handwriting data."

[0249] This invention allows users to easily generate handwritten-style characters using an eye-gaze input device and apply them to content, making it possible to create attractive digital content while preserving the warmth and individuality of handwriting.

[0250] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0251] Step 1:

[0252] The user scans or digitally imports previously handwritten data into the device. The device then sends the data to a cloud server, where it stores the handwritten data in a database. The generative AI model analyzes and learns from the character's shape, pen pressure, curve angle, and other characteristics.

[0253] Input: Handwritten data

[0254] Data processing: Analysis of character shape, pen pressure, and curve angle

[0255] Output: Trained model

[0256] Step 2:

[0257] The user selects characters on the screen by using the eye-gaze input device. The terminal acquires the user's eye-gaze data through the eye-gaze input device and transmits the selected character data to the cloud server.

[0258] Input: Gaze data, selected character data

[0259] Data processing: Analysis of gaze data, generation of text data

[0260] Output: Received character data

[0261] Step 3:

[0262] The server uses a generative AI model based on the received character data to generate handwritten-style characters. It adds variations to the character shape, pen pressure, and curve angle to reflect the characteristics of previous handwritten data. The generated handwritten-style character data is then sent back to the device in bitmap or vector format.

[0263] Input: Received character data, trained model

[0264] Data processing: Generating handwritten characters based on character shape, pressure, and curve angle

[0265] Output: Handwritten character data

[0266] Step 4:

[0267] The device applies the received handwritten-style character data to the image or video content specified by the user, drawing the handwritten-style characters on the content and adjusting the layout as needed.

[0268] Input: Handwritten text, image or video content

[0269] Data processing: Drawing handwritten characters, adjusting layout

[0270] Output: Content with handwritten text applied

[0271] Step 5:

[0272] The device displays the generated content on the display for the user to review, and if the user is satisfied, they can save the generated content and share it as needed.

[0273] Input: Content with handwritten text applied

[0274] Data processing: Displaying content, user confirmation

[0275] Output: Saved content, shared content

[0276] 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.

[0277] The present invention combines an emotion engine with a system that allows users who have difficulty writing by hand to use eye gaze input to generate handwritten-style characters and express the warmth and individuality of handwriting. The system of the present invention analyzes past handwritten data to learn the characteristics of handwritten characters, and makes it possible to convert character data acquired by eye gaze input into handwritten-style characters based on the emotion engine.

[0278] System Overview

[0279] The system consists of the following main components:

[0280] 1. Handwritten data analysis and learning (server)

[0281] 2. Character selection by eye gaze input (user, device)

[0282] 3. Emotion recognition using emotion engine (server)

[0283] 4. Generating handwritten-style characters (server)

[0284] 5. Display and save handwritten style characters (device, user)

[0285] Program processing

[0286] Learning Phase

[0287] Handwriting data collection and learning (server)

[0288] Data that a user has previously handwritten is scanned or digitally imported into the terminal.

[0289] The device sends handwritten data to a server, which stores it in a database and a generative AI model analyzes and learns features such as character shape, pen pressure, and curve angle.

[0290] This training data serves as a reference for generating user-style character shapes for new input data.

[0291] Gaze input phase

[0292] Character selection (user, terminal)

[0293] A user uses an eye-gaze input device to select characters on the screen by gazing at them.

[0294] The device analyzes the gaze input data and sends the selected string (e.g., "Hello") to the server.

[0295] Emotion Recognition Phase

[0296] Emotion Recognition (Server)

[0297] The emotion engine installed on the server analyzes the user's gaze, facial expressions, voice input, etc. to recognize their current emotional state.

[0298] The recognized emotion data is reflected in the handwritten character generation process.

[0299] Handwritten character generation phase

[0300] Handwritten character generation (server)

[0301] Based on the character data received by the server, the generative AI model generates handwritten-style characters, incorporating the characteristics of handwritten data learned in the past and emotional information from the emotion engine.

[0302] Fluctuations in pen pressure, curve angle, and even the color and style of the text are added based on emotion.

[0303] The generated handwritten character data is sent back to the terminal in bitmap or vector format.

[0304] Result output phase

[0305] Display and save handwritten style characters (device, user)

[0306] The terminal receives the handwritten character data and displays it on the screen.

[0307] The user can review the displayed handwriting and save or print it if desired.

[0308] Specific examples

[0309] Example 1: Writing a birthday message

[0310] 1. Learning handwritten data (server)

[0311] The system scans a handwritten message written by the user in the past, such as "Happy Birthday," and sends it from the device to the server.

[0312] The server analyzes this handwritten data, and the generative AI model learns features such as character shape and writing pressure.

[0313] 2. Character selection (user, terminal)

[0314] The user uses gaze input to select the string they want to enter: "Happy Birthday."

[0315] The device sends the results of the gaze input to the server.

[0316] 3. Emotion Recognition (Server)

[0317] The server analyzes the user's gaze and facial expressions to recognize their current emotional state (e.g., joy).

[0318] The recognized emotion data is reflected in the handwritten character generation process.

[0319] 4. Generating handwritten-style characters (server)

[0320] The server converts the string "Happy Birthday" selected through eye gaze input into handwritten text. Past learning data and recognized emotional information (joy) are reflected in the conversion, adding fluctuations to the pressure and curve of the characters, as well as brightening the colors and style.

[0321] The generated handwritten character data is sent to the terminal.

[0322] 5. Display and storage (device, user)

[0323] The terminal displays the handwritten-style characters on the display, and if the user is satisfied after checking them, they can save or print them.

[0324] The finalized message can be sent as a letter or an electronic message.

[0325] In this way, the system of the present invention allows users to use eye gaze input to generate handwritten-style characters that reflect their own emotions, allowing them to create messages that incorporate the warmth and individuality of handwriting, as well as emotional expression.

[0326] The processing flow will be explained below.

[0327] Step 1: Collect handwritten data (user)

[0328] Scan or photograph letters or messages that the user has previously written.

[0329] The user imports scanned data or photos into the device.

[0330] Step 2: Send handwritten data (device)

[0331] The handwritten data captured by the terminal is sent to the server.

[0332] When sending, the data is sent with the user ID and timestamp attached.

[0333] Step 3: Storing and analyzing handwritten data (server)

[0334] The server stores the received handwritten data in a database.

[0335] The server analyzes the handwritten data and extracts features such as character shape, pen pressure, and curve angle.

[0336] Step 4: Feature Learning (Server)

[0337] The server uses a generative AI model to learn the characteristics of the extracted handwritten data.

[0338] The learned features are stored in a database and used as a basis for generating handwritten-style characters for new input data.

[0339] Step 5: Acquiring Gaze Input Data (User)

[0340] A user uses an eye-gaze input device to select characters on the screen by gazing at them.

[0341] The user selects a string of characters (for example, "hello") by eye gaze input.

[0342] Step 6: Sending gaze input data (device)

[0343] The device analyzes the gaze input data and sends the selected string "Hello" to the server.

[0344] Step 7: Emotion Recognition (Server)

[0345] The emotion engine installed on the server analyzes the user's gaze, facial expressions, voice input, etc. to recognize their current emotional state.

[0346] The recognized emotion data is reflected in the handwritten character generation process.

[0347] Step 8: Receiving a request to generate handwritten characters (server)

[0348] The server receives the text data "Hello" and emotion data from the terminal.

[0349] The server starts processing as a request to generate handwritten-style characters based on this character data and emotion data.

[0350] Step 9: Generate handwritten characters (server)

[0351] The server uses the generative AI model to convert the received text data "Hello" into handwritten text, incorporating the characteristics of handwritten data learned in the past and emotional information from the emotion engine.

[0352] Fluctuations in pen pressure, curve angle, and even the color and style of the text are added based on emotion.

[0353] Step 10: Determine the output format of the generated data (server)

[0354] The server converts the generated handwritten characters into output data in bitmap or vector format.

[0355] Define the output format and create the data to send back to the terminal.

[0356] Step 11: Returning handwritten character data (server)

[0357] The server returns the generated handwritten character data to the terminal.

[0358] Step 12: Displaying Handwritten Characters (Device)

[0359] The terminal receives the returned handwritten character data and displays it on the screen.

[0360] The user checks the displayed handwriting.

[0361] Step 13: Saving and using handwritten characters (user)

[0362] The user reviews the displayed handwriting and, if satisfied, saves or prints it.

[0363] Send it as a letter or message if necessary.

[0364] Example 2

[0365] 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."

[0366] With current text input systems, many users find it difficult to capture the warmth, individuality, and emotion of handwriting. Furthermore, for users of eye-gaze input devices, there are limited ways to easily create handwritten-style text. This makes it difficult to create emotionally charged messages using eye-gaze input.

[0367] 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.

[0368] In this invention, the server includes means for collecting past handwritten data and analyzing it to learn the characteristics of handwritten characters, means for receiving character data selected by a user using an eye-gaze input device, means for analyzing the user's eye movements and facial expressions to recognize an emotional state, means for converting the received character data into handwritten-style characters based on the learned handwritten character characteristics and the recognized emotional state, and means for outputting the converted handwritten character data, thereby enabling a user to generate handwritten-style characters through eye-gaze input and create an emotional message.

[0369] "Handwritten data" refers to digital data of characters and figures handwritten by a user.

[0370] An "eye gaze input device" is a device for tracking the movement of a user's eye gaze and acquiring that data.

[0371] "Character data" refers to digital information of a character string input by a user, acquired through an eye-gaze input device or other input means.

[0372] An "emotion engine" is software or hardware that has the function of recognizing a user's emotional state by analyzing the user's gaze movements, facial expressions, voice input, etc.

[0373] The "means for converting to a handwritten-like form" is a system or program for converting digital character data to a handwritten-like form based on the characteristics of the handwritten data.

[0374] "Pen pressure" refers to the strength of pressure that the pen applies to the paper when writing by hand, and is one of the characteristics of handwriting.

[0375] The "angle of the curve" refers to the angle of the curved part of handwritten characters, and is a feature that expresses the individuality of handwriting.

[0376] "Fluctuation" refers to the randomness in the pressure and angle of the curve of handwritten characters, and is a variable element used to express the naturalness and individuality of handwriting.

[0377] A "generative AI model" is an artificial intelligence model trained to analyze the characteristics of handwritten data and generate new handwritten-style characters based on that data.

[0378] A "bitmap" is a digital image file format that makes up an image on a pixel-by-pixel basis.

[0379] A "vector format" is a digital image file format that represents an image using mathematical formulas such as points, lines, and curves.

[0380] This invention combines an emotion engine with a system that allows users who have difficulty writing by hand to generate handwritten-style characters using an eye-gaze input device, thereby expressing the warmth and individuality of handwriting. This system consists of the following main components:

[0381] Handwriting data collection and learning

[0382] 1. Handwritten data collection

[0383] Users can scan previously handwritten data or write it on a digital device such as a tablet, for example, by scanning special messages or everyday notes and converting them into digital form.

[0384] The device stores this digital handwriting data and sends it to a server, where it uploads the data using its internet connection.

[0385] 2. Analysis and learning of handwritten data

[0386] The server stores the received handwritten data in a database. The handwritten feature information includes the character shape, writing pressure, and curve angle.

[0387] A generative AI model analyzes this data and learns the unique characteristics of the user's handwriting.

[0388] Selecting characters using eye gaze input

[0389] 3. Selecting the characters

[0390] The user wears an eye-gaze input device and the movement of the user's eyes is tracked. For example, an eye tracker is a commonly used eye-gaze input device.

[0391] The device analyzes the data from the eye-gaze input device and recognizes the characters the user is looking at. The user selects a character string, such as "hello," from a list of characters displayed on the screen.

[0392] Emotion recognition

[0393] 4. Emotional Recognition

[0394] The emotion engine installed on the server analyzes the user's gaze, facial expression, and, if necessary, voice input to recognize their current emotional state, which can include, for example, "happiness" or "sadness."

[0395] The recognized emotion data is reflected in the handwritten character generation process.

[0396] Generating handwritten characters

[0397] 5. Generating handwritten-style characters

[0398] Based on the character data and emotion data received by the server, the generative AI model generates handwritten-style characters. The generative AI model applies the characteristics of handwritten data it has learned in the past, and adjusts the pen pressure, curve angle, and even color to reflect the emotion information.

[0399] For example, when you type "hello," the pen pressure will fluctuate based on the emotional information, and the emotion will also be reflected in the color and style.

[0400] Display and save handwritten characters

[0401] 6. Display and save handwritten characters

[0402] The terminal receives the generated handwritten-style character data and displays it on the display.

[0403] The user checks the displayed handwritten-style characters and, if satisfied, saves or prints them. The device uses the save function to save them as a digital file or connects to a printer to print them.

[0404] Specific examples

[0405] Example: How to write a birthday message

[0406] 1. Learning from handwritten data

[0407] The system scans past messages written by users saying "Happy Birthday" and stores them on the device, which then sends this data to a server.

[0408] The server analyzes this data, and the generative AI model learns the character shape, pressure, and curve angle.

[0409] 2. Selecting the characters

[0410] The user uses the eye-gaze input device to select the character string "Happy Birthday" with their eyes.

[0411] The device analyzes the gaze data and sends the selected character string to the server.

[0412] 3. Emotional Recognition

[0413] The server analyzes gaze movements and facial expressions to recognize the current emotional state (e.g., joy).

[0414] 4. Generating handwritten-style characters

[0415] The server converts the text "Happy Birthday" into handwritten text, adjusting the pen pressure, curve, color, etc. to reflect the emotional information (joy).

[0416] The server transmits the generated handwritten-style character data to the terminal.

[0417] 5. Display and save

[0418] The device displays handwritten-style characters on the screen for the user to confirm.

[0419] The user selects save or print and the terminal performs the action.

[0420] This system allows users to easily create unique, handwritten-style characters by using eye-gaze input, and to create emotive messages.

[0421] Prompt Sentence Examples

[0422] "I want to scan past handwritten messages and have the server learn from them."

[0423] "I want to use my eye-gaze device to select text on the screen."

[0424] "I want to analyze the current emotion and reflect it in handwritten-style characters."

[0425] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0426] Step 1: Collecting handwritten data

[0427] Input: A handwritten message written by the user in the past

[0428] What it does: Scans data that a user has previously handwritten or writes on a digital device such as a tablet. For example, scans a birthday card or letter and converts it into a digital format.

[0429] Output: Handwritten data in digital format

[0430] Step 2: Send handwritten data

[0431] Input: Handwritten data in digital format

[0432] Specific operation: The handwritten data captured by the device in a scanned or digital format is sent to a server via the Internet, and the handwritten data is uploaded to cloud storage by calling an API.

[0433] Output: Handwritten data stored on the server

[0434] Step 3: Analyzing and learning handwritten data

[0435] Input: Handwritten data stored on the server

[0436] Specific operation: The server inputs the handwritten data it receives into the generative AI model, which analyzes features such as character shape, pen pressure, and curve angle. The generative AI model learns from this data, extracts and saves the user's unique handwritten character characteristics.

[0437] Output: Trained handwritten character feature data

[0438] Step 4: Selecting the text

[0439] Input: User's gaze input data

[0440] Specific operation: The user wears an eye-gaze input device and selects characters displayed on the screen with their eyes. The device analyzes the data from the eye-gaze input device in real time and identifies the character the user is looking at. For example, the character string "Hello" is selected.

[0441] Output: String selected by gaze

[0442] Step 5: Sending characters

[0443] Input: Gaze-selected string

[0444] Specific operation: The device sends the selected string to the server, and the character data is uploaded to the server via the Internet.

[0445] Output: Character data received by the server

[0446] Step 6: Recognize emotions

[0447] Input: User's gaze or facial expression data

[0448] Specific behavior: The server's emotion engine analyzes eye movements and facial expressions, and optionally voice input data, to identify the current emotional state (e.g., joy or sadness).

[0449] Output: Recognized emotional state data

[0450] Step 7: Generate handwritten characters

[0451] Input: Character data received by the server, trained handwriting feature data, recognized emotional state data

[0452] How it works: Based on the character data and emotion data received by the server, the generative AI model generates handwritten-style characters. It applies the characteristics of handwritten data it has learned in the past and adjusts the pen pressure, curve angle, color, etc. to reflect the recognized emotion information. For example, it converts the string "hello" into a handwritten-style character and adds bright colors and styles based on the emotion of joy.

[0453] Output: Generated handwritten character data

[0454] Step 8: Send handwritten text

[0455] Input: Generated handwritten-style character data

[0456] Specific operation: The server generates handwritten character data and sends it to the device in bitmap or vector format. The data is then returned via the Internet.

[0457] Output: Handwritten character data received by the device

[0458] Step 9: Displaying handwritten characters

[0459] Input: Handwritten-style character data received by the terminal

[0460] Specific operation: The handwritten character data received by the device is displayed on the display, and a graphic representation is provided so that the user can check it in real time.

[0461] Output: Handwritten characters displayed on the screen

[0462] Step 10: Save and print your handwritten text

[0463] Input: Handwritten characters displayed on the screen

[0464] Specific operation: The user checks the handwritten-style characters and selects whether to save or print them. The device uses the save function to save them as a digital file or connects to a printer to print them.

[0465] Output: Saved handwriting file or printed handwriting

[0466] (Application example 2)

[0467] 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."

[0468] When users who have difficulty writing by hand use eye-gaze input to generate handwritten-like characters, it is difficult to express warmth and individuality in the characters. Creating messages that reflect the user's emotions is also a challenge. In food delivery applications, there is a need for technology that can generate handwritten-like messages using eye-gaze input devices and convey emotions such as gratitude and encouragement.

[0469] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting past handwritten data and analyzing it to learn the characteristics of handwritten characters, means for receiving character data selected by a user using an eye-gaze input device, means for converting the received character data into handwritten-style characters based on the learned characteristics of handwritten characters, means for outputting the converted handwritten character data, and means for recognizing the emotional state of the user and adjusting the characteristics of handwritten-style characters based on the emotion. This enables a user to create a handwritten-style message that reflects their personality and emotion through the eye-gaze input device.

[0470] "Past handwritten data" refers to character information that a user has handwritten in the past, and serves as basic data for analyzing and learning the characteristics of handwritten characters.

[0471] "Handwritten character features" refer to individual attributes of handwritten characters, such as the shape, pressure, and angle of the curve, and are used as reference when generating handwritten-style characters.

[0472] An "eye gaze input device" is a device that includes hardware and software for tracking the movement of a user's eye gaze.

[0473] "Character data" refers to character information entered or selected by the user, and is the original data that is converted into handwritten-style characters.

[0474] The "means for converting to look like handwriting" refers to the process of processing character data entered by eye gaze input to look like handwriting based on the collected and learned characteristics of the handwriting data.

[0475] "Converted handwritten character data" refers to character information that has been processed to look like handwriting, and is output in bitmap or vector format.

[0476] An "emotional state" refers to the emotion a user is feeling at a particular moment, and may include joy, sadness, anger, etc.

[0477] "Means for adjusting the characteristics of handwritten-style characters based on emotions" refers to a process of analyzing a user's emotional state and adjusting the pressure, curve angle, color, style, etc. of the handwritten characters based on that information.

[0478] The present invention is a system that uses eye gaze input to generate handwritten-style characters, particularly in food delivery applications, allowing users to send thank you or other emotional messages to delivery staff or stores.

[0479] System Overview

[0480] The system consists of the following main components:

[0481] 1. Handwritten data analysis and learning (server)

[0482] 2. Character selection by eye gaze input (user, device)

[0483] 3. Emotion recognition using emotion engine (server)

[0484] 4. Generating handwritten-style characters (server)

[0485] 5. Display and save handwritten style characters (device, user)

[0486] Program Description

[0487] Hardware and Software

[0488] Hardware: Eye tracking device, camera (for emotion recognition)

[0489] Software: OpenCV (image processing), EmotionRecognizer (emotion recognition model), GazeTracking (eye tracking library), HandwritingModel (handwriting generation model)

[0490] Analysis and learning of handwritten data

[0491] The server collects, analyzes, and learns from handwritten data that the user has written. Specifically, the generative AI model learns from the collected handwritten data and analyzes the characteristics of handwritten characters (character shape, writing pressure, angle of curve, etc.).

[0492] Selecting characters using eye gaze input

[0493] The user selects characters on the screen using the eye-gaze input device. The device analyzes the eye-gaze input and sends the selected character data to the server. For example, if the user selects the character string "Thank you," this data is sent.

[0494] emotion recognition

[0495] The emotion engine installed on the server analyzes the user's facial expressions and gaze data acquired from the camera to recognize their current emotional state, which can include joy, sadness, surprise, etc.

[0496] Generating handwritten characters

[0497] The server uses the received character data and the emotion information obtained from the emotion engine to generate handwritten-style characters. In this generation process, the pen pressure, curve angle, color, etc. are adjusted based on the characteristics of the handwritten data and the emotion information.

[0498] Display and save

[0499] The device receives handwritten-style character data sent from the server and displays it on the screen. The user can check it and save or send it if they are satisfied. This function allows users to input and send a handwritten-style message of thanks when ordering food delivery.

[0500] Specific examples

[0501] For example, consider a scenario where a user places an order using a food delivery application and sends a thank you message to the delivery person. When the user inputs "Thank you" using the eye-gaze input device, the system recognizes the user's emotion and generates a handwritten-style message that reflects that emotion.

[0502] An example of a prompt to be input to the generative AI model is as follows:

[0503] "Generate handwritten-style characters based on the string selected using gaze input and the recognized emotion information."

[0504] for example,

[0505] "selected_text = 'Thank you'

[0506] recognized_emotion = 'joy' The user's emotional state recognized by the AI

[0507] handwritten_text = generate_handwritten_text(selected_text, recognized_emotion)

[0508] print_handwritten_text(handwritten_text)”

[0509] In this way, a handwritten-style message that visually reflects the user's emotions can be generated and used when ordering food delivery.

[0510] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0511] Step 1:

[0512] Collects and learns handwritten data. The server collects handwritten data from users and stores it in a database. The generative AI model then analyzes this data and learns the characteristics of handwritten characters (such as character shape, pressure, and curve angle). The input is handwritten data, and the output is the learned handwritten character characteristics. Specific operations include scanning handwritten data, uploading the digital data, saving it to a database, and training the AI ​​model.

[0513] Step 2:

[0514] Character selection is performed using gaze input. The user uses an eye-gaze input device to select characters on the screen with their gaze. The device analyzes the gaze input data and sends the selected character data to the server. The input is gaze data, and the output is the selected character data. Specific operations include calibrating the eye-tracking device, collecting and analyzing gaze data, and sending the character selection results to the server.

[0515] Step 3:

[0516] Recognizes emotions. The server analyzes the user's facial expression and gaze data acquired from the camera and uses an emotion engine to recognize the user's current emotional state. The input is facial images and gaze data, and the output is recognized emotion information. Specific operations include capturing facial images with the camera, and image analysis and emotion classification with the emotion engine.

[0517] Step 4:

[0518] Generates handwritten-style characters. The server generates handwritten-style characters using a generative AI model based on the received character data and emotion information obtained from the emotion engine. The input is character data and emotion information, and the output is handwritten-style character data. Specific operations include generating handwritten-style characters using an AI model, adjusting pen pressure and curve angle, and changing colors.

[0519] Step 5:

[0520] This displays and saves handwritten-style characters. The terminal receives handwritten-style character data sent from the server and displays it on the screen. The user can check it and save or send it if they are satisfied. The input is handwritten-style character data, and the output is the displayed handwritten-style characters. Specific operations include converting the character data to bitmap or vector format, displaying it on the screen, and saving it to a file.

[0521] 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.

[0522] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0523] 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.

[0524] [Second embodiment]

[0525] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0526] 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.

[0527] 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).

[0528] 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.

[0529] 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.

[0530] 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).

[0531] 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.

[0532] 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.

[0533] 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.

[0534] 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.

[0535] 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.

[0536] 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."

[0537] This invention is a system that enables users who have difficulty writing by hand, such as those with muscular dystrophy, to generate handwritten-style characters using eye gaze input, thereby expressing the warmth and individuality of handwriting without compromising the convenience of eye gaze input. The system of this invention analyzes and learns from past handwritten data, and makes it possible to convert character data acquired through eye gaze input into handwritten-style characters.

[0538] System Overview

[0539] The system consists of the following main components:

[0540] 1. Handwritten data analysis and learning (server)

[0541] 2. Character selection by eye gaze input (user, device)

[0542] 3. Generating handwritten-style characters (server)

[0543] 4. Display and save handwritten characters (device, user)

[0544] Program processing

[0545] Learning Phase

[0546] Handwriting data collection and learning (server)

[0547] Data that a user has previously handwritten is scanned or digitally imported into the terminal.

[0548] The device sends handwritten data to a server, which stores it in a database and a generative AI model analyzes and learns features such as character shape, pressure, and curve angle.

[0549] This training data serves as a reference for generating user-style character shapes for new input data.

[0550] Gaze input phase

[0551] Character selection (user, terminal)

[0552] A user uses an eye-gaze input device to select characters on the screen by gazing at them.

[0553] The device analyzes the gaze input data and sends the selected string (e.g., "Hello") to the server.

[0554] Handwritten character generation phase

[0555] Handwritten character generation (server)

[0556] Based on the character data received by the server, the generative AI model generates handwritten-style characters, incorporating the characteristics of handwritten data it has learned from the past.

[0557] The server adds fluctuations to the pen pressure and curve angle to prevent the character shape from being unique.

[0558] The generated handwritten character data is sent back to the terminal in bitmap or vector format.

[0559] Result output phase

[0560] Display and save handwritten style characters (device, user)

[0561] The terminal receives the handwritten character data and displays it on the screen.

[0562] The user can review the displayed handwriting and save or print it if desired.

[0563] Specific examples

[0564] Example 1: Writing a birthday message

[0565] 1. Learning handwritten data (server)

[0566] The system scans a handwritten message written by the user in the past, such as "Happy Birthday," and sends it from the device to the server.

[0567] The server analyzes this handwritten data, and the generative AI model learns features such as character shape and writing pressure.

[0568] 2. Character selection (user, terminal)

[0569] The user uses gaze input to select the string they want to enter: "Happy Birthday."

[0570] The device sends the results of the gaze input to the server.

[0571] 3. Generating handwritten-style characters (server)

[0572] The server converts the string "Happy Birthday" selected through eye gaze input into handwritten text, adjusting the pen pressure and curve as needed based on past learning data.

[0573] The generated handwritten character data is sent to the terminal.

[0574] 4. Display and storage (device, user)

[0575] The terminal displays the handwritten-style characters on the display, and if the user is satisfied after checking them, they can save or print them.

[0576] The finalized message can be sent as a letter or an electronic message.

[0577] In this way, the system of the present invention allows users to easily generate handwritten-style characters using an eye-gaze input device, allowing them to create messages that retain the warmth and individuality of handwriting.

[0578] The processing flow will be explained below.

[0579] Step 1: Collect handwritten data (user)

[0580] Scan or photograph letters or messages that the user has previously written.

[0581] The user imports scanned data or photos into the device.

[0582] Step 2: Send handwritten data (device)

[0583] The handwritten data captured by the terminal is sent to the server.

[0584] When sending, the data is sent with the user ID and timestamp attached.

[0585] Step 3: Storing and analyzing handwritten data (server)

[0586] The server stores the received handwritten data in a database.

[0587] The server analyzes the handwritten data and extracts features such as character shape, pen pressure, and curve angle.

[0588] Step 4: Feature Learning (Server)

[0589] The server uses a generative AI model to learn the characteristics of the extracted handwritten data.

[0590] The learned features are stored in a database and used as a basis for generating handwritten-style characters for new input data.

[0591] Step 5: Acquiring Gaze Input Data (User)

[0592] A user uses an eye-gaze input device to select characters on the screen by gazing at them.

[0593] The user selects a string of characters (for example, "hello") by eye gaze input.

[0594] Step 6: Sending gaze input data (device)

[0595] The device analyzes the gaze input data and sends the selected string "Hello" to the server.

[0596] Step 7: Receiving a request to generate handwritten characters (server)

[0597] The server receives the text data "Hello" from the terminal.

[0598] The server starts processing as a request to generate handwritten-style characters based on this character data.

[0599] Step 8: Generate handwritten characters (server)

[0600] The server uses the generated AI model to convert the received text data "hello" into a handwritten version.

[0601] The server applies fluctuations to the pen pressure and curve angle to ensure that the generated handwritten characters are not unique.

[0602] Step 9: Determine the output format of the generated data (server)

[0603] The server converts the generated handwritten characters into output data in bitmap or vector format.

[0604] Define the output format and create the data to send back to the terminal.

[0605] Step 10: Returning handwritten character data (server)

[0606] The server returns the generated handwritten character data to the terminal.

[0607] Step 11: Displaying Handwritten Characters (Device)

[0608] The terminal receives the returned handwritten character data and displays it on the screen.

[0609] The user checks the displayed handwriting.

[0610] Step 12: Saving and using handwritten characters (user)

[0611] The user reviews the displayed handwriting and, if satisfied, saves or prints it.

[0612] Send it as a letter or message if necessary.

[0613] Example 1

[0614] 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."

[0615] For people with muscular dystrophy or those who have difficulty writing by hand, eye gaze input alone can be used to select and input characters, but it can lose the warmth and individuality of handwriting. Furthermore, existing eye gaze input systems do not provide an appropriate means for generating handwritten-style characters, making it difficult to reproduce the feel of handwriting.

[0616] 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.

[0617] In this invention, the server includes means for collecting past handwritten data and analyzing it to learn the characteristics of handwritten characters, means for receiving character data selected by a user using an eye-gaze input device, means for converting the received character data into a handwritten-style character based on the learned characteristics of handwritten characters, and means for displaying and saving the converted handwritten character data. This makes it possible to generate characters that have the warmth and individuality of handwriting without losing the convenience of eye-gaze input.

[0618] "Previous handwritten data" refers to character information previously handwritten by a user, and is data captured in a scanned or digital format.

[0619] "Analysis" refers to the process of extracting features such as character shape, pen pressure, and curve angle from handwritten data, allowing the generative AI model to learn.

[0620] "Characteristics of handwritten characters" refers to the unique expressive elements of handwriting, such as the character shape, writing pressure, and angle of curve contained in the handwritten data.

[0621] An "eye gaze input device" is a device that tracks the user's gaze and can use the gaze movement as data.

[0622] "Character data" refers to character information selected or input by the user through the eye-gaze input device.

[0623] "Conversion" refers to the process of regenerating received character data into a handwritten style based on the learned characteristics of handwritten characters.

[0624] "Display and save" refers to the function of displaying the generated handwritten-style character data on the device screen and, if necessary, saving the data.

[0625] "Adding fluctuations to the pen pressure and curve angle" refers to the process of adding small changes to the generated handwritten-style character data to give the characters a handwritten feel.

[0626] "Bitmap format" refers to a format that represents image data in pixel units.

[0627] "Vector format" refers to a format in which image data is expressed using mathematical formulas and geometric elements.

[0628] A "generative AI model" refers to a model that uses artificial intelligence technology to learn the characteristics of handwritten characters and generate new data.

[0629] The present invention is a system that uses an eye-gaze input device and a generative AI model to generate handwritten-style characters, providing the convenience of eye-gaze input without losing the warmth and individuality of handwriting. This system is specifically implemented using the following hardware and software.

[0630] Hardware

[0631] Scanner: A device that allows a user to convert past handwritten data into digital form.

[0632] Device (PC, tablet, etc.): A device used to capture handwritten data, analyze eye-gaze input data, and display and save handwritten-style character data.

[0633] Server: A device that analyzes and learns from handwritten data and generates handwritten-style characters.

[0634] Eye-gaze input device: A device that tracks the user's gaze and captures their gaze movements as data (e.g., Tobii Eye Tracker).

[0635] Printer: A device for printing the generated handwritten-style character data.

[0636] software

[0637] Database (e.g. MySQL): Software for storing handwritten data and learning data.

[0638] Generative AI model (e.g., Python TensorFlow): A model that learns the features of handwritten data and generates handwritten-style characters.

[0639] Eye-gaze applications (e.g., Tobii Pro Lab): Applications that analyze data from eye-gaze devices and select characters.

[0640] Image viewer (e.g. Adobe Reader): Software for displaying the generated handwritten-style character data.

[0641] System Overview

[0642] The system consists of the following main components:

[0643] 1. Handwriting data collection and learning (server)

[0644] A scanner captures data that a user has previously handwritten and stores it on the device. The device then sends the handwritten data to a server, which analyzes and learns from it. A generative AI model analyzes and learns features such as character shape, pen pressure, and curve angle. This learning data serves as the basis for generating user-style character shapes for new data.

[0645] 2. Character selection by eye gaze input (user, device)

[0646] The user uses an eye-gaze input device to select characters on the screen. The device analyzes the eye-gaze input data and sends the selected character string (e.g., "Hello") to the server.

[0647] 3. Generating handwritten-style characters (server)

[0648] Based on the character data received by the server, the generative AI model generates handwritten-style characters. The characteristics of handwritten data learned in the past are reflected. The server adds fluctuations to the pen pressure and curve angle to prevent the character shapes from becoming unique. The generated handwritten-style character data is returned to the device in bitmap or vector format.

[0649] 4. Display and save handwritten characters (device, user)

[0650] The terminal receives the handwritten-style character data and displays it on the screen. The user can check the displayed handwritten characters and save or print them as needed.

[0651] Specific examples

[0652] Example 1: Writing a birthday message

[0653] 1. Learning handwritten data (server)

[0654] The system scans a handwritten message written by the user in the past, such as "Happy Birthday," and sends it from the device to the server.

[0655] The server analyzes this handwritten data, and the generative AI model learns features such as character shape and writing pressure.

[0656] 2. Character selection (user, terminal)

[0657] The user uses gaze input to select the string they want to enter: "Happy Birthday."

[0658] The device sends the results of the gaze input to the server.

[0659] 3. Generating handwritten-style characters (server)

[0660] The server converts the string "Happy Birthday" selected through eye gaze input into handwritten text, adjusting the pen pressure and curve as needed based on past learning data.

[0661] The generated handwritten character data is sent to the terminal.

[0662] 4. Display and storage (device, user)

[0663] The terminal displays the handwritten-style characters on the display, and if the user is satisfied after checking them, they can save or print them.

[0664] The finalized message can be sent as a letter or an electronic message.

[0665] In this way, the system of the present invention allows a user to easily generate handwritten-style characters using an eye-gaze input device, and create messages that retain the warmth and individuality of handwriting.

[0666] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0667] Step 1: Collecting and training handwriting data

[0668] Input: A scanned image file of data previously handwritten by a user (e.g., "Happy Birthday").

[0669] Operation:

[0670] 1. The user uses a scanner to capture handwritten data as a digital image on the device.

[0671] 2. The device sends the image file to the server. Specifically, the image file is uploaded to the server using the HTTP protocol.

[0672] Output: Handwritten data stored on the server.

[0673] Specific behavior:

[0674] The server stores the received image file in a database.

[0675] The server's generative AI model analyzes this data and learns characteristics such as character shape, pen pressure, and curve angle. This learning data is then used to generate new data.

[0676] Step 2: Select text with gaze input

[0677] Input: Gaze data captured from the user's eye tracking device.

[0678] Operation:

[0679] 1. The user uses an eye-gaze input device (e.g., Tobii Eye Tracker) to select text on the screen.

[0680] 2. The eye-gaze input device tracks the user's gaze and detects character selection.

[0681] 3. The device analyzes the gaze data and identifies the selected string (e.g., "Hello").

[0682] 4. The device sends the selected string to the server using the HTTP protocol.

[0683] Output: The string data received by the server.

[0684] Specific behavior:

[0685] The device uses a Python script to analyze the gaze data and extract the selected string.

[0686] The terminal transmits the extracted character string data to the server as an HTTP request.

[0687] Step 3: Generate handwritten characters

[0688] Input: Character data received by the server (e.g. "Hello").

[0689] Operation:

[0690] 1. The server uses a generative AI model to generate handwritten-style characters based on the character data received.

[0691] 2. The server uses the learned data to convert the characters into handwritten style, adding fluctuations to the pen pressure and curve angle.

[0692] 3. Convert the generated handwritten character data into bitmap or vector format.

[0693] 4. The server sends the generated handwritten-style character data to the device using the HTTP protocol.

[0694] Output: Generated handwritten character data.

[0695] Specific behavior:

[0696] The server runs a generative AI model (e.g., Python TensorFlow) and generates handwritten-style characters based on the input character data.

[0697] The server converts the generated handwritten-style character data into bitmap or vector format and sends it to the terminal as an HTTP response.

[0698] Step 4: Display and save handwritten text

[0699] Input: Handwritten-style character data received by the device.

[0700] Operation:

[0701] 1. The device displays the handwritten character data received from the server. Specifically, an image viewer (e.g., Adobe Reader) is used.

[0702] 2. The user checks the displayed handwritten characters and, if satisfied, saves or prints them.

[0703] 3. If you choose to save, the device will save the data to local storage or a cloud service. If you choose to print, the device will print it out.

[0704] Output: Saved handwriting data or printed handwriting.

[0705] Specific behavior:

[0706] The terminal displays the received handwritten-style character data using an image viewer.

[0707] The user clicks the save button to save the data or the print button to print it out on a printer.

[0708] Through the specific actions performed at each step, users can generate handwritten-style characters using only their gaze, easily creating messages that retain the warmth and individuality of handwriting.

[0709] (Application example 1)

[0710] 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."

[0711] In systems that generate handwritten-style characters using eye gaze input, users can easily create characters that have the warmth and individuality of handwriting. However, the lack of a function to apply this handwritten-style character to video or image content makes it difficult for users to easily create practical content. Therefore, there is a need not only for generating handwritten-style characters using eye gaze input, but also for an efficient means to apply them to video and images.

[0712] 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.

[0713] In this invention, the server includes means for collecting past handwritten data and analyzing it to learn the characteristics of handwritten characters, means for receiving character data selected by a user using an eye-gaze input device, means for converting the received character data into a handwritten-style character based on the learned characteristics of the handwritten characters, means for outputting the converted handwritten character data, and means for applying handwritten-style characters to image or video content based on the eye-gaze data collected using the eye-gaze input device. This allows a user to easily apply handwritten-style characters created using eye-gaze input to video or image content, thereby enabling the creation of original, unique content.

[0714] "Past handwritten data" refers to character information previously handwritten by a user, and is data that the learning model uses to analyze handwriting characteristics.

[0715] An "eye gaze input device" is a device that detects the movement of a user's eye gaze and uses that information as input data.

[0716] "Handwritten character features" refer to the individual attributes of handwritten characters, such as character shape, pen pressure, and curve angle, and by analyzing and learning these, it becomes possible to generate handwritten-style characters.

[0717] "Received character data" refers to character information selected by the user that is acquired from the eye-gaze input device, and handwritten-style characters are generated based on this data.

[0718] "Converting to handwritten style" refers to converting received character data into a character format that has the warmth and individuality unique to handwriting, based on past handwritten data.

[0719] "Handwritten-style character data" refers to character information that has been converted to look like handwritten text by a generative AI model that reflects the characteristics of past handwritten data.

[0720] "Gaze data" refers to the movement and position information of a user's gaze collected by an eye-gaze input device, and is used for character selection, etc.

[0721] "Image or video content" refers to media content in the form of still images or videos, to which handwritten-style characters can be added.

[0722] The present invention is a system that uses eye gaze input to generate handwritten-style characters and apply these characters to image or video content. Each component of the present invention and its operation are described in detail below.

[0723] System configuration

[0724] The system consists of the following main components:

[0725] 1. Handwriting data collection and learning

[0726] 2. Character selection using eye gaze input

[0727] 3. Generating handwritten-style characters

[0728] 4. Applying handwritten text to content

[0729] 5. Output and Saving

[0730] Handwriting data collection and learning

[0731] Users scan or digitally import previously written handwriting data into their device. The device then sends this handwriting data to a cloud server. The server stores the handwriting data in a database, and the generative AI model analyzes and learns features such as character shape, pen pressure, and curve angle. This learning data serves as a reference for generating user-style character shapes for new gaze input data.

[0732] Selecting characters using eye gaze input

[0733] The user selects characters on the screen by guiding their gaze using an eye-gaze input device (e.g., a smartphone camera). The device analyzes the eye-gaze input and transmits the selected character data to a cloud server.

[0734] Generating handwritten characters

[0735] The server uses a generative AI model to generate handwritten-style characters based on the received character data. The server incorporates the characteristics of previous handwritten data, adding fluctuations to the pen pressure and curve angle. The generated handwritten-style character data is then sent back to the device in bitmap or vector format.

[0736] Applying handwritten text to content

[0737] The device applies the received handwritten-style character data to the image or video content specified by the user, thereby generating content containing a unique handwritten-style message.

[0738] Output and save

[0739] The generated content is displayed on the device's display, and the user can check the displayed content and save or share it if satisfied.

[0740] Hardware and software used

[0741] Camera: Uses your phone's camera to detect your gaze.

[0742] Cloud server: A server that learns handwritten data and processes the generation of handwritten-style characters, and also includes a generative AI model.

[0743] Generative AI model: A model that generates handwritten-style characters using deep learning frameworks such as TensorFlow.

[0744] Examples of concrete examples and prompts

[0745] Examples:

[0746] The user uses an eye-gaze input device to select the word "Thank you." The selected text is sent to a cloud server, where a generative AI model converts it into handwritten-style text based on past handwriting data. This handwritten-style text is then applied to the video message the user is creating, and the "Thank you" part is displayed in handwritten style.

[0747] Example prompt sentence:

[0748] "Using the gaze-selected string 'Thank you', generate handwritten-style characters based on past handwriting data."

[0749] This invention allows users to easily generate handwritten-style characters using an eye-gaze input device and apply them to content, making it possible to create attractive digital content while preserving the warmth and individuality of handwriting.

[0750] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0751] Step 1:

[0752] The user scans or digitally imports previously handwritten data into the device. The device then sends the data to a cloud server, where it stores the handwritten data in a database. The generative AI model analyzes and learns from the character's shape, pen pressure, curve angle, and other characteristics.

[0753] Input: Handwritten data

[0754] Data processing: Analysis of character shape, pen pressure, and curve angle

[0755] Output: Trained model

[0756] Step 2:

[0757] The user selects characters on the screen by using the eye-gaze input device. The terminal acquires the user's eye-gaze data through the eye-gaze input device and transmits the selected character data to the cloud server.

[0758] Input: Gaze data, selected character data

[0759] Data processing: Analysis of gaze data, generation of text data

[0760] Output: Received character data

[0761] Step 3:

[0762] The server uses a generative AI model based on the received character data to generate handwritten-style characters. It adds variations to the character shape, pen pressure, and curve angle to reflect the characteristics of previous handwritten data. The generated handwritten-style character data is then sent back to the device in bitmap or vector format.

[0763] Input: Received character data, trained model

[0764] Data processing: Generating handwritten characters based on character shape, pressure, and curve angle

[0765] Output: Handwritten character data

[0766] Step 4:

[0767] The device applies the received handwritten-style character data to the image or video content specified by the user, drawing the handwritten-style characters on the content and adjusting the layout as needed.

[0768] Input: Handwritten text, image or video content

[0769] Data processing: Drawing handwritten characters, adjusting layout

[0770] Output: Content with handwritten text applied

[0771] Step 5:

[0772] The device displays the generated content on the display for the user to review, and if the user is satisfied, they can save the generated content and share it as needed.

[0773] Input: Content with handwritten text applied

[0774] Data processing: Displaying content, user confirmation

[0775] Output: Saved content, shared content

[0776] 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.

[0777] The present invention combines an emotion engine with a system that allows users who have difficulty writing by hand to use eye gaze input to generate handwritten-style characters and express the warmth and individuality of handwriting. The system of the present invention analyzes past handwritten data to learn the characteristics of handwritten characters, and makes it possible to convert character data acquired by eye gaze input into handwritten-style characters based on the emotion engine.

[0778] System Overview

[0779] The system consists of the following main components:

[0780] 1. Handwritten data analysis and learning (server)

[0781] 2. Character selection by eye gaze input (user, device)

[0782] 3. Emotion recognition using emotion engine (server)

[0783] 4. Generating handwritten-style characters (server)

[0784] 5. Display and save handwritten style characters (device, user)

[0785] Program processing

[0786] Learning Phase

[0787] Handwriting data collection and learning (server)

[0788] Data that a user has previously handwritten is scanned or digitally imported into the terminal.

[0789] The device sends handwritten data to a server, which stores it in a database and a generative AI model analyzes and learns features such as character shape, pen pressure, and curve angle.

[0790] This training data serves as a reference for generating user-style character shapes for new input data.

[0791] Gaze input phase

[0792] Character selection (user, terminal)

[0793] A user uses an eye-gaze input device to select characters on the screen by gazing at them.

[0794] The device analyzes the gaze input data and sends the selected string (e.g., "Hello") to the server.

[0795] Emotion Recognition Phase

[0796] Emotion Recognition (Server)

[0797] The emotion engine installed on the server analyzes the user's gaze, facial expressions, voice input, etc. to recognize their current emotional state.

[0798] The recognized emotion data is reflected in the handwritten character generation process.

[0799] Handwritten character generation phase

[0800] Handwritten character generation (server)

[0801] Based on the character data received by the server, the generative AI model generates handwritten-style characters, incorporating the characteristics of handwritten data learned in the past and emotional information from the emotion engine.

[0802] Fluctuations in pen pressure, curve angle, and even the color and style of the text are added based on emotion.

[0803] The generated handwritten character data is sent back to the terminal in bitmap or vector format.

[0804] Result output phase

[0805] Display and save handwritten style characters (device, user)

[0806] The terminal receives the handwritten character data and displays it on the screen.

[0807] The user can review the displayed handwriting and save or print it if desired.

[0808] Specific examples

[0809] Example 1: Writing a birthday message

[0810] 1. Learning handwritten data (server)

[0811] The system scans a handwritten message written by the user in the past, such as "Happy Birthday," and sends it from the device to the server.

[0812] The server analyzes this handwritten data, and the generative AI model learns features such as character shape and writing pressure.

[0813] 2. Character selection (user, terminal)

[0814] The user uses gaze input to select the string they want to enter: "Happy Birthday."

[0815] The device sends the results of the gaze input to the server.

[0816] 3. Emotion Recognition (Server)

[0817] The server analyzes the user's gaze and facial expressions to recognize their current emotional state (e.g., joy).

[0818] The recognized emotion data is reflected in the handwritten character generation process.

[0819] 4. Generating handwritten-style characters (server)

[0820] The server converts the string "Happy Birthday" selected through eye gaze input into handwritten text. Past learning data and recognized emotional information (joy) are reflected in the conversion, adding fluctuations to the pressure and curve of the characters, as well as brightening the colors and style.

[0821] The generated handwritten character data is sent to the terminal.

[0822] 5. Display and storage (device, user)

[0823] The terminal displays the handwritten-style characters on the display, and if the user is satisfied after checking them, they can save or print them.

[0824] The finalized message can be sent as a letter or an electronic message.

[0825] In this way, the system of the present invention allows users to use eye gaze input to generate handwritten-style characters that reflect their own emotions, allowing them to create messages that incorporate the warmth and individuality of handwriting, as well as emotional expression.

[0826] The processing flow will be explained below.

[0827] Step 1: Collect handwritten data (user)

[0828] Scan or photograph letters or messages that the user has previously written.

[0829] The user imports scanned data or photos into the device.

[0830] Step 2: Send handwritten data (device)

[0831] The handwritten data captured by the terminal is sent to the server.

[0832] When sending, the data is sent with the user ID and timestamp attached.

[0833] Step 3: Storing and analyzing handwritten data (server)

[0834] The server stores the received handwritten data in a database.

[0835] The server analyzes the handwritten data and extracts features such as character shape, pen pressure, and curve angle.

[0836] Step 4: Feature Learning (Server)

[0837] The server uses a generative AI model to learn the characteristics of the extracted handwritten data.

[0838] The learned features are stored in a database and used as a basis for generating handwritten-style characters for new input data.

[0839] Step 5: Acquiring Gaze Input Data (User)

[0840] A user uses an eye-gaze input device to select characters on the screen by gazing at them.

[0841] The user selects a string of characters (for example, "hello") by eye gaze input.

[0842] Step 6: Sending gaze input data (device)

[0843] The device analyzes the gaze input data and sends the selected string "Hello" to the server.

[0844] Step 7: Emotion Recognition (Server)

[0845] The emotion engine installed on the server analyzes the user's gaze, facial expressions, voice input, etc. to recognize their current emotional state.

[0846] The recognized emotion data is reflected in the handwritten character generation process.

[0847] Step 8: Receiving a request to generate handwritten characters (server)

[0848] The server receives the text data "Hello" and emotion data from the terminal.

[0849] The server starts processing as a request to generate handwritten-style characters based on this character data and emotion data.

[0850] Step 9: Generate handwritten characters (server)

[0851] The server uses the generative AI model to convert the received text data "Hello" into handwritten text, incorporating the characteristics of handwritten data learned in the past and emotional information from the emotion engine.

[0852] Fluctuations in pen pressure, curve angle, and even the color and style of the text are added based on emotion.

[0853] Step 10: Determine the output format of the generated data (server)

[0854] The server converts the generated handwritten characters into output data in bitmap or vector format.

[0855] Define the output format and create the data to send back to the terminal.

[0856] Step 11: Returning handwritten character data (server)

[0857] The server returns the generated handwritten character data to the terminal.

[0858] Step 12: Displaying Handwritten Characters (Device)

[0859] The terminal receives the returned handwritten character data and displays it on the screen.

[0860] The user checks the displayed handwriting.

[0861] Step 13: Saving and using handwritten characters (user)

[0862] The user reviews the displayed handwriting and, if satisfied, saves or prints it.

[0863] Send it as a letter or message if necessary.

[0864] Example 2

[0865] 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."

[0866] With current text input systems, many users find it difficult to capture the warmth, individuality, and emotion of handwriting. Furthermore, for users of eye-gaze input devices, there are limited ways to easily create handwritten-style text. This makes it difficult to create emotionally charged messages using eye-gaze input.

[0867] 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.

[0868] In this invention, the server includes means for collecting past handwritten data and analyzing it to learn the characteristics of handwritten characters, means for receiving character data selected by a user using an eye-gaze input device, means for analyzing the user's eye movements and facial expressions to recognize an emotional state, means for converting the received character data into handwritten-style characters based on the learned handwritten character characteristics and the recognized emotional state, and means for outputting the converted handwritten character data, thereby enabling a user to generate handwritten-style characters through eye-gaze input and create an emotional message.

[0869] "Handwritten data" refers to digital data of characters and figures handwritten by a user.

[0870] An "eye gaze input device" is a device for tracking the movement of a user's eye gaze and acquiring that data.

[0871] "Character data" refers to digital information of a character string input by a user, acquired through an eye-gaze input device or other input means.

[0872] An "emotion engine" is software or hardware that has the function of recognizing a user's emotional state by analyzing the user's gaze movements, facial expressions, voice input, etc.

[0873] The "means for converting to a handwritten-like form" is a system or program for converting digital character data to a handwritten-like form based on the characteristics of the handwritten data.

[0874] "Pen pressure" refers to the strength of pressure that the pen applies to the paper when writing by hand, and is one of the characteristics of handwriting.

[0875] The "angle of the curve" refers to the angle of the curved part of handwritten characters, and is a feature that expresses the individuality of handwriting.

[0876] "Fluctuation" refers to the randomness in the pressure and angle of the curve of handwritten characters, and is a variable element used to express the naturalness and individuality of handwriting.

[0877] A "generative AI model" is an artificial intelligence model trained to analyze the characteristics of handwritten data and generate new handwritten-style characters based on that data.

[0878] A "bitmap" is a digital image file format that makes up an image on a pixel-by-pixel basis.

[0879] A "vector format" is a digital image file format that represents an image using mathematical formulas such as points, lines, and curves.

[0880] This invention combines an emotion engine with a system that allows users who have difficulty writing by hand to generate handwritten-style characters using an eye-gaze input device, thereby expressing the warmth and individuality of handwriting. This system consists of the following main components:

[0881] Handwriting data collection and learning

[0882] 1. Handwritten data collection

[0883] Users can scan previously handwritten data or write it on a digital device such as a tablet, for example, by scanning special messages or everyday notes and converting them into digital form.

[0884] The device stores this digital handwriting data and sends it to a server, where it uploads the data using its internet connection.

[0885] 2. Analysis and learning of handwritten data

[0886] The server stores the received handwritten data in a database. The handwritten feature information includes the character shape, writing pressure, and curve angle.

[0887] A generative AI model analyzes this data and learns the unique characteristics of the user's handwriting.

[0888] Selecting characters using eye gaze input

[0889] 3. Selecting the characters

[0890] The user wears an eye-gaze input device and the movement of the user's eyes is tracked. For example, an eye tracker is a commonly used eye-gaze input device.

[0891] The device analyzes the data from the eye-gaze input device and recognizes the characters the user is looking at. The user selects a character string, such as "hello," from a list of characters displayed on the screen.

[0892] Emotion recognition

[0893] 4. Emotional Recognition

[0894] The emotion engine installed on the server analyzes the user's gaze, facial expression, and, if necessary, voice input to recognize their current emotional state, which can include, for example, "happiness" or "sadness."

[0895] The recognized emotion data is reflected in the handwritten character generation process.

[0896] Generating handwritten characters

[0897] 5. Generating handwritten-style characters

[0898] Based on the character data and emotion data received by the server, the generative AI model generates handwritten-style characters. The generative AI model applies the characteristics of handwritten data it has learned in the past, and adjusts the pen pressure, curve angle, and even color to reflect the emotion information.

[0899] For example, when you type "hello," the pen pressure will fluctuate based on the emotional information, and the emotion will also be reflected in the color and style.

[0900] Display and save handwritten characters

[0901] 6. Display and save handwritten characters

[0902] The terminal receives the generated handwritten-style character data and displays it on the display.

[0903] The user checks the displayed handwritten-style characters and, if satisfied, saves or prints them. The device uses the save function to save them as a digital file or connects to a printer to print them.

[0904] Specific examples

[0905] Example: How to write a birthday message

[0906] 1. Learning from handwritten data

[0907] The system scans past messages written by users saying "Happy Birthday" and stores them on the device, which then sends this data to a server.

[0908] The server analyzes this data, and the generative AI model learns the character shape, pressure, and curve angle.

[0909] 2. Selecting the characters

[0910] The user uses the eye-gaze input device to select the character string "Happy Birthday" with their eyes.

[0911] The device analyzes the gaze data and sends the selected character string to the server.

[0912] 3. Emotional Recognition

[0913] The server analyzes gaze movements and facial expressions to recognize the current emotional state (e.g., joy).

[0914] 4. Generating handwritten-style characters

[0915] The server converts the text "Happy Birthday" into handwritten text, adjusting the pen pressure, curve, color, etc. to reflect the emotional information (joy).

[0916] The server transmits the generated handwritten-style character data to the terminal.

[0917] 5. Display and save

[0918] The device displays handwritten-style characters on the screen for the user to confirm.

[0919] The user selects save or print and the terminal performs the action.

[0920] This system allows users to easily create unique, handwritten-style characters by using eye-gaze input, and to create emotive messages.

[0921] Prompt Sentence Examples

[0922] "I want to scan past handwritten messages and have the server learn from them."

[0923] "I want to use my eye-gaze device to select text on the screen."

[0924] "I want to analyze the current emotion and reflect it in handwritten-style characters."

[0925] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0926] Step 1: Collecting handwritten data

[0927] Input: A handwritten message written by the user in the past

[0928] What it does: Scans data that a user has previously handwritten or writes on a digital device such as a tablet. For example, scans a birthday card or letter and converts it into a digital format.

[0929] Output: Handwritten data in digital format

[0930] Step 2: Send handwritten data

[0931] Input: Handwritten data in digital format

[0932] Specific operation: The handwritten data captured by the device in a scanned or digital format is sent to a server via the Internet, and the handwritten data is uploaded to cloud storage by calling an API.

[0933] Output: Handwritten data stored on the server

[0934] Step 3: Analyzing and learning handwritten data

[0935] Input: Handwritten data stored on the server

[0936] Specific operation: The server inputs the handwritten data it receives into the generative AI model, which analyzes features such as character shape, pen pressure, and curve angle. The generative AI model learns from this data, extracts and saves the user's unique handwritten character characteristics.

[0937] Output: Trained handwritten character feature data

[0938] Step 4: Selecting the text

[0939] Input: User's gaze input data

[0940] Specific operation: The user wears an eye-gaze input device and selects characters displayed on the screen with their eyes. The device analyzes the data from the eye-gaze input device in real time and identifies the character the user is looking at. For example, the character string "Hello" is selected.

[0941] Output: String selected by gaze

[0942] Step 5: Sending characters

[0943] Input: Gaze-selected string

[0944] Specific operation: The device sends the selected string to the server, and the character data is uploaded to the server via the Internet.

[0945] Output: Character data received by the server

[0946] Step 6: Recognize emotions

[0947] Input: User's gaze or facial expression data

[0948] Specific behavior: The server's emotion engine analyzes eye movements and facial expressions, and optionally voice input data, to identify the current emotional state (e.g., joy or sadness).

[0949] Output: Recognized emotional state data

[0950] Step 7: Generate handwritten characters

[0951] Input: Character data received by the server, trained handwriting feature data, recognized emotional state data

[0952] How it works: Based on the character data and emotion data received by the server, the generative AI model generates handwritten-style characters. It applies the characteristics of handwritten data it has learned in the past and adjusts the pen pressure, curve angle, color, etc. to reflect the recognized emotion information. For example, it converts the string "hello" into a handwritten-style character and adds bright colors and styles based on the emotion of joy.

[0953] Output: Generated handwritten character data

[0954] Step 8: Send handwritten text

[0955] Input: Generated handwritten-style character data

[0956] Specific operation: The server generates handwritten character data and sends it to the device in bitmap or vector format. The data is then returned via the Internet.

[0957] Output: Handwritten character data received by the device

[0958] Step 9: Displaying handwritten characters

[0959] Input: Handwritten-style character data received by the terminal

[0960] Specific operation: The handwritten character data received by the device is displayed on the display, and a graphic representation is provided so that the user can check it in real time.

[0961] Output: Handwritten characters displayed on the screen

[0962] Step 10: Save and print your handwritten text

[0963] Input: Handwritten characters displayed on the screen

[0964] Specific operation: The user checks the handwritten-style characters and selects whether to save or print them. The device uses the save function to save them as a digital file or connects to a printer to print them.

[0965] Output: Saved handwriting file or printed handwriting

[0966] (Application example 2)

[0967] 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."

[0968] When users who have difficulty writing by hand use eye-gaze input to generate handwritten-like characters, it is difficult to express warmth and individuality in the characters. Creating messages that reflect the user's emotions is also a challenge. In food delivery applications, there is a need for technology that can generate handwritten-like messages using eye-gaze input devices and convey emotions such as gratitude and encouragement.

[0969] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting past handwritten data and analyzing it to learn the characteristics of handwritten characters, means for receiving character data selected by a user using an eye-gaze input device, means for converting the received character data into handwritten-style characters based on the learned characteristics of handwritten characters, means for outputting the converted handwritten character data, and means for recognizing the emotional state of the user and adjusting the characteristics of handwritten-style characters based on the emotion. This enables a user to create a handwritten-style message that reflects their personality and emotion through the eye-gaze input device.

[0970] "Past handwritten data" refers to character information that a user has handwritten in the past, and serves as basic data for analyzing and learning the characteristics of handwritten characters.

[0971] "Handwritten character features" refer to individual attributes of handwritten characters, such as the shape, pressure, and angle of the curve, and are used as reference when generating handwritten-style characters.

[0972] An "eye gaze input device" is a device that includes hardware and software for tracking the movement of a user's eye gaze.

[0973] "Character data" refers to character information entered or selected by the user, and is the original data that is converted into handwritten-style characters.

[0974] The "means for converting to look like handwriting" refers to the process of processing character data entered by eye gaze input to look like handwriting based on the collected and learned characteristics of the handwriting data.

[0975] "Converted handwritten character data" refers to character information that has been processed to look like handwriting, and is output in bitmap or vector format.

[0976] An "emotional state" refers to the emotion a user is feeling at a particular moment, and may include joy, sadness, anger, etc.

[0977] "Means for adjusting the characteristics of handwritten-style characters based on emotions" refers to a process of analyzing a user's emotional state and adjusting the pressure, curve angle, color, style, etc. of the handwritten characters based on that information.

[0978] The present invention is a system that uses eye gaze input to generate handwritten-style characters, particularly in food delivery applications, allowing users to send thank you or other emotional messages to delivery staff or stores.

[0979] System Overview

[0980] The system consists of the following main components:

[0981] 1. Handwritten data analysis and learning (server)

[0982] 2. Character selection by eye gaze input (user, device)

[0983] 3. Emotion recognition using emotion engine (server)

[0984] 4. Generating handwritten-style characters (server)

[0985] 5. Display and save handwritten style characters (device, user)

[0986] Program Description

[0987] Hardware and Software

[0988] Hardware: Eye tracking device, camera (for emotion recognition)

[0989] Software: OpenCV (image processing), EmotionRecognizer (emotion recognition model), GazeTracking (eye tracking library), HandwritingModel (handwriting generation model)

[0990] Analysis and learning of handwritten data

[0991] The server collects, analyzes, and learns from handwritten data that the user has written. Specifically, the generative AI model learns from the collected handwritten data and analyzes the characteristics of handwritten characters (character shape, writing pressure, angle of curve, etc.).

[0992] Selecting characters using eye gaze input

[0993] The user selects characters on the screen using the eye-gaze input device. The device analyzes the eye-gaze input and sends the selected character data to the server. For example, if the user selects the character string "Thank you," this data is sent.

[0994] emotion recognition

[0995] The emotion engine installed on the server analyzes the user's facial expressions and gaze data acquired from the camera to recognize their current emotional state, which can include joy, sadness, surprise, etc.

[0996] Generating handwritten characters

[0997] The server uses the received character data and the emotion information obtained from the emotion engine to generate handwritten-style characters. In this generation process, the pen pressure, curve angle, color, etc. are adjusted based on the characteristics of the handwritten data and the emotion information.

[0998] Display and save

[0999] The device receives handwritten-style character data sent from the server and displays it on the screen. The user can check it and save or send it if they are satisfied. This function allows users to input and send a handwritten-style message of thanks when ordering food delivery.

[1000] Specific examples

[1001] For example, consider a scenario where a user places an order using a food delivery application and sends a thank you message to the delivery person. When the user inputs "Thank you" using the eye-gaze input device, the system recognizes the user's emotion and generates a handwritten-style message that reflects that emotion.

[1002] An example of a prompt to be input to the generative AI model is as follows:

[1003] "Generate handwritten-style characters based on the string selected using gaze input and the recognized emotion information."

[1004] for example,

[1005] "selected_text = 'Thank you'

[1006] recognized_emotion = 'joy' The user's emotional state recognized by the AI

[1007] handwritten_text = generate_handwritten_text(selected_text, recognized_emotion)

[1008] print_handwritten_text(handwritten_text)”

[1009] In this way, a handwritten-style message that visually reflects the user's emotions can be generated and used when ordering food delivery.

[1010] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1011] Step 1:

[1012] Collects and learns handwritten data. The server collects handwritten data from users and stores it in a database. The generative AI model then analyzes this data and learns the characteristics of handwritten characters (such as character shape, pressure, and curve angle). The input is handwritten data, and the output is the learned handwritten character characteristics. Specific operations include scanning handwritten data, uploading the digital data, saving it to a database, and training the AI ​​model.

[1013] Step 2:

[1014] Character selection is performed using gaze input. The user uses an eye-gaze input device to select characters on the screen with their gaze. The device analyzes the gaze input data and sends the selected character data to the server. The input is gaze data, and the output is the selected character data. Specific operations include calibrating the eye-tracking device, collecting and analyzing gaze data, and sending the character selection results to the server.

[1015] Step 3:

[1016] Recognizes emotions. The server analyzes the user's facial expression and gaze data acquired from the camera and uses an emotion engine to recognize the user's current emotional state. The input is facial images and gaze data, and the output is recognized emotion information. Specific operations include capturing facial images with the camera, and image analysis and emotion classification with the emotion engine.

[1017] Step 4:

[1018] Generates handwritten-style characters. The server generates handwritten-style characters using a generative AI model based on the received character data and emotion information obtained from the emotion engine. The input is character data and emotion information, and the output is handwritten-style character data. Specific operations include generating handwritten-style characters using an AI model, adjusting pen pressure and curve angle, and changing colors.

[1019] Step 5:

[1020] This displays and saves handwritten-style characters. The terminal receives handwritten-style character data sent from the server and displays it on the screen. The user can check it and save or send it if they are satisfied. The input is handwritten-style character data, and the output is the displayed handwritten-style characters. Specific operations include converting the character data to bitmap or vector format, displaying it on the screen, and saving it to a file.

[1021] 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.

[1022] 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.

[1023] 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.

[1024] [Third embodiment]

[1025] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1026] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1027] 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).

[1028] 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.

[1029] 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.

[1030] 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).

[1031] 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.

[1032] 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.

[1033] 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.

[1034] 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.

[1035] 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.

[1036] 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."

[1037] This invention is a system that enables users who have difficulty writing by hand, such as those with muscular dystrophy, to generate handwritten-style characters using eye gaze input, thereby expressing the warmth and individuality of handwriting without compromising the convenience of eye gaze input. The system of this invention analyzes and learns from past handwritten data, and makes it possible to convert character data acquired through eye gaze input into handwritten-style characters.

[1038] System Overview

[1039] The system consists of the following main components:

[1040] 1. Handwritten data analysis and learning (server)

[1041] 2. Character selection by eye gaze input (user, device)

[1042] 3. Generating handwritten-style characters (server)

[1043] 4. Display and save handwritten characters (device, user)

[1044] Program processing

[1045] Learning Phase

[1046] Handwriting data collection and learning (server)

[1047] Data that a user has previously handwritten is scanned or digitally imported into the terminal.

[1048] The device sends handwritten data to a server, which stores it in a database and a generative AI model analyzes and learns features such as character shape, pressure, and curve angle.

[1049] This training data serves as a reference for generating user-style character shapes for new input data.

[1050] Gaze input phase

[1051] Character selection (user, terminal)

[1052] A user uses an eye-gaze input device to select characters on the screen by gazing at them.

[1053] The device analyzes the gaze input data and sends the selected string (e.g., "Hello") to the server.

[1054] Handwritten character generation phase

[1055] Handwritten character generation (server)

[1056] Based on the character data received by the server, the generative AI model generates handwritten-style characters, incorporating the characteristics of handwritten data it has learned from the past.

[1057] The server adds fluctuations to the pen pressure and curve angle to prevent the character shape from being unique.

[1058] The generated handwritten character data is sent back to the terminal in bitmap or vector format.

[1059] Result output phase

[1060] Display and save handwritten style characters (device, user)

[1061] The terminal receives the handwritten character data and displays it on the screen.

[1062] The user can review the displayed handwriting and save or print it if desired.

[1063] Specific examples

[1064] Example 1: Writing a birthday message

[1065] 1. Learning handwritten data (server)

[1066] The system scans a handwritten message written by the user in the past, such as "Happy Birthday," and sends it from the device to the server.

[1067] The server analyzes this handwritten data, and the generative AI model learns features such as character shape and writing pressure.

[1068] 2. Character selection (user, terminal)

[1069] The user uses gaze input to select the string they want to enter: "Happy Birthday."

[1070] The device sends the results of the gaze input to the server.

[1071] 3. Generating handwritten-style characters (server)

[1072] The server converts the string "Happy Birthday" selected through eye gaze input into handwritten text, adjusting the pen pressure and curve as needed based on past learning data.

[1073] The generated handwritten character data is sent to the terminal.

[1074] 4. Display and storage (device, user)

[1075] The terminal displays the handwritten-style characters on the display, and if the user is satisfied after checking them, they can save or print them.

[1076] The finalized message can be sent as a letter or an electronic message.

[1077] In this way, the system of the present invention allows users to easily generate handwritten-style characters using an eye-gaze input device, allowing them to create messages that retain the warmth and individuality of handwriting.

[1078] The processing flow will be explained below.

[1079] Step 1: Collect handwritten data (user)

[1080] Scan or photograph letters or messages that the user has previously written.

[1081] The user imports scanned data or photos into the device.

[1082] Step 2: Send handwritten data (device)

[1083] The handwritten data captured by the terminal is sent to the server.

[1084] When sending, the data is sent with the user ID and timestamp attached.

[1085] Step 3: Storing and analyzing handwritten data (server)

[1086] The server stores the received handwritten data in a database.

[1087] The server analyzes the handwritten data and extracts features such as character shape, pen pressure, and curve angle.

[1088] Step 4: Feature Learning (Server)

[1089] The server uses a generative AI model to learn the characteristics of the extracted handwritten data.

[1090] The learned features are stored in a database and used as a basis for generating handwritten-style characters for new input data.

[1091] Step 5: Acquiring Gaze Input Data (User)

[1092] A user uses an eye-gaze input device to select characters on the screen by gazing at them.

[1093] The user selects a string of characters (for example, "hello") by eye gaze input.

[1094] Step 6: Sending gaze input data (device)

[1095] The device analyzes the gaze input data and sends the selected string "Hello" to the server.

[1096] Step 7: Receiving a request to generate handwritten characters (server)

[1097] The server receives the text data "Hello" from the terminal.

[1098] The server starts processing as a request to generate handwritten-style characters based on this character data.

[1099] Step 8: Generate handwritten characters (server)

[1100] The server uses the generated AI model to convert the received text data "hello" into a handwritten version.

[1101] The server applies fluctuations to the pen pressure and curve angle to ensure that the generated handwritten characters are not unique.

[1102] Step 9: Determine the output format of the generated data (server)

[1103] The server converts the generated handwritten characters into output data in bitmap or vector format.

[1104] Define the output format and create the data to send back to the terminal.

[1105] Step 10: Returning handwritten character data (server)

[1106] The server returns the generated handwritten character data to the terminal.

[1107] Step 11: Displaying Handwritten Characters (Device)

[1108] The terminal receives the returned handwritten character data and displays it on the screen.

[1109] The user checks the displayed handwriting.

[1110] Step 12: Saving and using handwritten characters (user)

[1111] The user reviews the displayed handwriting and, if satisfied, saves or prints it.

[1112] Send it as a letter or message if necessary.

[1113] Example 1

[1114] 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."

[1115] For people with muscular dystrophy or those who have difficulty writing by hand, eye gaze input alone can be used to select and input characters, but it can lose the warmth and individuality of handwriting. Furthermore, existing eye gaze input systems do not provide an appropriate means for generating handwritten-style characters, making it difficult to reproduce the feel of handwriting.

[1116] 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.

[1117] In this invention, the server includes means for collecting past handwritten data and analyzing it to learn the characteristics of handwritten characters, means for receiving character data selected by a user using an eye-gaze input device, means for converting the received character data into a handwritten-style character based on the learned characteristics of handwritten characters, and means for displaying and saving the converted handwritten character data. This makes it possible to generate characters that have the warmth and individuality of handwriting without losing the convenience of eye-gaze input.

[1118] "Previous handwritten data" refers to character information previously handwritten by a user, and is data captured in a scanned or digital format.

[1119] "Analysis" refers to the process of extracting features such as character shape, pen pressure, and curve angle from handwritten data, allowing the generative AI model to learn.

[1120] "Characteristics of handwritten characters" refers to the unique expressive elements of handwriting, such as the character shape, writing pressure, and angle of curve contained in the handwritten data.

[1121] An "eye gaze input device" is a device that tracks the user's gaze and can use the gaze movement as data.

[1122] "Character data" refers to character information selected or input by the user through the eye-gaze input device.

[1123] "Conversion" refers to the process of regenerating received character data into a handwritten style based on the learned characteristics of handwritten characters.

[1124] "Display and save" refers to the function of displaying the generated handwritten-style character data on the device screen and, if necessary, saving the data.

[1125] "Adding fluctuations to the pen pressure and curve angle" refers to the process of adding small changes to the generated handwritten-style character data to give the characters a handwritten feel.

[1126] "Bitmap format" refers to a format that represents image data in pixel units.

[1127] "Vector format" refers to a format in which image data is expressed using mathematical formulas and geometric elements.

[1128] A "generative AI model" refers to a model that uses artificial intelligence technology to learn the characteristics of handwritten characters and generate new data.

[1129] The present invention is a system that uses an eye-gaze input device and a generative AI model to generate handwritten-style characters, providing the convenience of eye-gaze input without losing the warmth and individuality of handwriting. This system is specifically implemented using the following hardware and software.

[1130] Hardware

[1131] Scanner: A device that allows a user to convert past handwritten data into digital form.

[1132] Device (PC, tablet, etc.): A device used to capture handwritten data, analyze eye-gaze input data, and display and save handwritten-style character data.

[1133] Server: A device that analyzes and learns from handwritten data and generates handwritten-style characters.

[1134] Eye-gaze input device: A device that tracks the user's gaze and captures their gaze movements as data (e.g., Tobii Eye Tracker).

[1135] Printer: A device for printing the generated handwritten-style character data.

[1136] software

[1137] Database (e.g. MySQL): Software for storing handwritten data and learning data.

[1138] Generative AI model (e.g., Python TensorFlow): A model that learns the features of handwritten data and generates handwritten-style characters.

[1139] Eye-gaze applications (e.g., Tobii Pro Lab): Applications that analyze data from eye-gaze devices and select characters.

[1140] Image viewer (e.g. Adobe Reader): Software for displaying the generated handwritten-style character data.

[1141] System Overview

[1142] The system consists of the following main components:

[1143] 1. Handwriting data collection and learning (server)

[1144] A scanner captures data that a user has previously handwritten and stores it on the device. The device then sends the handwritten data to a server, which analyzes and learns from it. A generative AI model analyzes and learns features such as character shape, pen pressure, and curve angle. This learning data serves as the basis for generating user-style character shapes for new data.

[1145] 2. Character selection by eye gaze input (user, device)

[1146] The user uses an eye-gaze input device to select characters on the screen. The device analyzes the eye-gaze input data and sends the selected character string (e.g., "Hello") to the server.

[1147] 3. Generating handwritten-style characters (server)

[1148] Based on the character data received by the server, the generative AI model generates handwritten-style characters. The characteristics of handwritten data learned in the past are reflected. The server adds fluctuations to the pen pressure and curve angle to prevent the character shapes from becoming unique. The generated handwritten-style character data is returned to the device in bitmap or vector format.

[1149] 4. Display and save handwritten characters (device, user)

[1150] The terminal receives the handwritten-style character data and displays it on the screen. The user can check the displayed handwritten characters and save or print them as needed.

[1151] Specific examples

[1152] Example 1: Writing a birthday message

[1153] 1. Learning handwritten data (server)

[1154] The system scans a handwritten message written by the user in the past, such as "Happy Birthday," and sends it from the device to the server.

[1155] The server analyzes this handwritten data, and the generative AI model learns features such as character shape and writing pressure.

[1156] 2. Character selection (user, terminal)

[1157] The user uses gaze input to select the string they want to enter: "Happy Birthday."

[1158] The device sends the results of the gaze input to the server.

[1159] 3. Generating handwritten-style characters (server)

[1160] The server converts the string "Happy Birthday" selected through eye gaze input into handwritten text, adjusting the pen pressure and curve as needed based on past learning data.

[1161] The generated handwritten character data is sent to the terminal.

[1162] 4. Display and storage (device, user)

[1163] The terminal displays the handwritten-style characters on the display, and if the user is satisfied after checking them, they can save or print them.

[1164] The finalized message can be sent as a letter or an electronic message.

[1165] In this way, the system of the present invention allows a user to easily generate handwritten-style characters using an eye-gaze input device, and create messages that retain the warmth and individuality of handwriting.

[1166] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1167] Step 1: Collecting and training handwriting data

[1168] Input: A scanned image file of data previously handwritten by a user (e.g., "Happy Birthday").

[1169] Operation:

[1170] 1. The user uses a scanner to capture handwritten data as a digital image on the device.

[1171] 2. The device sends the image file to the server. Specifically, the image file is uploaded to the server using the HTTP protocol.

[1172] Output: Handwritten data stored on the server.

[1173] Specific behavior:

[1174] The server stores the received image file in a database.

[1175] The server's generative AI model analyzes this data and learns characteristics such as character shape, pen pressure, and curve angle. This learning data is then used to generate new data.

[1176] Step 2: Select text with gaze input

[1177] Input: Gaze data captured from the user's eye tracking device.

[1178] Operation:

[1179] 1. The user uses an eye-gaze input device (e.g., Tobii Eye Tracker) to select text on the screen.

[1180] 2. The eye-gaze input device tracks the user's gaze and detects character selection.

[1181] 3. The device analyzes the gaze data and identifies the selected string (e.g., "Hello").

[1182] 4. The device sends the selected string to the server using the HTTP protocol.

[1183] Output: The string data received by the server.

[1184] Specific behavior:

[1185] The device uses a Python script to analyze the gaze data and extract the selected string.

[1186] The terminal transmits the extracted character string data to the server as an HTTP request.

[1187] Step 3: Generate handwritten characters

[1188] Input: Character data received by the server (e.g. "Hello").

[1189] Operation:

[1190] 1. The server uses a generative AI model to generate handwritten-style characters based on the character data received.

[1191] 2. The server uses the learned data to convert the characters into handwritten style, adding fluctuations to the pen pressure and curve angle.

[1192] 3. Convert the generated handwritten character data into bitmap or vector format.

[1193] 4. The server sends the generated handwritten-style character data to the device using the HTTP protocol.

[1194] Output: Generated handwritten character data.

[1195] Specific behavior:

[1196] The server runs a generative AI model (e.g., Python TensorFlow) and generates handwritten-style characters based on the input character data.

[1197] The server converts the generated handwritten-style character data into bitmap or vector format and sends it to the terminal as an HTTP response.

[1198] Step 4: Display and save handwritten text

[1199] Input: Handwritten-style character data received by the device.

[1200] Operation:

[1201] 1. The device displays the handwritten character data received from the server. Specifically, an image viewer (e.g., Adobe Reader) is used.

[1202] 2. The user checks the displayed handwritten characters and, if satisfied, saves or prints them.

[1203] 3. If you choose to save, the device will save the data to local storage or a cloud service. If you choose to print, the device will print it out.

[1204] Output: Saved handwriting data or printed handwriting.

[1205] Specific behavior:

[1206] The terminal displays the received handwritten-style character data using an image viewer.

[1207] The user clicks the save button to save the data or the print button to print it out on a printer.

[1208] Through the specific actions performed at each step, users can generate handwritten-style characters using only their gaze, easily creating messages that retain the warmth and individuality of handwriting.

[1209] (Application example 1)

[1210] 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."

[1211] In systems that generate handwritten-style characters using eye gaze input, users can easily create characters that have the warmth and individuality of handwriting. However, the lack of a function to apply this handwritten-style character to video or image content makes it difficult for users to easily create practical content. Therefore, there is a need not only for generating handwritten-style characters using eye gaze input, but also for an efficient means to apply them to video and images.

[1212] 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.

[1213] In this invention, the server includes means for collecting past handwritten data and analyzing it to learn the characteristics of handwritten characters, means for receiving character data selected by a user using an eye-gaze input device, means for converting the received character data into a handwritten-style character based on the learned characteristics of the handwritten characters, means for outputting the converted handwritten character data, and means for applying handwritten-style characters to image or video content based on the eye-gaze data collected using the eye-gaze input device. This allows a user to easily apply handwritten-style characters created using eye-gaze input to video or image content, thereby enabling the creation of original, unique content.

[1214] "Past handwritten data" refers to character information previously handwritten by a user, and is data that the learning model uses to analyze handwriting characteristics.

[1215] An "eye gaze input device" is a device that detects the movement of a user's eye gaze and uses that information as input data.

[1216] "Handwritten character features" refer to the individual attributes of handwritten characters, such as character shape, pen pressure, and curve angle, and by analyzing and learning these, it becomes possible to generate handwritten-style characters.

[1217] "Received character data" refers to character information selected by the user that is acquired from the eye-gaze input device, and handwritten-style characters are generated based on this data.

[1218] "Converting to handwritten style" refers to converting received character data into a character format that has the warmth and individuality unique to handwriting, based on past handwritten data.

[1219] "Handwritten-style character data" refers to character information that has been converted to look like handwritten text by a generative AI model that reflects the characteristics of past handwritten data.

[1220] "Gaze data" refers to the movement and position information of a user's gaze collected by an eye-gaze input device, and is used for character selection, etc.

[1221] "Image or video content" refers to media content in the form of still images or videos, to which handwritten-style characters can be added.

[1222] The present invention is a system that uses eye gaze input to generate handwritten-style characters and apply these characters to image or video content. Each component of the present invention and its operation are described in detail below.

[1223] System configuration

[1224] The system consists of the following main components:

[1225] 1. Handwriting data collection and learning

[1226] 2. Character selection using eye gaze input

[1227] 3. Generating handwritten-style characters

[1228] 4. Applying handwritten text to content

[1229] 5. Output and Saving

[1230] Handwriting data collection and learning

[1231] Users scan or digitally import previously written handwriting data into their device. The device then sends this handwriting data to a cloud server. The server stores the handwriting data in a database, and the generative AI model analyzes and learns features such as character shape, pen pressure, and curve angle. This learning data serves as a reference for generating user-style character shapes for new gaze input data.

[1232] Selecting characters using eye gaze input

[1233] The user selects characters on the screen by guiding their gaze using an eye-gaze input device (e.g., a smartphone camera). The device analyzes the eye-gaze input and transmits the selected character data to a cloud server.

[1234] Generating handwritten characters

[1235] The server uses a generative AI model to generate handwritten-style characters based on the received character data. The server incorporates the characteristics of previous handwritten data, adding fluctuations to the pen pressure and curve angle. The generated handwritten-style character data is then sent back to the device in bitmap or vector format.

[1236] Applying handwritten text to content

[1237] The device applies the received handwritten-style character data to the image or video content specified by the user, thereby generating content containing a unique handwritten-style message.

[1238] Output and save

[1239] The generated content is displayed on the device's display, and the user can check the displayed content and save or share it if satisfied.

[1240] Hardware and software used

[1241] Camera: Uses your phone's camera to detect your gaze.

[1242] Cloud server: A server that learns handwritten data and processes the generation of handwritten-style characters, and also includes a generative AI model.

[1243] Generative AI model: A model that generates handwritten-style characters using deep learning frameworks such as TensorFlow.

[1244] Examples of concrete examples and prompts

[1245] Examples:

[1246] The user uses an eye-gaze input device to select the word "Thank you." The selected text is sent to a cloud server, where a generative AI model converts it into handwritten-style text based on past handwriting data. This handwritten-style text is then applied to the video message the user is creating, and the "Thank you" part is displayed in handwritten style.

[1247] Example prompt sentence:

[1248] "Using the gaze-selected string 'Thank you', generate handwritten-style characters based on past handwriting data."

[1249] This invention allows users to easily generate handwritten-style characters using an eye-gaze input device and apply them to content, making it possible to create attractive digital content while preserving the warmth and individuality of handwriting.

[1250] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1251] Step 1:

[1252] The user scans or digitally imports previously handwritten data into the device. The device then sends the data to a cloud server, where it stores the handwritten data in a database. The generative AI model analyzes and learns from the character's shape, pen pressure, curve angle, and other characteristics.

[1253] Input: Handwritten data

[1254] Data processing: Analysis of character shape, pen pressure, and curve angle

[1255] Output: Trained model

[1256] Step 2:

[1257] The user selects characters on the screen by using the eye-gaze input device. The terminal acquires the user's eye-gaze data through the eye-gaze input device and transmits the selected character data to the cloud server.

[1258] Input: Gaze data, selected character data

[1259] Data processing: Analysis of gaze data, generation of text data

[1260] Output: Received character data

[1261] Step 3:

[1262] The server uses a generative AI model based on the received character data to generate handwritten-style characters. It adds variations to the character shape, pen pressure, and curve angle to reflect the characteristics of previous handwritten data. The generated handwritten-style character data is then sent back to the device in bitmap or vector format.

[1263] Input: Received character data, trained model

[1264] Data processing: Generating handwritten characters based on character shape, pressure, and curve angle

[1265] Output: Handwritten character data

[1266] Step 4:

[1267] The device applies the received handwritten-style character data to the image or video content specified by the user, drawing the handwritten-style characters on the content and adjusting the layout as needed.

[1268] Input: Handwritten text, image or video content

[1269] Data processing: Drawing handwritten characters, adjusting layout

[1270] Output: Content with handwritten text applied

[1271] Step 5:

[1272] The device displays the generated content on the display for the user to review, and if the user is satisfied, they can save the generated content and share it as needed.

[1273] Input: Content with handwritten text applied

[1274] Data processing: Displaying content, user confirmation

[1275] Output: Saved content, shared content

[1276] 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.

[1277] The present invention combines an emotion engine with a system that allows users who have difficulty writing by hand to use eye gaze input to generate handwritten-style characters and express the warmth and individuality of handwriting. The system of the present invention analyzes past handwritten data to learn the characteristics of handwritten characters, and makes it possible to convert character data acquired by eye gaze input into handwritten-style characters based on the emotion engine.

[1278] System Overview

[1279] The system consists of the following main components:

[1280] 1. Handwritten data analysis and learning (server)

[1281] 2. Character selection by eye gaze input (user, device)

[1282] 3. Emotion recognition using emotion engine (server)

[1283] 4. Generating handwritten-style characters (server)

[1284] 5. Display and save handwritten style characters (device, user)

[1285] Program processing

[1286] Learning Phase

[1287] Handwriting data collection and learning (server)

[1288] Data that a user has previously handwritten is scanned or digitally imported into the terminal.

[1289] The device sends handwritten data to a server, which stores it in a database and a generative AI model analyzes and learns features such as character shape, pen pressure, and curve angle.

[1290] This training data serves as a reference for generating user-style character shapes for new input data.

[1291] Gaze input phase

[1292] Character selection (user, terminal)

[1293] A user uses an eye-gaze input device to select characters on the screen by gazing at them.

[1294] The device analyzes the gaze input data and sends the selected string (e.g., "Hello") to the server.

[1295] Emotion Recognition Phase

[1296] Emotion Recognition (Server)

[1297] The emotion engine installed on the server analyzes the user's gaze, facial expressions, voice input, etc. to recognize their current emotional state.

[1298] The recognized emotion data is reflected in the handwritten character generation process.

[1299] Handwritten character generation phase

[1300] Handwritten character generation (server)

[1301] Based on the character data received by the server, the generative AI model generates handwritten-style characters, incorporating the characteristics of handwritten data learned in the past and emotional information from the emotion engine.

[1302] Fluctuations in pen pressure, curve angle, and even the color and style of the text are added based on emotion.

[1303] The generated handwritten character data is sent back to the terminal in bitmap or vector format.

[1304] Result output phase

[1305] Display and save handwritten style characters (device, user)

[1306] The terminal receives the handwritten character data and displays it on the screen.

[1307] The user can review the displayed handwriting and save or print it if desired.

[1308] Specific examples

[1309] Example 1: Writing a birthday message

[1310] 1. Learning handwritten data (server)

[1311] The system scans a handwritten message written by the user in the past, such as "Happy Birthday," and sends it from the device to the server.

[1312] The server analyzes this handwritten data, and the generative AI model learns features such as character shape and writing pressure.

[1313] 2. Character selection (user, terminal)

[1314] The user uses gaze input to select the string they want to enter: "Happy Birthday."

[1315] The device sends the results of the gaze input to the server.

[1316] 3. Emotion Recognition (Server)

[1317] The server analyzes the user's gaze and facial expressions to recognize their current emotional state (e.g., joy).

[1318] The recognized emotion data is reflected in the handwritten character generation process.

[1319] 4. Generating handwritten-style characters (server)

[1320] The server converts the string "Happy Birthday" selected through eye gaze input into handwritten text. Past learning data and recognized emotional information (joy) are reflected in the conversion, adding fluctuations to the pressure and curve of the characters, as well as brightening the colors and style.

[1321] The generated handwritten character data is sent to the terminal.

[1322] 5. Display and storage (device, user)

[1323] The terminal displays the handwritten-style characters on the display, and if the user is satisfied after checking them, they can save or print them.

[1324] The finalized message can be sent as a letter or an electronic message.

[1325] In this way, the system of the present invention allows users to use eye gaze input to generate handwritten-style characters that reflect their own emotions, allowing them to create messages that incorporate the warmth and individuality of handwriting, as well as emotional expression.

[1326] The processing flow will be explained below.

[1327] Step 1: Collect handwritten data (user)

[1328] Scan or photograph letters or messages that the user has previously written.

[1329] The user imports scanned data or photos into the device.

[1330] Step 2: Send handwritten data (device)

[1331] The handwritten data captured by the terminal is sent to the server.

[1332] When sending, the data is sent with the user ID and timestamp attached.

[1333] Step 3: Storing and analyzing handwritten data (server)

[1334] The server stores the received handwritten data in a database.

[1335] The server analyzes the handwritten data and extracts features such as character shape, pen pressure, and curve angle.

[1336] Step 4: Feature Learning (Server)

[1337] The server uses a generative AI model to learn the characteristics of the extracted handwritten data.

[1338] The learned features are stored in a database and used as a basis for generating handwritten-style characters for new input data.

[1339] Step 5: Acquiring Gaze Input Data (User)

[1340] A user uses an eye-gaze input device to select characters on the screen by gazing at them.

[1341] The user selects a string of characters (for example, "hello") by eye gaze input.

[1342] Step 6: Sending gaze input data (device)

[1343] The device analyzes the gaze input data and sends the selected string "Hello" to the server.

[1344] Step 7: Emotion Recognition (Server)

[1345] The emotion engine installed on the server analyzes the user's gaze, facial expressions, voice input, etc. to recognize their current emotional state.

[1346] The recognized emotion data is reflected in the handwritten character generation process.

[1347] Step 8: Receiving a request to generate handwritten characters (server)

[1348] The server receives the text data "Hello" and emotion data from the terminal.

[1349] The server starts processing as a request to generate handwritten-style characters based on this character data and emotion data.

[1350] Step 9: Generate handwritten characters (server)

[1351] The server uses the generative AI model to convert the received text data "Hello" into handwritten text, incorporating the characteristics of handwritten data learned in the past and emotional information from the emotion engine.

[1352] Fluctuations in pen pressure, curve angle, and even the color and style of the text are added based on emotion.

[1353] Step 10: Determine the output format of the generated data (server)

[1354] The server converts the generated handwritten characters into output data in bitmap or vector format.

[1355] Define the output format and create the data to send back to the terminal.

[1356] Step 11: Returning handwritten character data (server)

[1357] The server returns the generated handwritten character data to the terminal.

[1358] Step 12: Displaying Handwritten Characters (Device)

[1359] The terminal receives the returned handwritten character data and displays it on the screen.

[1360] The user checks the displayed handwriting.

[1361] Step 13: Saving and using handwritten characters (user)

[1362] The user reviews the displayed handwriting and, if satisfied, saves or prints it.

[1363] Send it as a letter or message if necessary.

[1364] Example 2

[1365] 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."

[1366] With current text input systems, many users find it difficult to capture the warmth, individuality, and emotion of handwriting. Furthermore, for users of eye-gaze input devices, there are limited ways to easily create handwritten-style text. This makes it difficult to create emotionally charged messages using eye-gaze input.

[1367] 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.

[1368] In this invention, the server includes means for collecting past handwritten data and analyzing it to learn the characteristics of handwritten characters, means for receiving character data selected by a user using an eye-gaze input device, means for analyzing the user's eye movements and facial expressions to recognize an emotional state, means for converting the received character data into handwritten-style characters based on the learned handwritten character characteristics and the recognized emotional state, and means for outputting the converted handwritten character data, thereby enabling a user to generate handwritten-style characters through eye-gaze input and create an emotional message.

[1369] "Handwritten data" refers to digital data of characters and figures handwritten by a user.

[1370] An "eye gaze input device" is a device for tracking the movement of a user's eye gaze and acquiring that data.

[1371] "Character data" refers to digital information of a character string input by a user, acquired through an eye-gaze input device or other input means.

[1372] An "emotion engine" is software or hardware that has the function of recognizing a user's emotional state by analyzing the user's gaze movements, facial expressions, voice input, etc.

[1373] The "means for converting to a handwritten-like form" is a system or program for converting digital character data to a handwritten-like form based on the characteristics of the handwritten data.

[1374] "Pen pressure" refers to the strength of pressure that the pen applies to the paper when writing by hand, and is one of the characteristics of handwriting.

[1375] The "angle of the curve" refers to the angle of the curved part of handwritten characters, and is a feature that expresses the individuality of handwriting.

[1376] "Fluctuation" refers to the randomness in the pressure and angle of the curve of handwritten characters, and is a variable element used to express the naturalness and individuality of handwriting.

[1377] A "generative AI model" is an artificial intelligence model trained to analyze the characteristics of handwritten data and generate new handwritten-style characters based on that data.

[1378] A "bitmap" is a digital image file format that makes up an image on a pixel-by-pixel basis.

[1379] A "vector format" is a digital image file format that represents an image using mathematical formulas such as points, lines, and curves.

[1380] This invention combines an emotion engine with a system that allows users who have difficulty writing by hand to generate handwritten-style characters using an eye-gaze input device, thereby expressing the warmth and individuality of handwriting. This system consists of the following main components:

[1381] Handwriting data collection and learning

[1382] 1. Handwritten data collection

[1383] Users can scan previously handwritten data or write it on a digital device such as a tablet, for example, by scanning special messages or everyday notes and converting them into digital form.

[1384] The device stores this digital handwriting data and sends it to a server, where it uploads the data using its internet connection.

[1385] 2. Analysis and learning of handwritten data

[1386] The server stores the received handwritten data in a database. The handwritten feature information includes the character shape, writing pressure, and curve angle.

[1387] A generative AI model analyzes this data and learns the unique characteristics of the user's handwriting.

[1388] Selecting characters using eye gaze input

[1389] 3. Selecting the characters

[1390] The user wears an eye-gaze input device and the movement of the user's eyes is tracked. For example, an eye tracker is a commonly used eye-gaze input device.

[1391] The device analyzes the data from the eye-gaze input device and recognizes the characters the user is looking at. The user selects a character string, such as "hello," from a list of characters displayed on the screen.

[1392] Emotion recognition

[1393] 4. Emotional Recognition

[1394] The emotion engine installed on the server analyzes the user's gaze, facial expression, and, if necessary, voice input to recognize their current emotional state, which can include, for example, "happiness" or "sadness."

[1395] The recognized emotion data is reflected in the handwritten character generation process.

[1396] Generating handwritten characters

[1397] 5. Generating handwritten-style characters

[1398] Based on the character data and emotion data received by the server, the generative AI model generates handwritten-style characters. The generative AI model applies the characteristics of handwritten data it has learned in the past, and adjusts the pen pressure, curve angle, and even color to reflect the emotion information.

[1399] For example, when you type "hello," the pen pressure will fluctuate based on the emotional information, and the emotion will also be reflected in the color and style.

[1400] Display and save handwritten characters

[1401] 6. Display and save handwritten characters

[1402] The terminal receives the generated handwritten-style character data and displays it on the display.

[1403] The user checks the displayed handwritten-style characters and, if satisfied, saves or prints them. The device uses the save function to save them as a digital file or connects to a printer to print them.

[1404] Specific examples

[1405] Example: How to write a birthday message

[1406] 1. Learning from handwritten data

[1407] The system scans past messages written by users saying "Happy Birthday" and stores them on the device, which then sends this data to a server.

[1408] The server analyzes this data, and the generative AI model learns the character shape, pressure, and curve angle.

[1409] 2. Selecting the characters

[1410] The user uses the eye-gaze input device to select the character string "Happy Birthday" with their eyes.

[1411] The device analyzes the gaze data and sends the selected character string to the server.

[1412] 3. Emotional Recognition

[1413] The server analyzes gaze movements and facial expressions to recognize the current emotional state (e.g., joy).

[1414] 4. Generating handwritten-style characters

[1415] The server converts the text "Happy Birthday" into handwritten text, adjusting the pen pressure, curve, color, etc. to reflect the emotional information (joy).

[1416] The server transmits the generated handwritten-style character data to the terminal.

[1417] 5. Display and save

[1418] The device displays handwritten-style characters on the screen for the user to confirm.

[1419] The user selects save or print and the terminal performs the action.

[1420] This system allows users to easily create unique, handwritten-style characters by using eye-gaze input, and to create emotive messages.

[1421] Prompt Sentence Examples

[1422] "I want to scan past handwritten messages and have the server learn from them."

[1423] "I want to use my eye-gaze device to select text on the screen."

[1424] "I want to analyze the current emotion and reflect it in handwritten-style characters."

[1425] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1426] Step 1: Collecting handwritten data

[1427] Input: A handwritten message written by the user in the past

[1428] What it does: Scans data that a user has previously handwritten or writes on a digital device such as a tablet. For example, scans a birthday card or letter and converts it into a digital format.

[1429] Output: Handwritten data in digital format

[1430] Step 2: Send handwritten data

[1431] Input: Handwritten data in digital format

[1432] Specific operation: The handwritten data captured by the device in a scanned or digital format is sent to a server via the Internet, and the handwritten data is uploaded to cloud storage by calling an API.

[1433] Output: Handwritten data stored on the server

[1434] Step 3: Analyzing and learning handwritten data

[1435] Input: Handwritten data stored on the server

[1436] Specific operation: The server inputs the handwritten data it receives into the generative AI model, which analyzes features such as character shape, pen pressure, and curve angle. The generative AI model learns from this data, extracts and saves the user's unique handwritten character characteristics.

[1437] Output: Trained handwritten character feature data

[1438] Step 4: Selecting the text

[1439] Input: User's gaze input data

[1440] Specific operation: The user wears an eye-gaze input device and selects characters displayed on the screen with their eyes. The device analyzes the data from the eye-gaze input device in real time and identifies the character the user is looking at. For example, the character string "Hello" is selected.

[1441] Output: String selected by gaze

[1442] Step 5: Sending characters

[1443] Input: Gaze-selected string

[1444] Specific operation: The device sends the selected string to the server, and the character data is uploaded to the server via the Internet.

[1445] Output: Character data received by the server

[1446] Step 6: Recognize emotions

[1447] Input: User's gaze or facial expression data

[1448] Specific behavior: The server's emotion engine analyzes eye movements and facial expressions, and optionally voice input data, to identify the current emotional state (e.g., joy or sadness).

[1449] Output: Recognized emotional state data

[1450] Step 7: Generate handwritten characters

[1451] Input: Character data received by the server, trained handwriting feature data, recognized emotional state data

[1452] How it works: Based on the character data and emotion data received by the server, the generative AI model generates handwritten-style characters. It applies the characteristics of handwritten data it has learned in the past and adjusts the pen pressure, curve angle, color, etc. to reflect the recognized emotion information. For example, it converts the string "hello" into a handwritten-style character and adds bright colors and styles based on the emotion of joy.

[1453] Output: Generated handwritten character data

[1454] Step 8: Send handwritten text

[1455] Input: Generated handwritten-style character data

[1456] Specific operation: The server generates handwritten character data and sends it to the device in bitmap or vector format. The data is then returned via the Internet.

[1457] Output: Handwritten character data received by the device

[1458] Step 9: Displaying handwritten characters

[1459] Input: Handwritten-style character data received by the terminal

[1460] Specific operation: The handwritten character data received by the device is displayed on the display, and a graphic representation is provided so that the user can check it in real time.

[1461] Output: Handwritten characters displayed on the screen

[1462] Step 10: Save and print your handwritten text

[1463] Input: Handwritten characters displayed on the screen

[1464] Specific operation: The user checks the handwritten-style characters and selects whether to save or print them. The device uses the save function to save them as a digital file or connects to a printer to print them.

[1465] Output: Saved handwriting file or printed handwriting

[1466] (Application example 2)

[1467] 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."

[1468] When users who have difficulty writing by hand use eye-gaze input to generate handwritten-like characters, it is difficult to express warmth and individuality in the characters. Creating messages that reflect the user's emotions is also a challenge. In food delivery applications, there is a need for technology that can generate handwritten-like messages using eye-gaze input devices and convey emotions such as gratitude and encouragement.

[1469] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting past handwritten data and analyzing it to learn the characteristics of handwritten characters, means for receiving character data selected by a user using an eye-gaze input device, means for converting the received character data into handwritten-style characters based on the learned characteristics of handwritten characters, means for outputting the converted handwritten character data, and means for recognizing the emotional state of the user and adjusting the characteristics of handwritten-style characters based on the emotion. This enables a user to create a handwritten-style message that reflects their personality and emotion through the eye-gaze input device.

[1470] "Past handwritten data" refers to character information that a user has handwritten in the past, and serves as basic data for analyzing and learning the characteristics of handwritten characters.

[1471] "Handwritten character features" refer to individual attributes of handwritten characters, such as the shape, pressure, and angle of the curve, and are used as reference when generating handwritten-style characters.

[1472] An "eye gaze input device" is a device that includes hardware and software for tracking the movement of a user's eye gaze.

[1473] "Character data" refers to character information entered or selected by the user, and is the original data that is converted into handwritten-style characters.

[1474] The "means for converting to look like handwriting" refers to the process of processing character data entered by eye gaze input to look like handwriting based on the collected and learned characteristics of the handwriting data.

[1475] "Converted handwritten character data" refers to character information that has been processed to look like handwriting, and is output in bitmap or vector format.

[1476] An "emotional state" refers to the emotion a user is feeling at a particular moment, and may include joy, sadness, anger, etc.

[1477] "Means for adjusting the characteristics of handwritten-style characters based on emotions" refers to a process of analyzing a user's emotional state and adjusting the pressure, curve angle, color, style, etc. of the handwritten characters based on that information.

[1478] The present invention is a system that uses eye gaze input to generate handwritten-style characters, particularly in food delivery applications, allowing users to send thank you or other emotional messages to delivery staff or stores.

[1479] System Overview

[1480] The system consists of the following main components:

[1481] 1. Handwritten data analysis and learning (server)

[1482] 2. Character selection by eye gaze input (user, device)

[1483] 3. Emotion recognition using emotion engine (server)

[1484] 4. Generating handwritten-style characters (server)

[1485] 5. Display and save handwritten style characters (device, user)

[1486] Program Description

[1487] Hardware and Software

[1488] Hardware: Eye tracking device, camera (for emotion recognition)

[1489] Software: OpenCV (image processing), EmotionRecognizer (emotion recognition model), GazeTracking (eye tracking library), HandwritingModel (handwriting generation model)

[1490] Analysis and learning of handwritten data

[1491] The server collects, analyzes, and learns from handwritten data that the user has written. Specifically, the generative AI model learns from the collected handwritten data and analyzes the characteristics of handwritten characters (character shape, writing pressure, angle of curve, etc.).

[1492] Selecting characters using eye gaze input

[1493] The user selects characters on the screen using the eye-gaze input device. The device analyzes the eye-gaze input and sends the selected character data to the server. For example, if the user selects the character string "Thank you," this data is sent.

[1494] emotion recognition

[1495] The emotion engine installed on the server analyzes the user's facial expressions and gaze data acquired from the camera to recognize their current emotional state, which can include joy, sadness, surprise, etc.

[1496] Generating handwritten characters

[1497] The server uses the received character data and the emotion information obtained from the emotion engine to generate handwritten-style characters. In this generation process, the pen pressure, curve angle, color, etc. are adjusted based on the characteristics of the handwritten data and the emotion information.

[1498] Display and save

[1499] The device receives handwritten-style character data sent from the server and displays it on the screen. The user can check it and save or send it if they are satisfied. This function allows users to input and send a handwritten-style message of thanks when ordering food delivery.

[1500] Specific examples

[1501] For example, consider a scenario where a user places an order using a food delivery application and sends a thank you message to the delivery person. When the user inputs "Thank you" using the eye-gaze input device, the system recognizes the user's emotion and generates a handwritten-style message that reflects that emotion.

[1502] An example of a prompt to be input to the generative AI model is as follows:

[1503] "Generate handwritten-style characters based on the string selected using gaze input and the recognized emotion information."

[1504] for example,

[1505] "selected_text = 'Thank you'

[1506] recognized_emotion = 'joy' The user's emotional state recognized by the AI

[1507] handwritten_text = generate_handwritten_text(selected_text, recognized_emotion)

[1508] print_handwritten_text(handwritten_text)”

[1509] In this way, a handwritten-style message that visually reflects the user's emotions can be generated and used when ordering food delivery.

[1510] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1511] Step 1:

[1512] Collects and learns handwritten data. The server collects handwritten data from users and stores it in a database. The generative AI model then analyzes this data and learns the characteristics of handwritten characters (such as character shape, pressure, and curve angle). The input is handwritten data, and the output is the learned handwritten character characteristics. Specific operations include scanning handwritten data, uploading the digital data, saving it to a database, and training the AI ​​model.

[1513] Step 2:

[1514] Character selection is performed using gaze input. The user uses an eye-gaze input device to select characters on the screen with their gaze. The device analyzes the gaze input data and sends the selected character data to the server. The input is gaze data, and the output is the selected character data. Specific operations include calibrating the eye-tracking device, collecting and analyzing gaze data, and sending the character selection results to the server.

[1515] Step 3:

[1516] Recognizes emotions. The server analyzes the user's facial expression and gaze data acquired from the camera and uses an emotion engine to recognize the user's current emotional state. The input is facial images and gaze data, and the output is recognized emotion information. Specific operations include capturing facial images with the camera, and image analysis and emotion classification with the emotion engine.

[1517] Step 4:

[1518] Generates handwritten-style characters. The server generates handwritten-style characters using a generative AI model based on the received character data and emotion information obtained from the emotion engine. The input is character data and emotion information, and the output is handwritten-style character data. Specific operations include generating handwritten-style characters using an AI model, adjusting pen pressure and curve angle, and changing colors.

[1519] Step 5:

[1520] This displays and saves handwritten-style characters. The terminal receives handwritten-style character data sent from the server and displays it on the screen. The user can check it and save or send it if they are satisfied. The input is handwritten-style character data, and the output is the displayed handwritten-style characters. Specific operations include converting the character data to bitmap or vector format, displaying it on the screen, and saving it to a file.

[1521] 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.

[1522] 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.

[1523] 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.

[1524] [Fourth embodiment]

[1525] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1526] 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.

[1527] 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).

[1528] 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.

[1529] 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.

[1530] 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).

[1531] 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.

[1532] 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.

[1533] 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.

[1534] 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.

[1535] 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.

[1536] 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.

[1537] 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."

[1538] This invention is a system that enables users who have difficulty writing by hand, such as those with muscular dystrophy, to generate handwritten-style characters using eye gaze input, thereby expressing the warmth and individuality of handwriting without compromising the convenience of eye gaze input. The system of this invention analyzes and learns from past handwritten data, and makes it possible to convert character data acquired through eye gaze input into handwritten-style characters.

[1539] System Overview

[1540] The system consists of the following main components:

[1541] 1. Handwritten data analysis and learning (server)

[1542] 2. Character selection by eye gaze input (user, device)

[1543] 3. Generating handwritten-style characters (server)

[1544] 4. Display and save handwritten characters (device, user)

[1545] Program processing

[1546] Learning Phase

[1547] Handwriting data collection and learning (server)

[1548] Data that a user has previously handwritten is scanned or digitally imported into the terminal.

[1549] The device sends handwritten data to a server, which stores it in a database and a generative AI model analyzes and learns features such as character shape, pressure, and curve angle.

[1550] This training data serves as a reference for generating user-style character shapes for new input data.

[1551] Gaze input phase

[1552] Character selection (user, terminal)

[1553] A user uses an eye-gaze input device to select characters on the screen by gazing at them.

[1554] The device analyzes the gaze input data and sends the selected string (e.g., "Hello") to the server.

[1555] Handwritten character generation phase

[1556] Handwritten character generation (server)

[1557] Based on the character data received by the server, the generative AI model generates handwritten-style characters, incorporating the characteristics of handwritten data it has learned from the past.

[1558] The server adds fluctuations to the pen pressure and curve angle to prevent the character shape from being unique.

[1559] The generated handwritten character data is sent back to the terminal in bitmap or vector format.

[1560] Result output phase

[1561] Display and save handwritten style characters (device, user)

[1562] The terminal receives the handwritten character data and displays it on the screen.

[1563] The user can review the displayed handwriting and save or print it if desired.

[1564] Specific examples

[1565] Example 1: Writing a birthday message

[1566] 1. Learning handwritten data (server)

[1567] The system scans a handwritten message written by the user in the past, such as "Happy Birthday," and sends it from the device to the server.

[1568] The server analyzes this handwritten data, and the generative AI model learns features such as character shape and writing pressure.

[1569] 2. Character selection (user, terminal)

[1570] The user uses gaze input to select the string they want to enter: "Happy Birthday."

[1571] The device sends the results of the gaze input to the server.

[1572] 3. Generating handwritten-style characters (server)

[1573] The server converts the string "Happy Birthday" selected through eye gaze input into handwritten text, adjusting the pen pressure and curve as needed based on past learning data.

[1574] The generated handwritten character data is sent to the terminal.

[1575] 4. Display and storage (device, user)

[1576] The terminal displays the handwritten-style characters on the display, and if the user is satisfied after checking them, they can save or print them.

[1577] The finalized message can be sent as a letter or an electronic message.

[1578] In this way, the system of the present invention allows users to easily generate handwritten-style characters using an eye-gaze input device, allowing them to create messages that retain the warmth and individuality of handwriting.

[1579] The processing flow will be explained below.

[1580] Step 1: Collect handwritten data (user)

[1581] Scan or photograph letters or messages that the user has previously written.

[1582] The user imports scanned data or photos into the device.

[1583] Step 2: Send handwritten data (device)

[1584] The handwritten data captured by the terminal is sent to the server.

[1585] When sending, the data is sent with the user ID and timestamp attached.

[1586] Step 3: Storing and analyzing handwritten data (server)

[1587] The server stores the received handwritten data in a database.

[1588] The server analyzes the handwritten data and extracts features such as character shape, pen pressure, and curve angle.

[1589] Step 4: Feature Learning (Server)

[1590] The server uses a generative AI model to learn the characteristics of the extracted handwritten data.

[1591] The learned features are stored in a database and used as a basis for generating handwritten-style characters for new input data.

[1592] Step 5: Acquiring Gaze Input Data (User)

[1593] A user uses an eye-gaze input device to select characters on the screen by gazing at them.

[1594] The user selects a string of characters (for example, "hello") by eye gaze input.

[1595] Step 6: Sending gaze input data (device)

[1596] The device analyzes the gaze input data and sends the selected string "Hello" to the server.

[1597] Step 7: Receiving a request to generate handwritten characters (server)

[1598] The server receives the text data "Hello" from the terminal.

[1599] The server starts processing as a request to generate handwritten-style characters based on this character data.

[1600] Step 8: Generate handwritten characters (server)

[1601] The server uses the generated AI model to convert the received text data "hello" into a handwritten version.

[1602] The server applies fluctuations to the pen pressure and curve angle to ensure that the generated handwritten characters are not unique.

[1603] Step 9: Determine the output format of the generated data (server)

[1604] The server converts the generated handwritten characters into output data in bitmap or vector format.

[1605] Define the output format and create the data to send back to the terminal.

[1606] Step 10: Returning handwritten character data (server)

[1607] The server returns the generated handwritten character data to the terminal.

[1608] Step 11: Displaying Handwritten Characters (Device)

[1609] The terminal receives the returned handwritten character data and displays it on the screen.

[1610] The user checks the displayed handwriting.

[1611] Step 12: Saving and using handwritten characters (user)

[1612] The user reviews the displayed handwriting and, if satisfied, saves or prints it.

[1613] Send it as a letter or message if necessary.

[1614] Example 1

[1615] 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."

[1616] For people with muscular dystrophy or those who have difficulty writing by hand, eye gaze input alone can be used to select and input characters, but it can lose the warmth and individuality of handwriting. Furthermore, existing eye gaze input systems do not provide an appropriate means for generating handwritten-style characters, making it difficult to reproduce the feel of handwriting.

[1617] 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.

[1618] In this invention, the server includes means for collecting past handwritten data and analyzing it to learn the characteristics of handwritten characters, means for receiving character data selected by a user using an eye-gaze input device, means for converting the received character data into a handwritten-style character based on the learned characteristics of handwritten characters, and means for displaying and saving the converted handwritten character data. This makes it possible to generate characters that have the warmth and individuality of handwriting without losing the convenience of eye-gaze input.

[1619] "Previous handwritten data" refers to character information previously handwritten by a user, and is data captured in a scanned or digital format.

[1620] "Analysis" refers to the process of extracting features such as character shape, pen pressure, and curve angle from handwritten data, allowing the generative AI model to learn.

[1621] "Characteristics of handwritten characters" refers to the unique expressive elements of handwriting, such as the character shape, writing pressure, and angle of curve contained in the handwritten data.

[1622] An "eye gaze input device" is a device that tracks the user's gaze and can use the gaze movement as data.

[1623] "Character data" refers to character information selected or input by the user through the eye-gaze input device.

[1624] "Conversion" refers to the process of regenerating received character data into a handwritten style based on the learned characteristics of handwritten characters.

[1625] "Display and save" refers to the function of displaying the generated handwritten-style character data on the device screen and, if necessary, saving the data.

[1626] "Adding fluctuations to the pen pressure and curve angle" refers to the process of adding small changes to the generated handwritten-style character data to give the characters a handwritten feel.

[1627] "Bitmap format" refers to a format that represents image data in pixel units.

[1628] "Vector format" refers to a format in which image data is expressed using mathematical formulas and geometric elements.

[1629] A "generative AI model" refers to a model that uses artificial intelligence technology to learn the characteristics of handwritten characters and generate new data.

[1630] The present invention is a system that uses an eye-gaze input device and a generative AI model to generate handwritten-style characters, providing the convenience of eye-gaze input without losing the warmth and individuality of handwriting. This system is specifically implemented using the following hardware and software.

[1631] Hardware

[1632] Scanner: A device that allows a user to convert past handwritten data into digital form.

[1633] Device (PC, tablet, etc.): A device used to capture handwritten data, analyze eye-gaze input data, and display and save handwritten-style character data.

[1634] Server: A device that analyzes and learns from handwritten data and generates handwritten-style characters.

[1635] Eye-gaze input device: A device that tracks the user's gaze and captures their gaze movements as data (e.g., Tobii Eye Tracker).

[1636] Printer: A device for printing the generated handwritten-style character data.

[1637] software

[1638] Database (e.g. MySQL): Software for storing handwritten data and learning data.

[1639] Generative AI model (e.g., Python TensorFlow): A model that learns the features of handwritten data and generates handwritten-style characters.

[1640] Eye-gaze applications (e.g., Tobii Pro Lab): Applications that analyze data from eye-gaze devices and select characters.

[1641] Image viewer (e.g. Adobe Reader): Software for displaying the generated handwritten-style character data.

[1642] System Overview

[1643] The system consists of the following main components:

[1644] 1. Handwriting data collection and learning (server)

[1645] A scanner captures data that a user has previously handwritten and stores it on the device. The device then sends the handwritten data to a server, which analyzes and learns from it. A generative AI model analyzes and learns features such as character shape, pen pressure, and curve angle. This learning data serves as the basis for generating user-style character shapes for new data.

[1646] 2. Character selection by eye gaze input (user, device)

[1647] The user uses an eye-gaze input device to select characters on the screen. The device analyzes the eye-gaze input data and sends the selected character string (e.g., "Hello") to the server.

[1648] 3. Generating handwritten-style characters (server)

[1649] Based on the character data received by the server, the generative AI model generates handwritten-style characters. The characteristics of handwritten data learned in the past are reflected. The server adds fluctuations to the pen pressure and curve angle to prevent the character shapes from becoming unique. The generated handwritten-style character data is returned to the device in bitmap or vector format.

[1650] 4. Display and save handwritten characters (device, user)

[1651] The terminal receives the handwritten-style character data and displays it on the screen. The user can check the displayed handwritten characters and save or print them as needed.

[1652] Specific examples

[1653] Example 1: Writing a birthday message

[1654] 1. Learning handwritten data (server)

[1655] The system scans a handwritten message written by the user in the past, such as "Happy Birthday," and sends it from the device to the server.

[1656] The server analyzes this handwritten data, and the generative AI model learns features such as character shape and writing pressure.

[1657] 2. Character selection (user, terminal)

[1658] The user uses gaze input to select the string they want to enter: "Happy Birthday."

[1659] The device sends the results of the gaze input to the server.

[1660] 3. Generating handwritten-style characters (server)

[1661] The server converts the string "Happy Birthday" selected through eye gaze input into handwritten text, adjusting the pen pressure and curve as needed based on past learning data.

[1662] The generated handwritten character data is sent to the terminal.

[1663] 4. Display and storage (device, user)

[1664] The terminal displays the handwritten-style characters on the display, and if the user is satisfied after checking them, they can save or print them.

[1665] The finalized message can be sent as a letter or an electronic message.

[1666] In this way, the system of the present invention allows a user to easily generate handwritten-style characters using an eye-gaze input device, and create messages that retain the warmth and individuality of handwriting.

[1667] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1668] Step 1: Collecting and training handwriting data

[1669] Input: A scanned image file of data previously handwritten by a user (e.g., "Happy Birthday").

[1670] Operation:

[1671] 1. The user uses a scanner to capture handwritten data as a digital image on the device.

[1672] 2. The device sends the image file to the server. Specifically, the image file is uploaded to the server using the HTTP protocol.

[1673] Output: Handwritten data stored on the server.

[1674] Specific behavior:

[1675] The server stores the received image file in a database.

[1676] The server's generative AI model analyzes this data and learns characteristics such as character shape, pen pressure, and curve angle. This learning data is then used to generate new data.

[1677] Step 2: Select text with gaze input

[1678] Input: Gaze data captured from the user's eye tracking device.

[1679] Operation:

[1680] 1. The user uses an eye-gaze input device (e.g., Tobii Eye Tracker) to select text on the screen.

[1681] 2. The eye-gaze input device tracks the user's gaze and detects character selection.

[1682] 3. The device analyzes the gaze data and identifies the selected string (e.g., "Hello").

[1683] 4. The device sends the selected string to the server using the HTTP protocol.

[1684] Output: The string data received by the server.

[1685] Specific behavior:

[1686] The device uses a Python script to analyze the gaze data and extract the selected string.

[1687] The terminal transmits the extracted character string data to the server as an HTTP request.

[1688] Step 3: Generate handwritten characters

[1689] Input: Character data received by the server (e.g. "Hello").

[1690] Operation:

[1691] 1. The server uses a generative AI model to generate handwritten-style characters based on the character data received.

[1692] 2. The server uses the learned data to convert the characters into handwritten style, adding fluctuations to the pen pressure and curve angle.

[1693] 3. Convert the generated handwritten character data into bitmap or vector format.

[1694] 4. The server sends the generated handwritten-style character data to the device using the HTTP protocol.

[1695] Output: Generated handwritten character data.

[1696] Specific behavior:

[1697] The server runs a generative AI model (e.g., Python TensorFlow) and generates handwritten-style characters based on the input character data.

[1698] The server converts the generated handwritten-style character data into bitmap or vector format and sends it to the terminal as an HTTP response.

[1699] Step 4: Display and save handwritten text

[1700] Input: Handwritten-style character data received by the device.

[1701] Operation:

[1702] 1. The device displays the handwritten character data received from the server. Specifically, an image viewer (e.g., Adobe Reader) is used.

[1703] 2. The user checks the displayed handwritten characters and, if satisfied, saves or prints them.

[1704] 3. If you choose to save, the device will save the data to local storage or a cloud service. If you choose to print, the device will print it out.

[1705] Output: Saved handwriting data or printed handwriting.

[1706] Specific behavior:

[1707] The terminal displays the received handwritten-style character data using an image viewer.

[1708] The user clicks the save button to save the data or the print button to print it out on a printer.

[1709] Through the specific actions performed at each step, users can generate handwritten-style characters using only their gaze, easily creating messages that retain the warmth and individuality of handwriting.

[1710] (Application example 1)

[1711] 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."

[1712] In systems that generate handwritten-style characters using eye gaze input, users can easily create characters that have the warmth and individuality of handwriting. However, the lack of a function to apply this handwritten-style character to video or image content makes it difficult for users to easily create practical content. Therefore, there is a need not only for generating handwritten-style characters using eye gaze input, but also for an efficient means to apply them to video and images.

[1713] 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.

[1714] In this invention, the server includes means for collecting past handwritten data and analyzing it to learn the characteristics of handwritten characters, means for receiving character data selected by a user using an eye-gaze input device, means for converting the received character data into a handwritten-style character based on the learned characteristics of the handwritten characters, means for outputting the converted handwritten character data, and means for applying handwritten-style characters to image or video content based on the eye-gaze data collected using the eye-gaze input device. This allows a user to easily apply handwritten-style characters created using eye-gaze input to video or image content, thereby enabling the creation of original, unique content.

[1715] "Past handwritten data" refers to character information previously handwritten by a user, and is data that the learning model uses to analyze handwriting characteristics.

[1716] An "eye gaze input device" is a device that detects the movement of a user's eye gaze and uses that information as input data.

[1717] "Handwritten character features" refer to the individual attributes of handwritten characters, such as character shape, pen pressure, and curve angle, and by analyzing and learning these, it becomes possible to generate handwritten-style characters.

[1718] "Received character data" refers to character information selected by the user that is acquired from the eye-gaze input device, and handwritten-style characters are generated based on this data.

[1719] "Converting to handwritten style" refers to converting received character data into a character format that has the warmth and individuality unique to handwriting, based on past handwritten data.

[1720] "Handwritten-style character data" refers to character information that has been converted to look like handwritten text by a generative AI model that reflects the characteristics of past handwritten data.

[1721] "Gaze data" refers to the movement and position information of a user's gaze collected by an eye-gaze input device, and is used for character selection, etc.

[1722] "Image or video content" refers to media content in the form of still images or videos, to which handwritten-style characters can be added.

[1723] The present invention is a system that uses eye gaze input to generate handwritten-style characters and apply these characters to image or video content. Each component of the present invention and its operation are described in detail below.

[1724] System configuration

[1725] The system consists of the following main components:

[1726] 1. Handwriting data collection and learning

[1727] 2. Character selection using eye gaze input

[1728] 3. Generating handwritten-style characters

[1729] 4. Applying handwritten text to content

[1730] 5. Output and Saving

[1731] Handwriting data collection and learning

[1732] Users scan or digitally import previously written handwriting data into their device. The device then sends this handwriting data to a cloud server. The server stores the handwriting data in a database, and the generative AI model analyzes and learns features such as character shape, pen pressure, and curve angle. This learning data serves as a reference for generating user-style character shapes for new gaze input data.

[1733] Selecting characters using eye gaze input

[1734] The user selects characters on the screen by guiding their gaze using an eye-gaze input device (e.g., a smartphone camera). The device analyzes the eye-gaze input and transmits the selected character data to a cloud server.

[1735] Generating handwritten characters

[1736] The server uses a generative AI model to generate handwritten-style characters based on the received character data. The server incorporates the characteristics of previous handwritten data, adding fluctuations to the pen pressure and curve angle. The generated handwritten-style character data is then sent back to the device in bitmap or vector format.

[1737] Applying handwritten text to content

[1738] The device applies the received handwritten-style character data to the image or video content specified by the user, thereby generating content containing a unique handwritten-style message.

[1739] Output and save

[1740] The generated content is displayed on the device's display, and the user can check the displayed content and save or share it if satisfied.

[1741] Hardware and software used

[1742] Camera: Uses your phone's camera to detect your gaze.

[1743] Cloud server: A server that learns handwritten data and processes the generation of handwritten-style characters, and also includes a generative AI model.

[1744] Generative AI model: A model that generates handwritten-style characters using deep learning frameworks such as TensorFlow.

[1745] Examples of concrete examples and prompts

[1746] Examples:

[1747] The user uses an eye-gaze input device to select the word "Thank you." The selected text is sent to a cloud server, where a generative AI model converts it into handwritten-style text based on past handwriting data. This handwritten-style text is then applied to the video message the user is creating, and the "Thank you" part is displayed in handwritten style.

[1748] Example prompt sentence:

[1749] "Using the gaze-selected string 'Thank you', generate handwritten-style characters based on past handwriting data."

[1750] This invention allows users to easily generate handwritten-style characters using an eye-gaze input device and apply them to content, making it possible to create attractive digital content while preserving the warmth and individuality of handwriting.

[1751] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1752] Step 1:

[1753] The user scans or digitally imports previously handwritten data into the device. The device then sends the data to a cloud server, where it stores the handwritten data in a database. The generative AI model analyzes and learns from the character's shape, pen pressure, curve angle, and other characteristics.

[1754] Input: Handwritten data

[1755] Data processing: Analysis of character shape, pen pressure, and curve angle

[1756] Output: Trained model

[1757] Step 2:

[1758] The user selects characters on the screen by using the eye-gaze input device. The terminal acquires the user's eye-gaze data through the eye-gaze input device and transmits the selected character data to the cloud server.

[1759] Input: Gaze data, selected character data

[1760] Data processing: Analysis of gaze data, generation of text data

[1761] Output: Received character data

[1762] Step 3:

[1763] The server uses a generative AI model based on the received character data to generate handwritten-style characters. It adds variations to the character shape, pen pressure, and curve angle to reflect the characteristics of previous handwritten data. The generated handwritten-style character data is then sent back to the device in bitmap or vector format.

[1764] Input: Received character data, trained model

[1765] Data processing: Generating handwritten characters based on character shape, pressure, and curve angle

[1766] Output: Handwritten character data

[1767] Step 4:

[1768] The device applies the received handwritten-style character data to the image or video content specified by the user, drawing the handwritten-style characters on the content and adjusting the layout as needed.

[1769] Input: Handwritten text, image or video content

[1770] Data processing: Drawing handwritten characters, adjusting layout

[1771] Output: Content with handwritten text applied

[1772] Step 5:

[1773] The device displays the generated content on the display for the user to review, and if the user is satisfied, they can save the generated content and share it as needed.

[1774] Input: Content with handwritten text applied

[1775] Data processing: Displaying content, user confirmation

[1776] Output: Saved content, shared content

[1777] 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.

[1778] The present invention combines an emotion engine with a system that allows users who have difficulty writing by hand to use eye gaze input to generate handwritten-style characters and express the warmth and individuality of handwriting. The system of the present invention analyzes past handwritten data to learn the characteristics of handwritten characters, and makes it possible to convert character data acquired by eye gaze input into handwritten-style characters based on the emotion engine.

[1779] System Overview

[1780] The system consists of the following main components:

[1781] 1. Handwritten data analysis and learning (server)

[1782] 2. Character selection by eye gaze input (user, device)

[1783] 3. Emotion recognition using emotion engine (server)

[1784] 4. Generating handwritten-style characters (server)

[1785] 5. Display and save handwritten style characters (device, user)

[1786] Program processing

[1787] Learning Phase

[1788] Handwriting data collection and learning (server)

[1789] Data that a user has previously handwritten is scanned or digitally imported into the terminal.

[1790] The device sends handwritten data to a server, which stores it in a database and a generative AI model analyzes and learns features such as character shape, pen pressure, and curve angle.

[1791] This training data serves as a reference for generating user-style character shapes for new input data.

[1792] Gaze input phase

[1793] Character selection (user, terminal)

[1794] A user uses an eye-gaze input device to select characters on the screen by gazing at them.

[1795] The device analyzes the gaze input data and sends the selected string (e.g., "Hello") to the server.

[1796] Emotion Recognition Phase

[1797] Emotion Recognition (Server)

[1798] The emotion engine installed on the server analyzes the user's gaze, facial expressions, voice input, etc. to recognize their current emotional state.

[1799] The recognized emotion data is reflected in the handwritten character generation process.

[1800] Handwritten character generation phase

[1801] Handwritten character generation (server)

[1802] Based on the character data received by the server, the generative AI model generates handwritten-style characters, incorporating the characteristics of handwritten data learned in the past and emotional information from the emotion engine.

[1803] Fluctuations in pen pressure, curve angle, and even the color and style of the text are added based on emotion.

[1804] The generated handwritten character data is sent back to the terminal in bitmap or vector format.

[1805] Result output phase

[1806] Display and save handwritten style characters (device, user)

[1807] The terminal receives the handwritten character data and displays it on the screen.

[1808] The user can review the displayed handwriting and save or print it if desired.

[1809] Specific examples

[1810] Example 1: Writing a birthday message

[1811] 1. Learning handwritten data (server)

[1812] The system scans a handwritten message written by the user in the past, such as "Happy Birthday," and sends it from the device to the server.

[1813] The server analyzes this handwritten data, and the generative AI model learns features such as character shape and writing pressure.

[1814] 2. Character selection (user, terminal)

[1815] The user uses gaze input to select the string they want to enter: "Happy Birthday."

[1816] The device sends the results of the gaze input to the server.

[1817] 3. Emotion Recognition (Server)

[1818] The server analyzes the user's gaze and facial expressions to recognize their current emotional state (e.g., joy).

[1819] The recognized emotion data is reflected in the handwritten character generation process.

[1820] 4. Generating handwritten-style characters (server)

[1821] The server converts the string "Happy Birthday" selected through eye gaze input into handwritten text. Past learning data and recognized emotional information (joy) are reflected in the conversion, adding fluctuations to the pressure and curve of the characters, as well as brightening the colors and style.

[1822] The generated handwritten character data is sent to the terminal.

[1823] 5. Display and storage (device, user)

[1824] The terminal displays the handwritten-style characters on the display, and if the user is satisfied after checking them, they can save or print them.

[1825] The finalized message can be sent as a letter or an electronic message.

[1826] In this way, the system of the present invention allows users to use eye gaze input to generate handwritten-style characters that reflect their own emotions, allowing them to create messages that incorporate the warmth and individuality of handwriting, as well as emotional expression.

[1827] The processing flow will be explained below.

[1828] Step 1: Collect handwritten data (user)

[1829] Scan or photograph letters or messages that the user has previously written.

[1830] The user imports scanned data or photos into the device.

[1831] Step 2: Send handwritten data (device)

[1832] The handwritten data captured by the terminal is sent to the server.

[1833] When sending, the data is sent with the user ID and timestamp attached.

[1834] Step 3: Storing and analyzing handwritten data (server)

[1835] The server stores the received handwritten data in a database.

[1836] The server analyzes the handwritten data and extracts features such as character shape, pen pressure, and curve angle.

[1837] Step 4: Feature Learning (Server)

[1838] The server uses a generative AI model to learn the characteristics of the extracted handwritten data.

[1839] The learned features are stored in a database and used as a basis for generating handwritten-style characters for new input data.

[1840] Step 5: Acquiring Gaze Input Data (User)

[1841] A user uses an eye-gaze input device to select characters on the screen by gazing at them.

[1842] The user selects a string of characters (for example, "hello") by eye gaze input.

[1843] Step 6: Sending gaze input data (device)

[1844] The device analyzes the gaze input data and sends the selected string "Hello" to the server.

[1845] Step 7: Emotion Recognition (Server)

[1846] The emotion engine installed on the server analyzes the user's gaze, facial expressions, voice input, etc. to recognize their current emotional state.

[1847] The recognized emotion data is reflected in the handwritten character generation process.

[1848] Step 8: Receiving a request to generate handwritten characters (server)

[1849] The server receives the text data "Hello" and emotion data from the terminal.

[1850] The server starts processing as a request to generate handwritten-style characters based on this character data and emotion data.

[1851] Step 9: Generate handwritten characters (server)

[1852] The server uses the generative AI model to convert the received text data "Hello" into handwritten text, incorporating the characteristics of handwritten data learned in the past and emotional information from the emotion engine.

[1853] Fluctuations in pen pressure, curve angle, and even the color and style of the text are added based on emotion.

[1854] Step 10: Determine the output format of the generated data (server)

[1855] The server converts the generated handwritten characters into output data in bitmap or vector format.

[1856] Define the output format and create the data to send back to the terminal.

[1857] Step 11: Returning handwritten character data (server)

[1858] The server returns the generated handwritten character data to the terminal.

[1859] Step 12: Displaying Handwritten Characters (Device)

[1860] The terminal receives the returned handwritten character data and displays it on the screen.

[1861] The user checks the displayed handwriting.

[1862] Step 13: Saving and using handwritten characters (user)

[1863] The user reviews the displayed handwriting and, if satisfied, saves or prints it.

[1864] Send it as a letter or message if necessary.

[1865] Example 2

[1866] 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."

[1867] With current text input systems, many users find it difficult to capture the warmth, individuality, and emotion of handwriting. Furthermore, for users of eye-gaze input devices, there are limited ways to easily create handwritten-style text. This makes it difficult to create emotionally charged messages using eye-gaze input.

[1868] 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.

[1869] In this invention, the server includes means for collecting past handwritten data and analyzing it to learn the characteristics of handwritten characters, means for receiving character data selected by a user using an eye-gaze input device, means for analyzing the user's eye movements and facial expressions to recognize an emotional state, means for converting the received character data into handwritten-style characters based on the learned handwritten character characteristics and the recognized emotional state, and means for outputting the converted handwritten character data, thereby enabling a user to generate handwritten-style characters through eye-gaze input and create an emotional message.

[1870] "Handwritten data" refers to digital data of characters and figures handwritten by a user.

[1871] An "eye gaze input device" is a device for tracking the movement of a user's eye gaze and acquiring that data.

[1872] "Character data" refers to digital information of a character string input by a user, acquired through an eye-gaze input device or other input means.

[1873] An "emotion engine" is software or hardware that has the function of recognizing a user's emotional state by analyzing the user's gaze movements, facial expressions, voice input, etc.

[1874] The "means for converting to a handwritten-like form" is a system or program for converting digital character data to a handwritten-like form based on the characteristics of the handwritten data.

[1875] "Pen pressure" refers to the strength of pressure that the pen applies to the paper when writing by hand, and is one of the characteristics of handwriting.

[1876] The "angle of the curve" refers to the angle of the curved part of handwritten characters, and is a feature that expresses the individuality of handwriting.

[1877] "Fluctuation" refers to the randomness in the pressure and angle of the curve of handwritten characters, and is a variable element used to express the naturalness and individuality of handwriting.

[1878] A "generative AI model" is an artificial intelligence model trained to analyze the characteristics of handwritten data and generate new handwritten-style characters based on that data.

[1879] A "bitmap" is a digital image file format that makes up an image on a pixel-by-pixel basis.

[1880] A "vector format" is a digital image file format that represents an image using mathematical formulas such as points, lines, and curves.

[1881] This invention combines an emotion engine with a system that allows users who have difficulty writing by hand to generate handwritten-style characters using an eye-gaze input device, thereby expressing the warmth and individuality of handwriting. This system consists of the following main components:

[1882] Handwriting data collection and learning

[1883] 1. Handwritten data collection

[1884] Users can scan previously handwritten data or write it on a digital device such as a tablet, for example, by scanning special messages or everyday notes and converting them into digital form.

[1885] The device stores this digital handwriting data and sends it to a server, where it uploads the data using its internet connection.

[1886] 2. Analysis and learning of handwritten data

[1887] The server stores the received handwritten data in a database. The handwritten feature information includes the character shape, writing pressure, and curve angle.

[1888] A generative AI model analyzes this data and learns the unique characteristics of the user's handwriting.

[1889] Selecting characters using eye gaze input

[1890] 3. Selecting the characters

[1891] The user wears an eye-gaze input device and the movement of the user's eyes is tracked. For example, an eye tracker is a commonly used eye-gaze input device.

[1892] The device analyzes the data from the eye-gaze input device and recognizes the characters the user is looking at. The user selects a character string, such as "hello," from a list of characters displayed on the screen.

[1893] Emotion recognition

[1894] 4. Emotional Recognition

[1895] The emotion engine installed on the server analyzes the user's gaze, facial expression, and, if necessary, voice input to recognize their current emotional state, which can include, for example, "happiness" or "sadness."

[1896] The recognized emotion data is reflected in the handwritten character generation process.

[1897] Generating handwritten characters

[1898] 5. Generating handwritten-style characters

[1899] Based on the character data and emotion data received by the server, the generative AI model generates handwritten-style characters. The generative AI model applies the characteristics of handwritten data it has learned in the past, and adjusts the pen pressure, curve angle, and even color to reflect the emotion information.

[1900] For example, when you type "hello," the pen pressure will fluctuate based on the emotional information, and the emotion will also be reflected in the color and style.

[1901] Display and save handwritten characters

[1902] 6. Display and save handwritten characters

[1903] The terminal receives the generated handwritten-style character data and displays it on the display.

[1904] The user checks the displayed handwritten-style characters and, if satisfied, saves or prints them. The device uses the save function to save them as a digital file or connects to a printer to print them.

[1905] Specific examples

[1906] Example: How to write a birthday message

[1907] 1. Learning from handwritten data

[1908] The system scans past messages written by users saying "Happy Birthday" and stores them on the device, which then sends this data to a server.

[1909] The server analyzes this data, and the generative AI model learns the character shape, pressure, and curve angle.

[1910] 2. Selecting the characters

[1911] The user uses the eye-gaze input device to select the character string "Happy Birthday" with their eyes.

[1912] The device analyzes the gaze data and sends the selected character string to the server.

[1913] 3. Emotional Recognition

[1914] The server analyzes gaze movements and facial expressions to recognize the current emotional state (e.g., joy).

[1915] 4. Generating handwritten-style characters

[1916] The server converts the text "Happy Birthday" into handwritten text, adjusting the pen pressure, curve, color, etc. to reflect the emotional information (joy).

[1917] The server transmits the generated handwritten-style character data to the terminal.

[1918] 5. Display and save

[1919] The device displays handwritten-style characters on the screen for the user to confirm.

[1920] The user selects save or print and the terminal performs the action.

[1921] This system allows users to easily create unique, handwritten-style characters by using eye-gaze input, and to create emotive messages.

[1922] Prompt Sentence Examples

[1923] "I want to scan past handwritten messages and have the server learn from them."

[1924] "I want to use my eye-gaze device to select text on the screen."

[1925] "I want to analyze the current emotion and reflect it in handwritten-style characters."

[1926] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1927] Step 1: Collecting handwritten data

[1928] Input: A handwritten message written by the user in the past

[1929] What it does: Scans data that a user has previously handwritten or writes on a digital device such as a tablet. For example, scans a birthday card or letter and converts it into a digital format.

[1930] Output: Handwritten data in digital format

[1931] Step 2: Send handwritten data

[1932] Input: Handwritten data in digital format

[1933] Specific operation: The handwritten data captured by the device in a scanned or digital format is sent to a server via the Internet, and the handwritten data is uploaded to cloud storage by calling an API.

[1934] Output: Handwritten data stored on the server

[1935] Step 3: Analyzing and learning handwritten data

[1936] Input: Handwritten data stored on the server

[1937] Specific operation: The server inputs the handwritten data it receives into the generative AI model, which analyzes features such as character shape, pen pressure, and curve angle. The generative AI model learns from this data, extracts and saves the user's unique handwritten character characteristics.

[1938] Output: Trained handwritten character feature data

[1939] Step 4: Selecting the text

[1940] Input: User's gaze input data

[1941] Specific operation: The user wears an eye-gaze input device and selects characters displayed on the screen with their eyes. The device analyzes the data from the eye-gaze input device in real time and identifies the character the user is looking at. For example, the character string "Hello" is selected.

[1942] Output: String selected by gaze

[1943] Step 5: Sending characters

[1944] Input: Gaze-selected string

[1945] Specific operation: The device sends the selected string to the server, and the character data is uploaded to the server via the Internet.

[1946] Output: Character data received by the server

[1947] Step 6: Recognize emotions

[1948] Input: User's gaze or facial expression data

[1949] Specific behavior: The server's emotion engine analyzes eye movements and facial expressions, and optionally voice input data, to identify the current emotional state (e.g., joy or sadness).

[1950] Output: Recognized emotional state data

[1951] Step 7: Generate handwritten characters

[1952] Input: Character data received by the server, trained handwriting feature data, recognized emotional state data

[1953] How it works: Based on the character data and emotion data received by the server, the generative AI model generates handwritten-style characters. It applies the characteristics of handwritten data it has learned in the past and adjusts the pen pressure, curve angle, color, etc. to reflect the recognized emotion information. For example, it converts the string "hello" into a handwritten-style character and adds bright colors and styles based on the emotion of joy.

[1954] Output: Generated handwritten character data

[1955] Step 8: Send handwritten text

[1956] Input: Generated handwritten-style character data

[1957] Specific operation: The server generates handwritten character data and sends it to the device in bitmap or vector format. The data is then returned via the Internet.

[1958] Output: Handwritten character data received by the device

[1959] Step 9: Displaying handwritten characters

[1960] Input: Handwritten-style character data received by the terminal

[1961] Specific operation: The handwritten character data received by the device is displayed on the display, and a graphic representation is provided so that the user can check it in real time.

[1962] Output: Handwritten characters displayed on the screen

[1963] Step 10: Save and print your handwritten text

[1964] Input: Handwritten characters displayed on the screen

[1965] Specific operation: The user checks the handwritten-style characters and selects whether to save or print them. The device uses the save function to save them as a digital file or connects to a printer to print them.

[1966] Output: Saved handwriting file or printed handwriting

[1967] (Application example 2)

[1968] 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."

[1969] When users who have difficulty writing by hand use eye-gaze input to generate handwritten-like characters, it is difficult to express warmth and individuality in the characters. Creating messages that reflect the user's emotions is also a challenge. In food delivery applications, there is a need for technology that can generate handwritten-like messages using eye-gaze input devices and convey emotions such as gratitude and encouragement.

[1970] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting past handwritten data and analyzing it to learn the characteristics of handwritten characters, means for receiving character data selected by a user using an eye-gaze input device, means for converting the received character data into handwritten-style characters based on the learned characteristics of handwritten characters, means for outputting the converted handwritten character data, and means for recognizing the emotional state of the user and adjusting the characteristics of handwritten-style characters based on the emotion. This enables a user to create a handwritten-style message that reflects their personality and emotion through the eye-gaze input device.

[1971] "Past handwritten data" refers to character information that a user has handwritten in the past, and serves as basic data for analyzing and learning the characteristics of handwritten characters.

[1972] "Handwritten character features" refer to individual attributes of handwritten characters, such as the shape, pressure, and angle of the curve, and are used as reference when generating handwritten-style characters.

[1973] An "eye gaze input device" is a device that includes hardware and software for tracking the movement of a user's eye gaze.

[1974] "Character data" refers to character information entered or selected by the user, and is the original data that is converted into handwritten-style characters.

[1975] The "means for converting to look like handwriting" refers to the process of processing character data entered by eye gaze input to look like handwriting based on the collected and learned characteristics of the handwriting data.

[1976] "Converted handwritten character data" refers to character information that has been processed to look like handwriting, and is output in bitmap or vector format.

[1977] An "emotional state" refers to the emotion a user is feeling at a particular moment, and may include joy, sadness, anger, etc.

[1978] "Means for adjusting the characteristics of handwritten-style characters based on emotions" refers to a process of analyzing a user's emotional state and adjusting the pressure, curve angle, color, style, etc. of the handwritten characters based on that information.

[1979] The present invention is a system that uses eye gaze input to generate handwritten-style characters, particularly in food delivery applications, allowing users to send thank you or other emotional messages to delivery staff or stores.

[1980] System Overview

[1981] The system consists of the following main components:

[1982] 1. Handwritten data analysis and learning (server)

[1983] 2. Character selection by eye gaze input (user, device)

[1984] 3. Emotion recognition using emotion engine (server)

[1985] 4. Generating handwritten-style characters (server)

[1986] 5. Display and save handwritten style characters (device, user)

[1987] Program Description

[1988] Hardware and Software

[1989] Hardware: Eye tracking device, camera (for emotion recognition)

[1990] Software: OpenCV (image processing), EmotionRecognizer (emotion recognition model), GazeTracking (eye tracking library), HandwritingModel (handwriting generation model)

[1991] Analysis and learning of handwritten data

[1992] The server collects, analyzes, and learns from handwritten data that the user has written. Specifically, the generative AI model learns from the collected handwritten data and analyzes the characteristics of handwritten characters (character shape, writing pressure, angle of curve, etc.).

[1993] Selecting characters using eye gaze input

[1994] The user selects characters on the screen using the eye-gaze input device. The device analyzes the eye-gaze input and sends the selected character data to the server. For example, if the user selects the character string "Thank you," this data is sent.

[1995] emotion recognition

[1996] The emotion engine installed on the server analyzes the user's facial expressions and gaze data acquired from the camera to recognize their current emotional state, which can include joy, sadness, surprise, etc.

[1997] Generating handwritten characters

[1998] The server uses the received character data and the emotion information obtained from the emotion engine to generate handwritten-style characters. In this generation process, the pen pressure, curve angle, color, etc. are adjusted based on the characteristics of the handwritten data and the emotion information.

[1999] Display and save

[2000] The device receives handwritten-style character data sent from the server and displays it on the screen. The user can check it and save or send it if they are satisfied. This function allows users to input and send a handwritten-style message of thanks when ordering food delivery.

[2001] Specific examples

[2002] For example, consider a scenario where a user places an order using a food delivery application and sends a thank you message to the delivery person. When the user inputs "Thank you" using the eye-gaze input device, the system recognizes the user's emotion and generates a handwritten-style message that reflects that emotion.

[2003] An example of a prompt to be input to the generative AI model is as follows:

[2004] "Generate handwritten-style characters based on the string selected using gaze input and the recognized emotion information."

[2005] for example,

[2006] "selected_text = 'Thank you'

[2007] recognized_emotion = 'joy' The user's emotional state recognized by the AI

[2008] handwritten_text = generate_handwritten_text(selected_text, recognized_emotion)

[2009] print_handwritten_text(handwritten_text)”

[2010] In this way, a handwritten-style message that visually reflects the user's emotions can be generated and used when ordering food delivery.

[2011] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2012] Step 1:

[2013] Collects and learns handwritten data. The server collects handwritten data from users and stores it in a database. The generative AI model then analyzes this data and learns the characteristics of handwritten characters (such as character shape, pressure, and curve angle). The input is handwritten data, and the output is the learned handwritten character characteristics. Specific operations include scanning handwritten data, uploading the digital data, saving it to a database, and training the AI ​​model.

[2014] Step 2:

[2015] Character selection is performed using gaze input. The user uses an eye-gaze input device to select characters on the screen with their gaze. The device analyzes the gaze input data and sends the selected character data to the server. The input is gaze data, and the output is the selected character data. Specific operations include calibrating the eye-tracking device, collecting and analyzing gaze data, and sending the character selection results to the server.

[2016] Step 3:

[2017] Recognizes emotions. The server analyzes the user's facial expression and gaze data acquired from the camera and uses an emotion engine to recognize the user's current emotional state. The input is facial images and gaze data, and the output is recognized emotion information. Specific operations include capturing facial images with the camera, and image analysis and emotion classification with the emotion engine.

[2018] Step 4:

[2019] Generates handwritten-style characters. The server generates handwritten-style characters using a generative AI model based on the received character data and emotion information obtained from the emotion engine. The input is character data and emotion information, and the output is handwritten-style character data. Specific operations include generating handwritten-style characters using an AI model, adjusting pen pressure and curve angle, and changing colors.

[2020] Step 5:

[2021] This displays and saves handwritten-style characters. The terminal receives handwritten-style character data sent from the server and displays it on the screen. The user can check it and save or send it if they are satisfied. The input is handwritten-style character data, and the output is the displayed handwritten-style characters. Specific operations include converting the character data to bitmap or vector format, displaying it on the screen, and saving it to a file.

[2022] 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.

[2023] 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.

[2024] 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.

[2025] 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.

[2026] 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.

[2027] 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.

[2028] 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).

[2029] 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.

[2030] 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."

[2031] 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.

[2032] 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).

[2033] 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.

[2034] 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.

[2035] 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.

[2036] 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.

[2037] 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.

[2038] 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.

[2039] 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.

[2040] 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.

[2041] 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.

[2042] 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.

[2043] The following is further disclosed regarding the above embodiment.

[2044] (Claim 1)

[2045] A method for collecting and analyzing past handwritten data to learn the characteristics of handwritten characters;

[2046] means for receiving character data selected by a user using an eye-gaze input device;

[2047] means for converting received character data into a handwritten-like form based on the learned characteristics of handwritten characters;

[2048] means for outputting the converted handwritten character data;

[2049] A system including:

[2050] (Claim 2)

[2051] 2. The system according to claim 1, further comprising means for adding fluctuations to the pen pressure and the angle of the curve to the converted handwritten character data.

[2052] (Claim 3)

[2053] 10. The system of claim 1, further comprising: means for converting received character data into output data in bitmap or vector format.

[2054] "Example 1"

[2055] (Claim 1)

[2056] A method for collecting and analyzing past handwritten data to learn the characteristics of handwritten characters;

[2057] means for receiving character data selected by a user using an eye-gaze input device;

[2058] means for converting received character data into a handwritten-like form based on the learned characteristics of handwritten characters;

[2059] A means for displaying and saving the converted handwritten character data,

[2060] A system including:

[2061] (Claim 2)

[2062] 2. The system according to claim 1, further comprising means for adding fluctuations to the pen pressure and the angle of the curve to the converted handwritten character data.

[2063] (Claim 3)

[2064] 10. The system of claim 1, further comprising: means for converting received character data into output data in bitmap or vector format.

[2065] "Application Example 1"

[2066] (Claim 1)

[2067] A method for collecting and analyzing past handwritten data to learn the characteristics of handwritten characters;

[2068] means for receiving character data selected by a user using an eye-gaze input device;

[2069] means for converting received character data into a handwritten-like form based on the learned characteristics of handwritten characters;

[2070] means for outputting the converted handwritten character data;

[2071] means for applying handwritten-style characters to image or video content based on gaze data collected using an eye-gaze input device;

[2072] A system including:

[2073] (Claim 2)

[2074] 2. The system according to claim 1, further comprising means for adding fluctuations to the pen pressure and the angle of the curve to the converted handwritten character data.

[2075] (Claim 3)

[2076] 10. The system of claim 1, further comprising: means for converting received character data into output data in bitmap or vector format.

[2077] "Example 2: Combining Emotion Engines"

[2078] (Claim 1)

[2079] A method for collecting and analyzing past handwritten data to learn the characteristics of handwritten characters;

[2080] means for receiving character data selected by a user using an eye-gaze input device;

[2081] A means for recognizing an emotional state of a user by analyzing the user's gaze movements and facial expressions;

[2082] means for converting received character data into a handwritten style based on the learned handwritten character characteristics and the recognized emotional state;

[2083] means for outputting the converted handwritten character data;

[2084] A system including:

[2085] (Claim 2)

[2086] 10. The system according to claim 1, further comprising means for adding fluctuations to the pen pressure, curve angle, color, and style of the converted handwritten character data.

[2087] (Claim 3)

[2088] 10. The system of claim 1, further comprising: means for converting received character data into output data in bitmap or vector format.

[2089] "Application example 2 when combining emotion engines"

[2090] (Claim 1)

[2091] A method for collecting and analyzing past handwritten data to learn the characteristics of handwritten characters;

[2092] means for receiving character data selected by a user using an eye-gaze input device;

[2093] means for converting received character data into a handwritten-like form based on the learned characteristics of handwritten characters;

[2094] means for outputting the converted handwritten character data;

[2095] means for recognizing an emotional state of a user and adjusting characteristics of handwritten-style characters based on the emotional state;

[2096] A system including:

[2097] (Claim 2)

[2098] 2. The system according to claim 1, further comprising means for adding fluctuations to the pen pressure and the angle of the curve to the converted handwritten character data.

[2099] (Claim 3)

[2100] 10. The system of claim 1, further comprising: means for converting received character data into output data in bitmap or vector format. [Explanation of symbols]

[2101] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A method for collecting and analyzing past handwritten data to learn the characteristics of handwritten characters; means for receiving character data selected by a user using an eye-gaze input device; means for converting received character data into a handwritten-like form based on the learned characteristics of handwritten characters; means for outputting the converted handwritten character data; A system including:

2. 2. The system according to claim 1, further comprising means for adding fluctuations to the pen pressure and the angle of the curve to the converted handwritten character data.

3. 2. The system of claim 1, further comprising means for converting received character data into output data in bitmap or vector format.

Images