System

Abstract

An object of a system according to an embodiment is to enable a user to easily create his / her own original stamp.SOLUTION: A system includes a picture capturing part, a voice capturing part, and a stamp generation part. The picture capturing unit captures a picture drawn by a user. The voice capturing unit captures a voice recorded by a user. The stamp generation unit generates a stamp based on the data captured by the picture capture unit and the sound capture unit.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] Conventional techniques have had the problem that it is difficult for users to easily create their own original stamps.

[0005] The system according to the embodiment aims to enable users to easily create their own original stamps. [Means for solving the problem]

[0006] The system according to the embodiment includes a picture capture unit, a voice capture unit, and a stamp generation unit. The picture capture unit captures a picture drawn by a user. The voice capture unit captures a voice recorded by the user. The stamp generation unit generates a stamp based on the data captured by the picture capture unit and the voice capture unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable a user to easily create his or her own original stamp. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The LINE stamp generation system according to the embodiment of the present invention is a system in which a generation AI generates stamps based on pictures drawn by a user or voice recordings. This allows users to easily create their own original stamps.

[0029] The LINE stamp generation system according to the embodiment includes a picture capture unit, a voice capture unit, and a stamp generation unit. The picture capture unit captures a picture drawn by a user. For example, a picture taken by a user with a smartphone is saved as digital data. The picture capture unit can also digitize a hand-drawn picture using a scanner. The picture capture unit can also directly capture digital art. For example, a picture taken with a smartphone is saved in JPEG format and input to the generation AI. A hand-drawn picture is scanned at high resolution using a scanner and converted into digital data. Digital art can be saved in PNG format and directly imported. The voice capture unit captures voices recorded by a user. For example, a voice recorded by a user with a smartphone is saved as digital data. The voice capture unit can also record voices in real time using a microphone. The voice capture unit can also capture voice memos. For example, a voice recorded with a smartphone is saved in MP3 format and input to the generation AI. Voice is recorded in real time using a microphone and converted into digital data. Voice memos can be saved in WAV format and imported. The stamp generation unit generates stamps based on the data captured by the picture capture unit and the voice capture unit. For example, the generation AI generates stamps by combining the captured picture and voice. The generation AI can also arrange the picture based on the intonation of the voice. Furthermore, the generation AI can select a message from a template collection and add it to the stamp. For example, the generation AI generates a smiling stamp based on the captured picture and voice. It changes the expression of the picture based on the intonation of the voice. It selects the message "Hello" from the template collection and adds it to the stamp. In this way, the LINE stamp generation system according to the embodiment allows users to easily create their own stamps. For example, by digitizing a picture drawn by a child, registering the voice, and generating a stamp, users can preserve memories in a tangible form. Furthermore, by utilizing the template collection, users can easily create stamps without having to think of a message.

[0030] The picture capture unit can automatically remove the background of the captured picture and extract only the characters and important elements. For example, when a user inputs a picture taken with a smartphone into the generation AI, the AI ​​automatically recognizes the background and extracts only the characters and important elements. For example, the wall and floor in the background of a picture drawn by a child can be removed, leaving only the character to be used as a stamp. Backgrounds include, for example, solid color backgrounds and patterned backgrounds. Characters include, for example, people, animals, and abstract shapes. Important elements include, for example, the center of the picture or specific colors or shapes. This makes it possible to automatically remove the background of a picture and extract only the characters and important elements.

[0031] The picture capture unit can analyze the style of the captured picture and convert it into another art style selected by the user. For example, the picture capture unit inputs a picture captured by the user into a generation AI, which then analyzes the style of the picture and converts it into another art style. For example, a child's drawing can be converted into an anime style or a watercolor painting style. Styles include, for example, art styles and design styles. Art styles include, for example, impressionism, abstract painting, and pop art. This allows the style of the picture to be analyzed and converted into another art style.

[0032] The picture capture unit saves the captured picture as a 3D model, making it usable not only as a stamp but also as a 3D animation. For example, the picture capture unit inputs a picture captured by a user into a generation AI, which then saves the picture as a 3D model. For example, a character drawn by a child can be converted into a 3D model and used as a 3D animation. 3D models include, for example, file formats and modeling technologies. 3D animation includes, for example, animation software and rendering technologies. This allows the captured picture to be saved as a 3D model and used as a 3D animation.

[0033] The picture capture unit can automatically animate parts of the captured picture to generate moving stamps. For example, the picture capture unit inputs a picture captured by a user into the generation AI, and the AI ​​automatically animates parts of the picture. For example, it generates an animation of moving the hands and feet of a character drawn by a child. Animation includes, for example, frame rate and movement pattern. Moving stamps include, for example, GIF animation and video formats. This makes it possible to automatically animate parts of the captured picture to generate moving stamps.

[0034] The voice capture unit can analyze the characteristics of the captured voice and automatically generate a character for the voice owner. For example, the voice capture unit inputs a user's recorded voice into the generation AI, which then analyzes the voice characteristics and automatically generates a character for the voice owner. For example, it can analyze a child's voice and generate a character that matches that voice. Characteristics include, for example, voice tone, pitch, and rhythm. This allows the characteristics of the captured voice to be analyzed and a character for the voice owner to be automatically generated.

[0035] The voice capture unit can analyze the mouth movements of the voice owner and add mouth movements to stamps. For example, the voice capture unit inputs a user's recorded voice into the generation AI, which then analyzes the mouth movements of the voice owner. For example, it can analyze a child's voice and add mouth movements that match that voice to the stamp. Mouth movements include, for example, opening and closing the mouth and lip movements. This allows the mouth movements of the voice owner to be analyzed and added to the stamp.

[0036] The voice capture unit can analyze the intonation of the voice and synchronize the movement of the illustration to the rhythm of the voice. For example, the voice capture unit inputs the user's recorded voice into the generation AI, which then analyzes the intonation of the voice and synchronizes the movement of the illustration to the rhythm of the voice. For example, the voice capture unit generates a character dancing to the rhythm of a child's voice. The rhythm includes, for example, the tempo, beat, and rhythm pattern of the sound. This allows the movement of the illustration to be synchronized to the rhythm of the voice.

[0037] The stamp generation unit can combine multiple pictures and voices to generate stamps in a continuous story format. For example, the stamp generation unit inputs multiple pictures and voices captured by a user into the generation AI, which then combines them to generate stamps in a continuous story format. For example, a story of a character's adventure is generated based on pictures drawn by a child and recorded voice. The story format includes, for example, a series of scenes or a narrative structure. This makes it possible to generate stamps in a continuous story format by combining multiple pictures and voices.

[0038] The stamp generation unit can analyze the user's past stamp usage history and suggest new stamps based on the most frequently used style and theme. For example, the stamp generation unit inputs the user's past stamp usage history into a generation AI, which then analyzes the data and suggests new stamps based on the most frequently used style and theme. For example, a new greeting stamp can be suggested based on the user's frequently used greeting stamps. The stamp usage history includes, for example, frequency of use and timing of use. The style includes, for example, design style and art style. The theme includes, for example, seasonal themes and event themes. This allows the user's past stamp usage history to be analyzed and new stamps to be suggested based on the most frequently used style and theme.

[0039] The stamp generation unit can also generate stamps in GIF or video format, enabling more dynamic expressions. For example, the stamp generation unit inputs a picture captured by a user into the generation AI, and the AI ​​generates stamps in GIF or video format based on that picture. For example, a GIF stamp of a moving character drawn by a child can be generated. The GIF format includes, for example, the frame rate, number of colors, and animation length. The video format includes, for example, the file format, resolution, and frame rate. This allows stamps to be generated in GIF or video format, enabling more dynamic expressions.

[0040] The stamp generation unit can add interactive elements to stamps and generate stamps that change when the user taps. For example, the stamp generation unit inputs a picture captured by the user into the generation AI, and the AI ​​adds interactive elements to the picture. For example, a stamp is generated in which a character drawn by a child changes expression when tapped. Interactive elements include, for example, tapping, swiping, and clicking. This allows for adding interactive elements to stamps and generating stamps that change when the user taps.

[0041] The stamp generation unit can analyze the user's past message history and generate messages based on the most frequently used phrases and words. For example, the stamp generation unit inputs the user's past message history into a generation AI, which then analyzes the data and generates messages based on the most frequently used phrases and words. For example, a new greeting message can be generated based on greeting phrases frequently used by the user. The message history includes, for example, the content of past messages and the date and time they were sent. Phrases include, for example, frequently used expressions and standard phrases. Words include, for example, single words and short sentences. This allows the user's past message history to be analyzed and messages can be generated based on the most frequently used phrases and words.

[0042] The stamp generation unit can understand the context of a message and automatically suggest appropriate emojis and stamps. For example, the stamp generation unit inputs a message entered by a user into the generation AI, and the AI ​​understands the context and automatically suggests appropriate emojis and stamps. For example, a smiling emoji is suggested for a greeting message. Context includes, for example, the surrounding sentences and related topics. Emojis include, for example, emojis that express emotions and emojis that represent actions. This makes it possible to understand the context of a message and automatically suggest appropriate emojis and stamps.

[0043] The stamp generation unit can automatically translate into different languages ​​and generate messages that support international communication. For example, the stamp generation unit inputs a message entered by a user into the generation AI, which then automatically translates the message into a different language. For example, a Japanese message can be translated into English to support international communication. Languages ​​include, for example, English, Japanese, and Spanish. International communication includes, for example, multilingual support and cultural considerations. This makes it possible to generate messages that can automatically translate into different languages ​​and support international communication.

[0044] The stamp generation unit can add voice synthesis to a message and generate a stamp with voice. For example, the stamp generation unit inputs a message entered by a user into the generation AI, and the AI ​​adds voice synthesis to the message. For example, voice synthesis is added to a greeting message to generate a stamp with voice. Voice synthesis includes, for example, text-to-speech and adding voice effects. This allows voice synthesis to be added to a message and a stamp with voice to be generated.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The picture capture unit can analyze the style of the captured picture and convert it into another art style selected by the user. For example, a user can input a picture captured by the user into a generation AI, which then analyzes the style of the picture and converts it into another art style. For example, a child's drawing can be converted into an anime style or watercolor style. Styles include, for example, art styles and design styles. Art styles include, for example, impressionism, abstract painting, and pop art. This allows the style of the picture to be analyzed and converted into another art style.

[0047] The picture capture unit can automatically animate parts of the captured picture to generate moving stamps. For example, a user can input a captured picture into the generation AI, and the AI ​​will automatically animate parts of the picture. For example, it can generate an animation of moving the hands and feet of a character drawn by a child. Animation includes, for example, frame rate and movement pattern. Moving stamps include, for example, GIF animation and video formats. This allows parts of the captured picture to be automatically animated to generate moving stamps.

[0048] The voice capture unit can analyze the characteristics of the captured voice and automatically generate a character for the voice owner. For example, a user can input their recorded voice into the generation AI, which then analyzes the voice characteristics and automatically generates a character for the voice owner. For example, a child's voice can be analyzed and a character that matches that voice can be generated. Characteristics include, for example, tone, pitch, and rhythm of the voice. This allows the characteristics of the captured voice to be analyzed and a character for the voice owner to be automatically generated.

[0049] The stamp generation unit can combine multiple pictures and voices to generate stamps in a continuous story format. For example, a user can input multiple pictures and voices into the generation AI, which then combines them to generate stamps in a continuous story format. For example, a story of a character's adventure can be generated based on a picture drawn by a child and a recorded voice. The story format can include, for example, a series of scenes or a narrative structure. This makes it possible to generate stamps in a continuous story format by combining multiple pictures and voices.

[0050] The stamp generation unit can understand the context of a message and automatically suggest appropriate emojis and stamps. For example, a user inputs a message into the generation AI, and the AI ​​understands the context and automatically suggests appropriate emojis and stamps. For example, a smiling emoji may be suggested for a greeting message. Context includes, for example, the surrounding sentences and related topics. Emojis include, for example, emojis that express emotions and emojis that represent actions. This allows the context of a message to be understood and appropriate emojis and stamps to be automatically suggested.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The picture capture unit captures a picture drawn by the user. For example, a picture taken by the user with a smartphone is saved as digital data. The picture capture unit can also digitize a hand-drawn picture using a scanner. The picture capture unit can also directly capture digital art. For example, a picture taken with a smartphone is saved in JPEG format and input into the generation AI. A hand-drawn picture is scanned at high resolution using a scanner and converted into digital data. Digital art can be saved in PNG format and directly captured. Step 2: The voice capture unit captures the voice recorded by the user. For example, the voice recorded by the user on a smartphone is saved as digital data. The voice capture unit can also record voice in real time using a microphone. The voice capture unit can also capture voice memos. For example, the voice recorded on a smartphone is saved in MP3 format and input into the generation AI. Voice is recorded in real time using a microphone and converted into digital data. Voice memos can be saved in WAV format and imported. Step 3: The stamp generation unit generates stamps based on the data captured by the picture capture unit and voice capture unit. For example, the generation AI generates stamps by combining the captured pictures and voice. The generation AI can also arrange the pictures based on the intonation of the voice. Furthermore, the generation AI can select a message from a template collection and add it to the stamp. For example, the generation AI generates a smiling stamp based on the captured pictures and voice. It changes the expression of the picture based on the intonation of the voice. It selects the message "Hello" from the template collection and adds it to the stamp.

[0053] (Example 2) The LINE stamp generation system according to the embodiment of the present invention is a system in which a generation AI generates stamps based on pictures drawn by a user or voice recordings. This allows users to easily create their own original stamps.

[0054] The LINE stamp generation system according to the embodiment includes a picture capture unit, a voice capture unit, and a stamp generation unit. The picture capture unit captures a picture drawn by a user. For example, a picture taken by a user with a smartphone is saved as digital data. The picture capture unit can also digitize a hand-drawn picture using a scanner. The picture capture unit can also directly capture digital art. For example, a picture taken with a smartphone is saved in JPEG format and input to the generation AI. A hand-drawn picture is scanned at high resolution using a scanner and converted into digital data. Digital art can be saved in PNG format and directly imported. The voice capture unit captures voices recorded by a user. For example, a voice recorded by a user with a smartphone is saved as digital data. The voice capture unit can also record voices in real time using a microphone. The voice capture unit can also capture voice memos. For example, a voice recorded with a smartphone is saved in MP3 format and input to the generation AI. Voice is recorded in real time using a microphone and converted into digital data. Voice memos can be saved in WAV format and imported. The stamp generation unit generates stamps based on the data captured by the picture capture unit and the voice capture unit. For example, the generation AI generates stamps by combining the captured picture and voice. The generation AI can also arrange the picture based on the intonation of the voice. Furthermore, the generation AI can select a message from a template collection and add it to the stamp. For example, the generation AI generates a smiling stamp based on the captured picture and voice. It changes the expression of the picture based on the intonation of the voice. It selects the message "Hello" from the template collection and adds it to the stamp. In this way, the LINE stamp generation system according to the embodiment allows users to easily create their own stamps. For example, by digitizing a picture drawn by a child, registering the voice, and generating a stamp, users can preserve memories in a tangible form. Furthermore, by utilizing the template collection, users can easily create stamps without having to think of a message.

[0055] The picture capture unit can automatically remove the background of the captured picture and extract only the characters and important elements. For example, when a user inputs a picture taken with a smartphone into the generation AI, the AI ​​automatically recognizes the background and extracts only the characters and important elements. For example, the wall and floor in the background of a picture drawn by a child can be removed, leaving only the character to be used as a stamp. Backgrounds include, for example, solid color backgrounds and patterned backgrounds. Characters include, for example, people, animals, and abstract shapes. Important elements include, for example, the center of the picture or specific colors or shapes. This makes it possible to automatically remove the background of a picture and extract only the characters and important elements.

[0056] The picture capture unit can analyze the style of the captured picture and convert it into another art style selected by the user. For example, the picture capture unit inputs a picture captured by the user into a generation AI, which then analyzes the style of the picture and converts it into another art style. For example, a child's drawing can be converted into an anime style or a watercolor painting style. Styles include, for example, art styles and design styles. Art styles include, for example, impressionism, abstract painting, and pop art. This allows the style of the picture to be analyzed and converted into another art style.

[0057] The picture capture unit can use an emotion estimation function to estimate the emotion of the person who drew the picture and automatically adjust the color tone and design of the picture based on that emotion. For example, the picture capture unit inputs a picture captured by a user into the generation AI, and the AI ​​uses the emotion estimation function to estimate the emotion of the person who drew the picture. For example, if the emotion of joy is strong, the color tone of the picture is adjusted to be brighter. The emotion estimation function uses technologies such as facial expression recognition and voice analysis. Emotions include, for example, joy, sadness, and anger. Color tone includes, for example, hue, saturation, and brightness. Design includes, for example, layout, font, and decoration. This makes it possible to automatically adjust the color tone and design of the picture based on the emotion of the person who drew the picture.

[0058] The picture capture unit saves the captured picture as a 3D model, making it usable not only as a stamp but also as a 3D animation. For example, the picture capture unit inputs a picture captured by a user into a generation AI, which then saves the picture as a 3D model. For example, a character drawn by a child can be converted into a 3D model and used as a 3D animation. 3D models include, for example, file formats and modeling technologies. 3D animation includes, for example, animation software and rendering technologies. This allows the captured picture to be saved as a 3D model and used as a 3D animation.

[0059] The picture capture unit can automatically animate parts of the captured picture to generate moving stamps. For example, the picture capture unit inputs a picture captured by a user into the generation AI, and the AI ​​automatically animates parts of the picture. For example, it generates an animation of moving the hands and feet of a character drawn by a child. Animation includes, for example, frame rate and movement pattern. Moving stamps include, for example, GIF animation and video formats. This makes it possible to automatically animate parts of the captured picture to generate moving stamps.

[0060] The picture capture unit can use the emotion estimation function to generate an animation that moves part of a picture based on the emotion of the person who drew the picture. For example, the picture capture unit inputs a picture captured by a user into the generation AI, and the AI ​​uses the emotion estimation function to estimate the emotion of the person who drew the picture and generates an animation that moves part of the picture based on that emotion. For example, if the emotion of joy is strong, an animation of a character jumping is generated. The movement includes, for example, the type of animation and the movement pattern. This makes it possible to generate an animation that moves part of a picture based on the emotion of the person who drew the picture.

[0061] The voice capture unit can analyze the characteristics of the captured voice and automatically generate a character for the voice owner. For example, the voice capture unit inputs a user's recorded voice into the generation AI, which then analyzes the voice characteristics and automatically generates a character for the voice owner. For example, it can analyze a child's voice and generate a character that matches that voice. Characteristics include, for example, voice tone, pitch, and rhythm. This allows the characteristics of the captured voice to be analyzed and a character for the voice owner to be automatically generated.

[0062] The voice capture unit can analyze the intonation of the voice, estimate the emotion of the voice, and adjust the facial expression and movement of the illustration based on that emotion. For example, the voice capture unit inputs a user's recorded voice into the generation AI, and the AI ​​analyzes the intonation of the voice to estimate the emotion of the voice. For example, if the emotion of joy is strong, the facial expression of the character in the illustration can be adjusted to a smile. Intonation includes, for example, pitch, volume, and rhythm. Facial expression includes, for example, the movement of facial parts and the type of expression. Movement includes, for example, animation patterns and movement speed. This makes it possible to analyze the intonation of the voice and adjust the facial expression and movement of the illustration based on the emotion of the voice.

[0063] The voice capture unit can use an emotion estimation function to analyze the emotion in the voice and change the background and color tone of the picture based on that emotion. For example, the voice capture unit inputs a user's recorded voice into the generation AI, which then analyzes the emotion in the voice using the emotion estimation function. For example, if the emotion of joy is strong, the background of the picture is changed to a bright color. The background can include, for example, background colors, patterns, images, etc. This allows the background and color tone of the picture to be changed based on the emotion in the voice.

[0064] The voice capture unit can analyze the mouth movements of the voice owner and add mouth movements to stamps. For example, the voice capture unit inputs a user's recorded voice into the generation AI, which then analyzes the mouth movements of the voice owner. For example, it can analyze a child's voice and add mouth movements that match that voice to the stamp. Mouth movements include, for example, opening and closing the mouth and lip movements. This allows the mouth movements of the voice owner to be analyzed and added to the stamp.

[0065] The voice capture unit can analyze the intonation of the voice and synchronize the movement of the illustration to the rhythm of the voice. For example, the voice capture unit inputs the user's recorded voice into the generation AI, which then analyzes the intonation of the voice and synchronizes the movement of the illustration to the rhythm of the voice. For example, the voice capture unit generates a character dancing to the rhythm of a child's voice. The rhythm includes, for example, the tempo, beat, and rhythm pattern of the sound. This allows the movement of the illustration to be synchronized to the rhythm of the voice.

[0066] The voice capture unit can add voice effects to stickers based on the emotion of the voice using the emotion estimation function. For example, the voice capture unit inputs a user's recorded voice into the generation AI, and the AI ​​analyzes the emotion of the voice using the emotion estimation function. For example, if the emotion of joy is strong, a bright voice effect is added to the sticker. Voice effects include, for example, echo, reverb, and distortion. This allows voice effects to be added to stickers based on the emotion of the voice.

[0067] The stamp generation unit can combine multiple pictures and voices to generate stamps in a continuous story format. For example, the stamp generation unit inputs multiple pictures and voices captured by a user into the generation AI, which then combines them to generate stamps in a continuous story format. For example, a story of a character's adventure is generated based on pictures drawn by a child and recorded voice. The story format includes, for example, a series of scenes or a narrative structure. This makes it possible to generate stamps in a continuous story format by combining multiple pictures and voices.

[0068] The stamp generation unit can analyze the user's past stamp usage history and suggest new stamps based on the most frequently used style and theme. For example, the stamp generation unit inputs the user's past stamp usage history into a generation AI, which then analyzes the data and suggests new stamps based on the most frequently used style and theme. For example, a new greeting stamp can be suggested based on the user's frequently used greeting stamps. The stamp usage history includes, for example, frequency of use and timing of use. The style includes, for example, design style and art style. The theme includes, for example, seasonal themes and event themes. This allows the user's past stamp usage history to be analyzed and new stamps to be suggested based on the most frequently used style and theme.

[0069] The stamp generation unit can customize stamp designs and messages based on the user's emotions using the emotion estimation function. For example, when a user creates a stamp, the generation AI uses the emotion estimation function to analyze the user's emotions and customizes the stamp design and message based on those emotions. For example, if the emotion of joy is strong, a bright design and a positive message are added. The design includes, for example, layout, font, decoration, etc. The message includes, for example, a text message or voice message. This allows the stamp design and message to be customized based on the user's emotions.

[0070] The stamp generation unit can also generate stamps in GIF or video format, enabling more dynamic expressions. For example, the stamp generation unit inputs a picture captured by a user into the generation AI, and the AI ​​generates stamps in GIF or video format based on that picture. For example, a GIF stamp of a moving character drawn by a child can be generated. The GIF format includes, for example, the frame rate, number of colors, and animation length. The video format includes, for example, the file format, resolution, and frame rate. This allows stamps to be generated in GIF or video format, enabling more dynamic expressions.

[0071] The stamp generation unit can add interactive elements to stamps and generate stamps that change when the user taps. For example, the stamp generation unit inputs a picture captured by the user into the generation AI, and the AI ​​adds interactive elements to the picture. For example, a stamp is generated in which a character drawn by a child changes expression when tapped. Interactive elements include, for example, tapping, swiping, and clicking. This allows for adding interactive elements to stamps and generating stamps that change when the user taps.

[0072] The stamp generation unit can change the movement and effects of stamps in real time based on the user's emotions using the emotion estimation function. For example, when a user creates a stamp, the generation AI uses the emotion estimation function to analyze the user's emotions and changes the movement and effects of the stamp in real time based on that emotion. For example, if the emotion of joy is strong, the stamp's movement is made faster and a bright effect is added. Examples of movement include animation patterns and movement speed. Examples of effects include visual effects and audio effects. This allows the movement and effects of stamps to be changed in real time based on the user's emotions.

[0073] The stamp generation unit can analyze the user's past message history and generate messages based on the most frequently used phrases and words. For example, the stamp generation unit inputs the user's past message history into a generation AI, which then analyzes the data and generates messages based on the most frequently used phrases and words. For example, a new greeting message can be generated based on greeting phrases frequently used by the user. The message history includes, for example, the content of past messages and the date and time they were sent. Phrases include, for example, frequently used expressions and standard phrases. Words include, for example, single words and short sentences. This allows the user's past message history to be analyzed and messages can be generated based on the most frequently used phrases and words.

[0074] The stamp generation unit can understand the context of a message and automatically suggest appropriate emojis and stamps. For example, the stamp generation unit inputs a message entered by a user into the generation AI, and the AI ​​understands the context and automatically suggests appropriate emojis and stamps. For example, a smiling emoji is suggested for a greeting message. Context includes, for example, the surrounding sentences and related topics. Emojis include, for example, emojis that express emotions and emojis that represent actions. This makes it possible to understand the context of a message and automatically suggest appropriate emojis and stamps.

[0075] The stamp generation unit can adjust the tone and content of the message based on the user's emotions using the emotion estimation function. For example, the stamp generation unit inputs a message entered by the user into the generation AI, which then analyzes the user's emotions using the emotion estimation function and adjusts the tone and content of the message based on those emotions. For example, if the emotion of joy is strong, the message is adjusted to have a positive tone. Examples of the tone include formal tones and casual tones. Examples of the content include the subject of the message and detailed information. This allows the tone and content of the message to be adjusted based on the user's emotions.

[0076] The stamp generation unit can automatically translate into different languages ​​and generate messages that support international communication. For example, the stamp generation unit inputs a message entered by a user into the generation AI, which then automatically translates the message into a different language. For example, a Japanese message can be translated into English to support international communication. Languages ​​include, for example, English, Japanese, and Spanish. International communication includes, for example, multilingual support and cultural considerations. This makes it possible to generate messages that can automatically translate into different languages ​​and support international communication.

[0077] The stamp generation unit can add voice synthesis to a message and generate a stamp with voice. For example, the stamp generation unit inputs a message entered by a user into the generation AI, and the AI ​​adds voice synthesis to the message. For example, voice synthesis is added to a greeting message to generate a stamp with voice. Voice synthesis includes, for example, text-to-speech and adding voice effects. This allows voice synthesis to be added to a message and a stamp with voice to be generated.

[0078] The stamp generation unit can use the emotion estimation function to change the content and expression of the message in real time based on the user's emotions. For example, the stamp generation unit inputs a message entered by the user into the generation AI, which then uses the emotion estimation function to analyze the user's emotions and changes the content and expression of the message in real time based on those emotions. For example, if the emotion of joy is strong, the message will be changed to one with more positive content. The content includes, for example, the subject of the message and detailed information. The expression includes, for example, the wording, style, and tone. This allows the content and expression of the message to be changed in real time based on the user's emotions.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The picture capture unit can analyze the style of the captured picture and convert it into another art style selected by the user. For example, a user can input a picture captured by the user into a generation AI, which then analyzes the style of the picture and converts it into another art style. For example, a child's drawing can be converted into an anime style or watercolor style. Styles include, for example, art styles and design styles. Art styles include, for example, impressionism, abstract painting, and pop art. This allows the style of the picture to be analyzed and converted into another art style.

[0081] The picture capture unit can automatically animate parts of the captured picture to generate moving stamps. For example, a user can input a captured picture into the generation AI, and the AI ​​will automatically animate parts of the picture. For example, it can generate an animation of moving the hands and feet of a character drawn by a child. Animation includes, for example, frame rate and movement pattern. Moving stamps include, for example, GIF animation and video formats. This allows parts of the captured picture to be automatically animated to generate moving stamps.

[0082] The voice capture unit can analyze the characteristics of the captured voice and automatically generate a character for the voice owner. For example, a user can input their recorded voice into the generation AI, which then analyzes the voice characteristics and automatically generates a character for the voice owner. For example, a child's voice can be analyzed and a character that matches that voice can be generated. Characteristics include, for example, tone, pitch, and rhythm of the voice. This allows the characteristics of the captured voice to be analyzed and a character for the voice owner to be automatically generated.

[0083] The stamp generation unit can combine multiple pictures and voices to generate stamps in a continuous story format. For example, a user can input multiple pictures and voices into the generation AI, which then combines them to generate stamps in a continuous story format. For example, a story of a character's adventure can be generated based on a picture drawn by a child and a recorded voice. The story format can include, for example, a series of scenes or a narrative structure. This makes it possible to generate stamps in a continuous story format by combining multiple pictures and voices.

[0084] The stamp generation unit can understand the context of a message and automatically suggest appropriate emojis and stamps. For example, a user inputs a message into the generation AI, and the AI ​​understands the context and automatically suggests appropriate emojis and stamps. For example, a smiling emoji may be suggested for a greeting message. Context includes, for example, the surrounding sentences and related topics. Emojis include, for example, emojis that express emotions and emojis that represent actions. This allows the context of a message to be understood and appropriate emojis and stamps to be automatically suggested.

[0085] The picture capture unit can use an emotion estimation function to estimate the emotion of the person who drew the picture and automatically adjust the color tone and design of the picture based on that emotion. For example, a user inputs a captured picture into the generation AI, and the AI ​​uses the emotion estimation function to estimate the emotion of the person who drew the picture. For example, if the emotion of joy is strong, the color tone of the picture is adjusted to be brighter. The emotion estimation function uses technologies such as facial expression recognition and voice analysis. Emotions include, for example, joy, sadness, and anger. Color tone includes, for example, hue, saturation, and brightness. Design includes, for example, layout, font, and decoration. This makes it possible to automatically adjust the color tone and design of the picture based on the emotion of the person who drew the picture.

[0086] The picture capture unit can use the emotion estimation function to generate animations that move parts of a picture based on the emotion of the person who drew the picture. For example, a user inputs a picture into the generation AI, and the AI ​​uses the emotion estimation function to estimate the emotion of the person who drew the picture and generates animations that move parts of the picture based on that emotion. For example, if the emotion of joy is strong, an animation of a character jumping is generated. The movement includes, for example, the type of animation and the movement pattern. This makes it possible to generate animations that move parts of a picture based on the emotion of the person who drew the picture.

[0087] The voice capture unit can analyze the intonation of the voice, estimate the emotion of the voice, and adjust the facial expression and movement of the illustration based on that emotion. For example, a user inputs their recorded voice into the generation AI, and the AI ​​analyzes the intonation of the voice to estimate the emotion of the voice. For example, if the emotion of joy is strong, the facial expression of the character in the illustration can be adjusted to a smile. Intonation includes, for example, pitch, volume, and rhythm. Facial expression includes, for example, the movement of facial parts and the type of expression. Movement includes, for example, animation patterns and speed of movement. This makes it possible to analyze the intonation of the voice and adjust the facial expression and movement of the illustration based on the emotion of the voice.

[0088] The voice capture unit can use an emotion estimation function to analyze the emotion in the voice and change the background and color tone of the picture based on that emotion. For example, a user can input their recorded voice into the generation AI, which then uses the emotion estimation function to analyze the emotion in the voice. For example, if the emotion of joy is strong, the background of the picture can be changed to a bright color. The background can include, for example, background colors, patterns, and images. This allows the background and color tone of the picture to be changed based on the emotion in the voice.

[0089] The stamp generation unit can customize stamp designs and messages based on the user's emotions using the emotion estimation function. For example, when a user creates a stamp, the generation AI analyzes the user's emotions using the emotion estimation function and customizes the stamp design and message based on those emotions. For example, if the emotion of joy is strong, a bright design and a positive message are added. Designs include, for example, layout, font, and decoration. Messages include, for example, text messages and voice messages. This allows stamp designs and messages to be customized based on the user's emotions.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The picture capture unit captures a picture drawn by the user. For example, a picture taken by the user with a smartphone is saved as digital data. The picture capture unit can also digitize a hand-drawn picture using a scanner. The picture capture unit can also directly capture digital art. For example, a picture taken with a smartphone is saved in JPEG format and input into the generation AI. A hand-drawn picture is scanned at high resolution using a scanner and converted into digital data. Digital art can be saved in PNG format and directly captured. Step 2: The voice capture unit captures the voice recorded by the user. For example, the voice recorded by the user on a smartphone is saved as digital data. The voice capture unit can also record voice in real time using a microphone. The voice capture unit can also capture voice memos. For example, the voice recorded on a smartphone is saved in MP3 format and input into the generation AI. Voice is recorded in real time using a microphone and converted into digital data. Voice memos can be saved in WAV format and imported. Step 3: The stamp generation unit generates stamps based on the data captured by the picture capture unit and voice capture unit. For example, the generation AI generates stamps by combining the captured pictures and voice. The generation AI can also arrange the pictures based on the intonation of the voice. Furthermore, the generation AI can select a message from a template collection and add it to the stamp. For example, the generation AI generates a smiling stamp based on the captured pictures and voice. It changes the expression of the picture based on the intonation of the voice. It selects the message "Hello" from the template collection and adds it to the stamp.

[0092] 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.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> 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.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0105] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0106] 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.

[0107] 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.

[0108] 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 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.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0118] 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.

[0119] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0121] 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.

[0122] 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.

[0123] 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 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.

[0124] 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0136] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0137] 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.

[0138] 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.

[0139] 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 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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 area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0158] 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. [Explanation of symbols]

[0159] 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. a picture capture unit that captures a picture drawn by a user; a voice capture unit that captures a voice recorded by a user; a stamp generation unit that generates a stamp based on the data captured by the picture capture unit and the voice capture unit. A system characterized by:

2. The picture capture unit Automatically remove backgrounds from captured images and extract only characters and important elements 2. The system of claim 1.

3. The picture capture unit Save the captured image as a 3D model and use it not only as a stamp but also as a 3D animation.

2. The system of claim 1.

4. The voice capture unit Analyzes the characteristics of the captured voice and automatically generates the character of the voice owner 2. The system of claim 1.

5. The stamp generation unit A plurality of the pictures and the voices are combined to generate the stamps in a continuous story format.

2. The system of claim 1.

6. The picture capture unit Using emotion estimation functionality, the artist's emotions are estimated, and the color tone and design of the painting are automatically adjusted based on those emotions.

2. The system of claim 1.

7. The voice capture unit Analyzes the intonation of the voice, estimates the emotion of the voice, and adjusts the facial expression and movement of the picture based on that emotion.

2. The system of claim 1.

8. The stamp generation unit Customize the stamp design and message based on the user's emotions using an emotion estimation function.

2. The system of claim 1.

Images