System

Abstract

An object of a system according to an embodiment is to convey warmth of handwritten characters even to a user with weakened muscle strength.SOLUTION: A system according to an embodiment includes a line-of-sight tracking unit, a character generation unit, a writing pressure addition unit, and a fluctuation addition unit. The eye tracking unit tracks the user's eye using the eye input device and identifies the selected character. The character generation unit analyzes the character information specified by the line-of-sight tracking unit, and generates a character based on the style of the past handwritten character of the user. The writing pressure applying section applies a writing pressure to the character generated by the character generating section. The fluctuation addition section adds a fluctuation to the character generated by the character generation section.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] With conventional techniques, it is difficult for users with weakened muscles to convey the warmth of handwritten characters, and there is room for improvement.

[0005] The system according to the embodiment aims to convey the warmth of handwritten characters even to users with weakened muscles. [Means for solving the problem]

[0006] The system according to the embodiment includes an eye gaze tracking unit, a character generation unit, a writing pressure adding unit, and a fluctuation adding unit. The eye gaze tracking unit tracks the user's gaze using an eye gaze input device and identifies a selected character. The character generation unit analyzes the character information identified by the eye gaze tracking unit and generates a character based on the user's past handwritten character style. The writing pressure adding unit adds writing pressure to the character generated by the character generation unit. The fluctuation adding unit adds fluctuation to the character generated by the character generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can convey the warmth of handwritten characters even to users with weakened muscles. [Brief explanation of the drawings]

[0008] [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. DETAILED DESCRIPTION OF THE INVENTION

[0009] 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.

[0010] First, the terms used in the following description will be explained.

[0011] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] 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.

[0013] 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.

[0014] 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), and Bluetooth (registered trademark).

[0015] 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."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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).

[0019] 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.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. 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 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. 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.

[0022] 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.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A system according to an embodiment of the present invention trains a large-scale language model (LLM) to learn a user's past handwritten characters and converts characters selected via eye gaze input into the user's handwritten characters for output. In this system, a user selects characters using an eye gaze input device, and the LLM analyzes the selected characters and generates characters based on the user's past handwritten style. The generated characters are modified to incorporate pen pressure and fluctuations to prevent each character from having a unique design. This allows even users with weakened muscles, such as muscular dystrophy patients, to use characters that retain the warmth and individuality of handwriting. For example, an eye gaze input device tracks the user's gaze and identifies the selected character. This information is then transmitted to the LLM, which generates characters based on the user's past handwritten style. The generated characters can be modified to incorporate pen pressure and fluctuations to add individuality to the characters. This allows the user to use characters that retain the warmth and individuality of handwriting. This allows even users with weakened muscles, such as muscular dystrophy patients, to use characters that retain the warmth and individuality of handwriting.

[0029] A character generation system according to an embodiment includes an eye gaze tracking unit, a character generation unit, a writing pressure adding unit, and a fluctuation adding unit. The eye gaze tracking unit uses an eye gaze input device to track the user's gaze and identify a selected character. Examples of the eye gaze input device include a camera-based device and an infrared-based device. The eye gaze tracking unit uses a camera-based device to detect the movement of the user's pupils and identify the direction of the gaze. The eye gaze tracking unit can also track the user's gaze using an infrared-based device. The character generation unit uses a large-scale language model (LLM) to analyze the character information identified by the eye gaze tracking unit and generate characters based on the user's past handwritten character style. For example, the character generation unit learns the user's past handwritten characters and generates characters based on the style. The character generation unit also includes a writing pressure adding unit that applies writing pressure to the generated characters and a fluctuation adding unit that adds fluctuation. The writing pressure adding unit applies writing pressure to the generated characters. For example, the writing pressure adding unit adds varying writing pressure to the generated characters, giving the characters a three-dimensional feel. The fluctuation adding unit adds fluctuation to the generated characters. For example, the fluctuation adding unit adds fluctuation to the generated characters, thereby giving the characters a natural fluctuation. As a result, the character generation system according to the embodiment can generate handwritten characters based on a user's gaze input, and by adding pen pressure and fluctuation, provide characters that have the warmth and individuality of handwriting.

[0030] The eye-gaze tracking unit can use an appropriate eye-gaze tracking method depending on the type of eye-gaze input device. For example, the eye-gaze tracking unit detects the movement of the user's pupils using a camera-based device to determine the direction of the gaze. The eye-gaze tracking unit can also track the user's gaze using an infrared-based device. For example, the eye-gaze tracking unit detects the reflection of the user's pupils using infrared rays to determine the direction of the gaze. This makes it possible to provide an optimal eye-gaze tracking method depending on the type of eye-gaze input device.

[0031] The character generation unit can collect learning data for learning characters handwritten by the user in the past. The character generation unit, for example, collects samples of characters handwritten by the user in the past. For example, the character generation unit collects image data of characters handwritten by the user in the past and uses the image data as learning data. The character generation unit can also collect data of characters handwritten by the user newly. For example, the character generation unit collects data of characters handwritten by the user using an eye-gaze input device and uses the data as learning data. This enables more accurate character generation by learning the user's handwritten characters in the past.

[0032] The gaze tracking unit can analyze the speed and pattern of the user's eye movement during gaze tracking to provide an optimal character selection method. For example, the gaze tracking unit can increase the sensitivity of character selection when the user's eye movement is fast. The gaze tracking unit can also decrease the sensitivity of character selection when the user's eye movement is slow. For example, the gaze tracking unit can analyze the pattern of the user's eye movement to provide an optimal character selection method. This can improve the accuracy of operation by providing an optimal character selection method according to the user's eye movement.

[0033] The gaze tracking unit can measure the degree of focus of the user's gaze during gaze tracking and dynamically adjust the sensitivity of character selection according to the degree of focus. For example, the gaze tracking unit increases the sensitivity of character selection when the user's gaze is focused. The gaze tracking unit can also decrease the sensitivity of character selection when the user's gaze is distracted. For example, the gaze tracking unit measures the degree of focus of the user's gaze in real time and adjusts the sensitivity of character selection. In this way, by adjusting the sensitivity of character selection according to the degree of focus of the user's gaze, erroneous selection can be prevented and operation accuracy can be improved.

[0034] The gaze tracking unit records the history of the user's gaze movements during gaze tracking and can improve the accuracy of gaze tracking based on the past data. For example, the gaze tracking unit records the history of the user's gaze movements and improves the accuracy of gaze tracking. The gaze tracking unit can also analyze the pattern of the user's gaze movements and improve the accuracy of gaze tracking. For example, the gaze tracking unit optimizes the gaze tracking algorithm based on the history of the user's gaze movements. This improves the accuracy of gaze tracking based on the history of the user's gaze movements, thereby improving the accuracy of operation.

[0035] The gaze tracking unit can adjust the gaze tracking accuracy during gaze tracking, taking into account variations in the user's ambient light. For example, the gaze tracking unit increases the gaze tracking accuracy when the ambient light is bright. The gaze tracking unit can also decrease the gaze tracking accuracy when the ambient light is dark. For example, the gaze tracking unit detects variations in the ambient light in real time and adjusts the gaze tracking accuracy. This allows the gaze tracking accuracy to be adjusted in accordance with variations in the user's ambient light, thereby improving the accuracy of operations.

[0036] The gaze tracking unit can detect the movement of the user's head during gaze tracking and correct the gaze tracking. For example, the gaze tracking unit corrects the gaze tracking when the user's head moves. The gaze tracking unit can also analyze the pattern of the user's head movement and correct the gaze tracking. For example, the gaze tracking unit detects the user's head movement in real time and corrects the gaze tracking. This allows the accuracy of operation to be improved by correcting the gaze tracking according to the user's head movement.

[0037] The eye tracking unit can customize the sensitivity of eye tracking by taking into account the user's visual acuity information when tracking the eye. For example, if the user has poor visual acuity, the eye tracking unit can increase the sensitivity of eye tracking. Furthermore, if the user has good visual acuity, the eye tracking unit can return the sensitivity of eye tracking to normal. For example, the eye tracking unit customizes the sensitivity of eye tracking based on the user's visual acuity information. This allows the accuracy of operation to be improved by customizing the sensitivity of eye tracking according to the user's visual acuity information.

[0038] When generating characters, the character generation unit can select an appropriate character style by analyzing the frequency and usage of the user's past handwritten characters. For example, the character generation unit preferentially selects a character style that the user frequently uses. The character generation unit can also analyze the user's past usage of handwritten characters and select an optimal character style. For example, the character generation unit selects an optimal character style based on the frequency of the user's handwritten characters. In this way, more appropriate characters can be generated by selecting an optimal character style based on the user's past frequency and usage of handwritten characters.

[0039] When generating characters, the character generation unit can generate characters that reflect the stroke order and writing habits of the user's handwritten characters. The character generation unit generates characters that reflect, for example, the stroke order of the user's handwritten characters. The character generation unit can also generate characters that reflect the user's writing habits. For example, the character generation unit learns the stroke order and writing habits of the user's handwritten characters and generates characters. In this way, by reflecting the stroke order and writing habits of the user's handwritten characters, more natural characters can be generated.

[0040] The character generation unit can adjust the size and spacing of the user's handwritten characters when generating characters to appropriately adjust the layout of the characters to be generated. The character generation unit adjusts the size of the characters to be generated based on, for example, the size of the user's handwritten characters. The character generation unit can also adjust the spacing of the characters to be generated based on the spacing of the user's handwritten characters. For example, the character generation unit learns the size and spacing of the user's handwritten characters and optimizes the layout of the characters to be generated. In this way, by adjusting the size and spacing of the user's handwritten characters, characters with a more appropriate layout can be generated.

[0041] The character generation unit can generate characters by reflecting the color and decoration of the user's handwritten characters when generating characters. The character generation unit generates characters by reflecting, for example, the color of the user's handwritten characters. The character generation unit can also generate characters by reflecting the decoration of the user's handwritten characters. For example, the character generation unit learns the color and decoration of the user's handwritten characters and generates characters. In this way, by reflecting the color and decoration of the user's handwritten characters, more unique characters can be generated.

[0042] The character generation unit can generate characters by reflecting the inclination and angle of the user's handwritten characters when generating characters. The character generation unit generates characters by reflecting, for example, the inclination of the user's handwritten characters. The character generation unit can also generate characters by reflecting the angle of the user's handwritten characters. For example, the character generation unit learns the inclination and angle of the user's handwritten characters and generates characters. In this way, by reflecting the inclination and angle of the user's handwritten characters, more natural characters can be generated.

[0043] The character generation unit can generate characters taking into consideration the continuity of the user's handwritten characters when generating characters. For example, the character generation unit generates characters taking into consideration the continuity of the user's handwritten characters. The character generation unit can also learn the continuity of the user's handwritten characters and generate characters. For example, the character generation unit generates characters based on the continuity of the user's handwritten characters. In this way, by taking into consideration the continuity of the user's handwritten characters, more natural characters can be generated.

[0044] When adding a fluctuation, the fluctuation adding unit can analyze fluctuations in the writing pressure of the user's handwritten characters and add an appropriate fluctuation. The fluctuation adding unit, for example, analyzes fluctuations in the writing pressure of the user's handwritten characters and adds an optimal fluctuation. The fluctuation adding unit can also learn fluctuations in the writing pressure of the user's handwritten characters and add an optimal fluctuation. For example, the fluctuation adding unit adds an optimal fluctuation based on fluctuations in the writing pressure of the user's handwritten characters. In this way, by analyzing fluctuations in the writing pressure of the user's handwritten characters, it is possible to add a more natural fluctuation.

[0045] When adding fluctuation, the fluctuation adding unit can add fluctuation by reflecting the speed and rhythm of the user's handwritten characters. The fluctuation adding unit adds fluctuation by reflecting, for example, the speed of the user's handwritten characters. The fluctuation adding unit can also add fluctuation by reflecting the rhythm of the user's handwritten characters. For example, the fluctuation adding unit learns the speed and rhythm of the user's handwritten characters and adds fluctuation. In this way, by reflecting the speed and rhythm of the user's handwritten characters, it is possible to add a more natural fluctuation.

[0046] When adding fluctuation, the fluctuation adding unit can add fluctuation by adjusting the line thickness and density of the user's handwritten characters. The fluctuation adding unit, for example, adds fluctuation by adjusting the line thickness of the user's handwritten characters. The fluctuation adding unit can also add fluctuation by adjusting the line density of the user's handwritten characters. For example, the fluctuation adding unit learns the line thickness and density of the user's handwritten characters and adds fluctuation. In this way, by adjusting the line thickness and density of the user's handwritten characters, it is possible to add a more natural fluctuation.

[0047] When adding fluctuation, the fluctuation adding unit can add fluctuation taking into consideration background information of the user's handwritten characters. The fluctuation adding unit, for example, adds an optimal fluctuation based on the background information of the user's handwritten characters. The fluctuation adding unit can also learn the background information of the user's handwritten characters and add an optimal fluctuation. For example, the fluctuation adding unit analyzes the background information of the user's handwritten characters and adds an optimal fluctuation. In this way, by taking into consideration the background information of the user's handwritten characters, it is possible to add a more natural fluctuation.

[0048] When adding a fluctuation, the fluctuation adding unit can add a fluctuation taking into consideration the continuity of the user's handwritten characters. For example, the fluctuation adding unit adds a fluctuation taking into consideration the continuity of the user's handwritten characters. The fluctuation adding unit can also learn the continuity of the user's handwritten characters and add a fluctuation. For example, the fluctuation adding unit adds a fluctuation based on the continuity of the user's handwritten characters. In this way, by taking into consideration the continuity of the user's handwritten characters, a more natural fluctuation can be added.

[0049] When adding fluctuation, the fluctuation adding unit can add fluctuation by reflecting the decoration and design of the user's handwritten characters. For example, the fluctuation adding unit adds fluctuation by reflecting the decoration of the user's handwritten characters. The fluctuation adding unit can also add fluctuation by reflecting the design of the user's handwritten characters. For example, the fluctuation adding unit learns the decoration and design of the user's handwritten characters and adds fluctuation. In this way, by reflecting the decoration and design of the user's handwritten characters, more unique characters can be generated.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The eye-tracking unit can analyze the speed and pattern of the user's eye movement to provide an optimal character selection method. For example, the eye-tracking unit can increase the sensitivity of character selection when the user's eye movement is fast. The eye-tracking unit can also decrease the sensitivity of character selection when the user's eye movement is slow. Furthermore, the eye-tracking unit can analyze the pattern of the user's eye movement to provide an optimal character selection method. This can improve the accuracy of operation by providing an optimal character selection method according to the user's eye movement.

[0052] The character generation unit can select an appropriate character style by analyzing the frequency of handwritten characters and usage status of the user in the past. For example, the character generation unit preferentially selects a character style that the user frequently uses. The character generation unit can also analyze the user's past usage status of handwritten characters and select an optimal character style. Furthermore, the character generation unit selects an optimal character style based on the frequency of handwritten characters by the user. In this way, more appropriate characters can be generated by selecting an optimal character style based on the user's past usage status of handwritten characters and frequency of handwritten characters.

[0053] The gaze tracking unit can measure the degree of focus of the user's gaze during gaze tracking and dynamically adjust the sensitivity of character selection according to the degree of focus. For example, the gaze tracking unit can increase the sensitivity of character selection when the user's gaze is focused. The gaze tracking unit can also decrease the sensitivity of character selection when the user's gaze is distracted. Furthermore, the gaze tracking unit can measure the degree of focus of the user's gaze in real time and adjust the sensitivity of character selection. In this way, by adjusting the sensitivity of character selection according to the degree of focus of the user's gaze, erroneous selections can be prevented and operation accuracy can be improved.

[0054] When generating characters, the character generation unit can generate characters that reflect the stroke order and writing habits of the user's handwritten characters. For example, the character generation unit generates characters that reflect the stroke order of the user's handwritten characters. The character generation unit can also generate characters that reflect the user's writing habits. Furthermore, the character generation unit learns the stroke order and writing habits of the user's handwritten characters and generates characters. In this way, by reflecting the stroke order and writing habits of the user's handwritten characters, more natural characters can be generated.

[0055] The eye tracking unit records the history of the user's eye movement during eye tracking, and can improve the accuracy of eye tracking based on the past data. For example, the eye tracking unit records the history of the user's eye movement and improves the accuracy of eye tracking. The eye tracking unit can also analyze the pattern of the user's eye movement and improve the accuracy of eye tracking. Furthermore, the eye tracking unit optimizes the eye tracking algorithm based on the history of the user's eye movement. This improves the accuracy of eye tracking based on the history of the user's eye movement, thereby improving the accuracy of operation.

[0056] When generating characters, the character generation unit can adjust the size and spacing of the user's handwritten characters to appropriately adjust the layout of the characters to be generated. For example, the character generation unit adjusts the size of the characters to be generated based on the size of the user's handwritten characters. The character generation unit can also adjust the spacing of the characters to be generated based on the spacing of the user's handwritten characters. Furthermore, the character generation unit learns the size and spacing of the user's handwritten characters and optimizes the layout of the characters to be generated. In this way, by adjusting the size and spacing of the user's handwritten characters, characters with a more appropriate layout can be generated.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The eye-gaze tracking unit uses an eye-gaze input device to track the user's gaze and identify the selected character. Eye-gaze input devices include camera-based devices and infrared-based devices. The eye-gaze tracking unit uses a camera-based device to detect the movement of the user's pupils and identify the direction of the gaze. Alternatively, the eye-gaze tracking unit can use an infrared-based device to track the user's gaze. Step 2: The character generation unit analyzes the character information identified by the gaze tracking unit and generates characters based on the user's past handwriting style. The character generation unit uses a large-scale language model (LLM) to learn the user's past handwriting and generates characters based on that style. Step 3: The writing pressure applying unit applies writing pressure to the characters generated by the character generating unit. The writing pressure applying unit applies varying degrees of writing pressure to the generated characters, thereby giving the characters a three-dimensional appearance. Step 4: The fluctuation adding unit adds fluctuation to the characters generated by the character generating unit. The fluctuation adding unit adds fluctuation to the generated characters, thereby giving the characters a natural fluctuation.

[0059] (Example 2) A system according to an embodiment of the present invention trains a large-scale language model (LLM) to learn a user's past handwritten characters and converts characters selected via eye gaze input into the user's handwritten characters for output. In this system, a user selects characters using an eye gaze input device, and the LLM analyzes the selected characters and generates characters based on the user's past handwritten style. The generated characters are modified to incorporate pen pressure and fluctuations to prevent each character from having a unique design. This allows even users with weakened muscles, such as muscular dystrophy patients, to use characters that retain the warmth and individuality of handwriting. For example, an eye gaze input device tracks the user's gaze and identifies the selected character. This information is then transmitted to the LLM, which generates characters based on the user's past handwritten style. The generated characters can be modified to incorporate pen pressure and fluctuations to add individuality to the characters. This allows the user to use characters that retain the warmth and individuality of handwriting. This allows even users with weakened muscles, such as muscular dystrophy patients, to use characters that retain the warmth and individuality of handwriting.

[0060] A character generation system according to an embodiment includes an eye gaze tracking unit, a character generation unit, a writing pressure adding unit, and a fluctuation adding unit. The eye gaze tracking unit uses an eye gaze input device to track the user's gaze and identify a selected character. Examples of the eye gaze input device include a camera-based device and an infrared-based device. The eye gaze tracking unit uses a camera-based device to detect the movement of the user's pupils and identify the direction of the gaze. The eye gaze tracking unit can also track the user's gaze using an infrared-based device. The character generation unit uses a large-scale language model (LLM) to analyze the character information identified by the eye gaze tracking unit and generate characters based on the user's past handwritten character style. For example, the character generation unit learns the user's past handwritten characters and generates characters based on the style. The character generation unit also includes a writing pressure adding unit that applies writing pressure to the generated characters and a fluctuation adding unit that adds fluctuation. The writing pressure adding unit applies writing pressure to the generated characters. For example, the writing pressure adding unit adds varying writing pressure to the generated characters, giving the characters a three-dimensional feel. The fluctuation adding unit adds fluctuation to the generated characters. For example, the fluctuation adding unit adds fluctuation to the generated characters, thereby giving the characters a natural fluctuation. As a result, the character generation system according to the embodiment can generate handwritten characters based on a user's gaze input, and by adding pen pressure and fluctuation, provide characters that have the warmth and individuality of handwriting.

[0061] The eye-gaze tracking unit can use an appropriate eye-gaze tracking method depending on the type of eye-gaze input device. For example, the eye-gaze tracking unit detects the movement of the user's pupils using a camera-based device to determine the direction of the gaze. The eye-gaze tracking unit can also track the user's gaze using an infrared-based device. For example, the eye-gaze tracking unit detects the reflection of the user's pupils using infrared rays to determine the direction of the gaze. This makes it possible to provide an optimal eye-gaze tracking method depending on the type of eye-gaze input device.

[0062] The character generation unit can collect learning data for learning characters handwritten by the user in the past. The character generation unit, for example, collects samples of characters handwritten by the user in the past. For example, the character generation unit collects image data of characters handwritten by the user in the past and uses the image data as learning data. The character generation unit can also collect data of characters handwritten by the user newly. For example, the character generation unit collects data of characters handwritten by the user using an eye-gaze input device and uses the data as learning data. This enables more accurate character generation by learning the user's handwritten characters in the past.

[0063] The gaze tracking unit can estimate the user's emotion and dynamically adjust the accuracy of the gaze tracking based on the estimated user emotion. For example, the gaze tracking unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the gaze tracking unit calculates an emotion score based on changes in facial expression. The gaze tracking unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the gaze tracking unit analyzes the tone and speed of the voice and calculates an emotion score. The gaze tracking unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the gaze tracking unit calculates an emotion score based on heart rate fluctuations. This allows the accuracy of the gaze tracking to be adjusted according to the user's emotion, preventing erroneous selections and providing a natural operating feel. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0064] The gaze tracking unit can analyze the speed and pattern of the user's eye movement during gaze tracking to provide an optimal character selection method. For example, the gaze tracking unit can increase the sensitivity of character selection when the user's eye movement is fast. The gaze tracking unit can also decrease the sensitivity of character selection when the user's eye movement is slow. For example, the gaze tracking unit can analyze the pattern of the user's eye movement to provide an optimal character selection method. This can improve the accuracy of operation by providing an optimal character selection method according to the user's eye movement.

[0065] The gaze tracking unit can measure the degree of focus of the user's gaze during gaze tracking and dynamically adjust the sensitivity of character selection according to the degree of focus. For example, the gaze tracking unit increases the sensitivity of character selection when the user's gaze is focused. The gaze tracking unit can also decrease the sensitivity of character selection when the user's gaze is distracted. For example, the gaze tracking unit measures the degree of focus of the user's gaze in real time and adjusts the sensitivity of character selection. In this way, by adjusting the sensitivity of character selection according to the degree of focus of the user's gaze, erroneous selection can be prevented and operation accuracy can be improved.

[0066] The gaze tracking unit records the history of the user's gaze movements during gaze tracking and can improve the accuracy of gaze tracking based on the past data. For example, the gaze tracking unit records the history of the user's gaze movements and improves the accuracy of gaze tracking. The gaze tracking unit can also analyze the pattern of the user's gaze movements and improve the accuracy of gaze tracking. For example, the gaze tracking unit optimizes the gaze tracking algorithm based on the history of the user's gaze movements. This improves the accuracy of gaze tracking based on the history of the user's gaze movements, thereby improving the accuracy of operation.

[0067] The gaze tracking unit can estimate the user's emotion and dynamically adjust the order in which the gaze tracking results are displayed based on the estimated user emotion. The gaze tracking unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the gaze tracking unit calculates an emotion score based on changes in facial expression. The gaze tracking unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the gaze tracking unit analyzes the tone and speed of the voice and calculates an emotion score. The gaze tracking unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the gaze tracking unit calculates an emotion score based on heart rate fluctuations. This allows important information to be displayed preferentially by adjusting the order in which the gaze tracking results are displayed based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0068] The gaze tracking unit can adjust the gaze tracking accuracy during gaze tracking, taking into account variations in the user's ambient light. For example, the gaze tracking unit increases the gaze tracking accuracy when the ambient light is bright. The gaze tracking unit can also decrease the gaze tracking accuracy when the ambient light is dark. For example, the gaze tracking unit detects variations in the ambient light in real time and adjusts the gaze tracking accuracy. This allows the gaze tracking accuracy to be adjusted in accordance with variations in the user's ambient light, thereby improving the accuracy of operations.

[0069] The gaze tracking unit can detect the movement of the user's head during gaze tracking and correct the gaze tracking. For example, the gaze tracking unit corrects the gaze tracking when the user's head moves. The gaze tracking unit can also analyze the pattern of the user's head movement and correct the gaze tracking. For example, the gaze tracking unit detects the user's head movement in real time and corrects the gaze tracking. This allows the accuracy of operation to be improved by correcting the gaze tracking according to the user's head movement.

[0070] The eye tracking unit can customize the sensitivity of eye tracking by taking into account the user's visual acuity information when tracking the eye. For example, if the user has poor visual acuity, the eye tracking unit can increase the sensitivity of eye tracking. Furthermore, if the user has good visual acuity, the eye tracking unit can return the sensitivity of eye tracking to normal. For example, the eye tracking unit customizes the sensitivity of eye tracking based on the user's visual acuity information. This allows the accuracy of operation to be improved by customizing the sensitivity of eye tracking according to the user's visual acuity information.

[0071] The character generation unit can estimate the user's emotions and dynamically adjust the style of the characters to be generated based on the estimated user's emotions. For example, if the user is relaxed, the character generation unit generates characters in a soft style. Furthermore, if the user is nervous, the character generation unit can also generate characters that are simple and highly visible. For example, if the user is having fun, the character generation unit generates characters in an ornamental style. This allows for more appropriate characters to be generated by adjusting the character style according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0072] When generating characters, the character generation unit can select an appropriate character style by analyzing the frequency and usage of the user's past handwritten characters. For example, the character generation unit preferentially selects a character style that the user frequently uses. The character generation unit can also analyze the user's past usage of handwritten characters and select an optimal character style. For example, the character generation unit selects an optimal character style based on the frequency of the user's handwritten characters. In this way, more appropriate characters can be generated by selecting an optimal character style based on the user's past frequency and usage of handwritten characters.

[0073] When generating characters, the character generation unit can generate characters that reflect the stroke order and writing habits of the user's handwritten characters. The character generation unit generates characters that reflect, for example, the stroke order of the user's handwritten characters. The character generation unit can also generate characters that reflect the user's writing habits. For example, the character generation unit learns the stroke order and writing habits of the user's handwritten characters and generates characters. In this way, by reflecting the stroke order and writing habits of the user's handwritten characters, more natural characters can be generated.

[0074] The character generation unit can adjust the size and spacing of the user's handwritten characters when generating characters to appropriately adjust the layout of the characters to be generated. The character generation unit adjusts the size of the characters to be generated based on, for example, the size of the user's handwritten characters. The character generation unit can also adjust the spacing of the characters to be generated based on the spacing of the user's handwritten characters. For example, the character generation unit learns the size and spacing of the user's handwritten characters and optimizes the layout of the characters to be generated. In this way, by adjusting the size and spacing of the user's handwritten characters, characters with a more appropriate layout can be generated.

[0075] The character generation unit can estimate the user's emotion and dynamically adjust the size of the generated characters based on the estimated user's emotion. For example, the character generation unit generates larger characters when the user is relaxed. The character generation unit can also generate smaller characters when the user is nervous. For example, the character generation unit generates normal-sized characters when the user is having fun. This allows for more appropriate characters to be generated by adjusting the character size according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0076] The character generation unit can generate characters by reflecting the color and decoration of the user's handwritten characters when generating characters. The character generation unit generates characters by reflecting, for example, the color of the user's handwritten characters. The character generation unit can also generate characters by reflecting the decoration of the user's handwritten characters. For example, the character generation unit learns the color and decoration of the user's handwritten characters and generates characters. In this way, by reflecting the color and decoration of the user's handwritten characters, more unique characters can be generated.

[0077] The character generation unit can generate characters by reflecting the inclination and angle of the user's handwritten characters when generating characters. The character generation unit generates characters by reflecting, for example, the inclination of the user's handwritten characters. The character generation unit can also generate characters by reflecting the angle of the user's handwritten characters. For example, the character generation unit learns the inclination and angle of the user's handwritten characters and generates characters. In this way, by reflecting the inclination and angle of the user's handwritten characters, more natural characters can be generated.

[0078] The character generation unit can generate characters taking into consideration the continuity of the user's handwritten characters when generating characters. For example, the character generation unit generates characters taking into consideration the continuity of the user's handwritten characters. The character generation unit can also learn the continuity of the user's handwritten characters and generate characters. For example, the character generation unit generates characters based on the continuity of the user's handwritten characters. In this way, by taking into consideration the continuity of the user's handwritten characters, more natural characters can be generated.

[0079] The fluctuation adding unit can estimate the user's emotion and dynamically adjust the intensity of the fluctuation based on the estimated user's emotion. For example, the fluctuation adding unit weakens the intensity of the fluctuation when the user is relaxed. The fluctuation adding unit can also increase the intensity of the fluctuation when the user is nervous. For example, the fluctuation adding unit returns the intensity of the fluctuation to normal when the user is having fun. This allows more appropriate characters to be generated by adjusting the intensity of the fluctuation according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0080] When adding a fluctuation, the fluctuation adding unit can analyze fluctuations in the writing pressure of the user's handwritten characters and add an appropriate fluctuation. The fluctuation adding unit, for example, analyzes fluctuations in the writing pressure of the user's handwritten characters and adds an optimal fluctuation. The fluctuation adding unit can also learn fluctuations in the writing pressure of the user's handwritten characters and add an optimal fluctuation. For example, the fluctuation adding unit adds an optimal fluctuation based on fluctuations in the writing pressure of the user's handwritten characters. In this way, by analyzing fluctuations in the writing pressure of the user's handwritten characters, it is possible to add a more natural fluctuation.

[0081] When adding fluctuation, the fluctuation adding unit can add fluctuation by reflecting the speed and rhythm of the user's handwritten characters. The fluctuation adding unit adds fluctuation by reflecting, for example, the speed of the user's handwritten characters. The fluctuation adding unit can also add fluctuation by reflecting the rhythm of the user's handwritten characters. For example, the fluctuation adding unit learns the speed and rhythm of the user's handwritten characters and adds fluctuation. In this way, by reflecting the speed and rhythm of the user's handwritten characters, it is possible to add a more natural fluctuation.

[0082] When adding fluctuation, the fluctuation adding unit can add fluctuation by adjusting the line thickness and density of the user's handwritten characters. The fluctuation adding unit, for example, adds fluctuation by adjusting the line thickness of the user's handwritten characters. The fluctuation adding unit can also add fluctuation by adjusting the line density of the user's handwritten characters. For example, the fluctuation adding unit learns the line thickness and density of the user's handwritten characters and adds fluctuation. In this way, by adjusting the line thickness and density of the user's handwritten characters, it is possible to add a more natural fluctuation.

[0083] The fluctuation adding unit can estimate the user's emotion and dynamically adjust the fluctuation pattern based on the estimated user's emotion. For example, if the user is relaxed, the fluctuation adding unit can apply a soft fluctuation pattern. Also, if the user is nervous, the fluctuation adding unit can apply a sharp fluctuation pattern. For example, if the user is having fun, the fluctuation adding unit can apply a random fluctuation pattern. This allows for adjusting the fluctuation pattern according to the user's emotion to generate more appropriate characters. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0084] When adding fluctuation, the fluctuation adding unit can add fluctuation taking into consideration background information of the user's handwritten characters. The fluctuation adding unit, for example, adds an optimal fluctuation based on the background information of the user's handwritten characters. The fluctuation adding unit can also learn the background information of the user's handwritten characters and add an optimal fluctuation. For example, the fluctuation adding unit analyzes the background information of the user's handwritten characters and adds an optimal fluctuation. In this way, by taking into consideration the background information of the user's handwritten characters, it is possible to add a more natural fluctuation.

[0085] When adding a fluctuation, the fluctuation adding unit can add a fluctuation taking into consideration the continuity of the user's handwritten characters. For example, the fluctuation adding unit adds a fluctuation taking into consideration the continuity of the user's handwritten characters. The fluctuation adding unit can also learn the continuity of the user's handwritten characters and add a fluctuation. For example, the fluctuation adding unit adds a fluctuation based on the continuity of the user's handwritten characters. In this way, by taking into consideration the continuity of the user's handwritten characters, a more natural fluctuation can be added.

[0086] When adding fluctuation, the fluctuation adding unit can add fluctuation by reflecting the decoration and design of the user's handwritten characters. For example, the fluctuation adding unit adds fluctuation by reflecting the decoration of the user's handwritten characters. The fluctuation adding unit can also add fluctuation by reflecting the design of the user's handwritten characters. For example, the fluctuation adding unit learns the decoration and design of the user's handwritten characters and adds fluctuation. In this way, by reflecting the decoration and design of the user's handwritten characters, more unique characters can be generated. === Hard Collateral 1-1 === Each of the multiple elements, including the gaze tracking unit, character generation unit, writing pressure application unit, and fluctuation application unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the gaze tracking unit can track the user's gaze using the camera 42 or microphone 38B of the smart device 14 to identify selected characters. The character generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates characters based on the user's past handwriting style using LLM. The writing pressure application unit and fluctuation application unit are implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and add writing pressure and fluctuation to the generated characters to give them individuality. === Hard Collateral 1-2 === Each of the multiple elements, including the gaze tracking unit, character generation unit, writing pressure adding unit, and fluctuation adding unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the gaze tracking unit can track the user's gaze using the camera 42 or the microphone 238 of the smart glasses 214 and identify the selected character. The character generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates characters based on the user's past handwriting style using LLM. The writing pressure adding unit and fluctuation adding unit are realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and add writing pressure and fluctuation to the generated characters, thereby giving the characters individuality. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned gaze tracking unit, character generation unit, writing pressure adding unit, and fluctuation adding unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the gaze tracking unit can track the user's gaze using the camera 42 or the microphone 238 of the headset-type terminal 314 and identify selected characters. The character generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates characters based on the user's past handwritten character style using an LLM. The writing pressure adding unit and fluctuation adding unit are realized by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12 and add writing pressure and fluctuation to the generated characters to give the characters individuality. === Hard Collateral 1-4 === Each of the multiple elements, including the gaze tracking unit, character generation unit, writing pressure adding unit, and fluctuation adding unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the gaze tracking unit can track the user's gaze using the camera 42 or microphone 238 of the robot 414 and identify the selected character. The character generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates characters based on the user's past handwritten character style using the LLM. The writing pressure adding unit and fluctuation adding unit are realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and add writing pressure and fluctuation to the generated characters, thereby giving the characters individuality.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The eye-tracking unit can analyze the speed and pattern of the user's eye movement to provide an optimal character selection method. For example, the eye-tracking unit can increase the sensitivity of character selection when the user's eye movement is fast. The eye-tracking unit can also decrease the sensitivity of character selection when the user's eye movement is slow. Furthermore, the eye-tracking unit can analyze the pattern of the user's eye movement to provide an optimal character selection method. This can improve the accuracy of operation by providing an optimal character selection method according to the user's eye movement.

[0089] The character generation unit can select an appropriate character style by analyzing the frequency of handwritten characters and usage status of the user in the past. For example, the character generation unit preferentially selects a character style that the user frequently uses. The character generation unit can also analyze the user's past usage status of handwritten characters and select an optimal character style. Furthermore, the character generation unit selects an optimal character style based on the frequency of handwritten characters by the user. In this way, more appropriate characters can be generated by selecting an optimal character style based on the user's past usage status of handwritten characters and frequency of handwritten characters.

[0090] The gaze tracking unit can estimate the user's emotion and dynamically adjust the accuracy of gaze tracking based on the estimated user emotion. For example, the gaze tracking unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The gaze tracking unit then calculates an emotion score based on changes in the facial expression. The gaze tracking unit can also record the user's voice and estimate the emotion using voice analysis technology. This allows the accuracy of gaze tracking to be adjusted according to the user's emotion, preventing erroneous selections and providing a natural feel to the user.

[0091] The character generation unit can estimate the user's emotion and dynamically adjust the style of the generated characters based on the estimated user's emotion. For example, if the user is relaxed, the character generation unit can generate characters in a soft style. Also, if the user is nervous, the character generation unit can generate characters that are simple and highly visible. Furthermore, if the user is having fun, the character generation unit can generate characters in an ornamental style. In this way, by adjusting the character style according to the user's emotion, more appropriate characters can be generated.

[0092] The gaze tracking unit can measure the degree of focus of the user's gaze during gaze tracking and dynamically adjust the sensitivity of character selection according to the degree of focus. For example, the gaze tracking unit can increase the sensitivity of character selection when the user's gaze is focused. The gaze tracking unit can also decrease the sensitivity of character selection when the user's gaze is distracted. Furthermore, the gaze tracking unit can measure the degree of focus of the user's gaze in real time and adjust the sensitivity of character selection. In this way, by adjusting the sensitivity of character selection according to the degree of focus of the user's gaze, erroneous selections can be prevented and operation accuracy can be improved.

[0093] When generating characters, the character generation unit can generate characters that reflect the stroke order and writing habits of the user's handwritten characters. For example, the character generation unit generates characters that reflect the stroke order of the user's handwritten characters. The character generation unit can also generate characters that reflect the user's writing habits. Furthermore, the character generation unit learns the stroke order and writing habits of the user's handwritten characters and generates characters. In this way, by reflecting the stroke order and writing habits of the user's handwritten characters, more natural characters can be generated.

[0094] The eye tracking unit records the history of the user's eye movement during eye tracking, and can improve the accuracy of eye tracking based on the past data. For example, the eye tracking unit records the history of the user's eye movement and improves the accuracy of eye tracking. The eye tracking unit can also analyze the pattern of the user's eye movement and improve the accuracy of eye tracking. Furthermore, the eye tracking unit optimizes the eye tracking algorithm based on the history of the user's eye movement. This improves the accuracy of eye tracking based on the history of the user's eye movement, thereby improving the accuracy of operation.

[0095] When generating characters, the character generation unit can adjust the size and spacing of the user's handwritten characters to appropriately adjust the layout of the characters to be generated. For example, the character generation unit adjusts the size of the characters to be generated based on the size of the user's handwritten characters. The character generation unit can also adjust the spacing of the characters to be generated based on the spacing of the user's handwritten characters. Furthermore, the character generation unit learns the size and spacing of the user's handwritten characters and optimizes the layout of the characters to be generated. In this way, by adjusting the size and spacing of the user's handwritten characters, characters with a more appropriate layout can be generated.

[0096] The gaze tracking unit can estimate the user's emotion and dynamically adjust the order in which the gaze tracking results are displayed based on the estimated user emotion. For example, the gaze tracking unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The gaze tracking unit then calculates an emotion score based on changes in the facial expression. The gaze tracking unit can also record the user's voice and estimate the emotion using voice analysis technology. This allows important information to be displayed preferentially by adjusting the order in which the gaze tracking results are displayed based on the user's emotion.

[0097] The fluctuation adding unit can estimate the user's emotion and dynamically adjust the intensity of the fluctuation based on the estimated user's emotion. For example, the fluctuation adding unit can reduce the intensity of the fluctuation when the user is relaxed. Also, the fluctuation adding unit can increase the intensity of the fluctuation when the user is nervous. Furthermore, the fluctuation adding unit can return the intensity of the fluctuation to normal when the user is having fun. In this way, by adjusting the intensity of the fluctuation according to the user's emotion, more appropriate characters can be generated.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The eye-gaze tracking unit uses an eye-gaze input device to track the user's gaze and identify the selected character. Eye-gaze input devices include camera-based devices and infrared-based devices. The eye-gaze tracking unit uses a camera-based device to detect the movement of the user's pupils and identify the direction of the gaze. Alternatively, the eye-gaze tracking unit can use an infrared-based device to track the user's gaze. Step 2: The character generation unit analyzes the character information identified by the gaze tracking unit and generates characters based on the user's past handwriting style. The character generation unit uses a large-scale language model (LLM) to learn the user's past handwriting and generates characters based on that style. Step 3: The writing pressure applying unit applies writing pressure to the characters generated by the character generating unit. The writing pressure applying unit applies varying degrees of writing pressure to the generated characters, thereby giving the characters a three-dimensional appearance. Step 4: The fluctuation adding unit adds fluctuation to the characters generated by the character generating unit. The fluctuation adding unit adds fluctuation to the generated characters, thereby giving the characters a natural fluctuation.

[0100] 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.

[0101] 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> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0103] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0107] 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.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0109] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0114] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0116] 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.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 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.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0123] 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.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] 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.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0139] 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.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] 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 image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] 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.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive 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.

[0144] 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.

[0145] 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.

[0146] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] 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.

[0154] FIG. 9 illustrates 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 behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions 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.

[0155] 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.

[0156] 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).

[0157] 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 expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, 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 expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, 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.

[0158] 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."

[0159] 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.

[0160] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, 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. A processor also includes 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.

[0165] The hardware resource that executes the specific process 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 process may be a single processor.

[0166] 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.

[0167] 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.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] 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.

[0170] 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.

[0171] [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an eye-gaze tracking unit that tracks the user's gaze using an eye-gaze input device and identifies a selected character; a character generation unit that analyzes the character information identified by the gaze tracking unit and generates characters based on the user's past handwritten character style; a writing pressure applying unit that applies writing pressure to the characters generated by the character generating unit, and a fluctuation applying unit that applies fluctuation to the characters A system characterized by:

2. The gaze tracking unit Use the right eye tracking method for each type of eye tracking device 2. The system of claim 1.

3. The character generation unit Collecting training data to learn from the user's past handwriting 2. The system of claim 1.

4. The gaze tracking unit Estimate the user's emotions and dynamically adjust the accuracy of eye tracking based on the estimated user emotions.

2. The system of claim 1.

5. The gaze tracking unit When tracking the user's gaze, the speed and pattern of the user's eye movements are analyzed to provide an appropriate character selection method.

2. The system of claim 1.

6. The gaze tracking unit During eye tracking, the system measures the user's gaze concentration and dynamically adjusts the sensitivity of character selection according to the concentration level.

2. The system of claim 1.

7. The gaze tracking unit During eye tracking, the history of the user's eye movements is recorded and the accuracy of eye tracking is improved based on past data.

2. The system of claim 1.

8. The gaze tracking unit Estimate the user's emotion and dynamically adjust the order in which the eye tracking results are displayed based on the estimated emotion.

2. The system of claim 1.

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