system

Abstract

Provide a system. 【Solution means】 Means for receiving user input data, Means for generating an image based on the received data, Means for saving the generated image, Means for displaying the saved image, Means for receiving messages by voice or text from the user, Means for generating a response based on the received message, Means for converting the generated response into voice, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional character generation and operation systems have been difficult to flexibly provide the appearance and dialogue functions of characters according to user wishes. In particular, it has been insufficient in performance to quickly and accurately generate a character reflecting specific appearance requirements of the user and further enable the character to conduct dialogue in a natural form. In such a situation, in order to improve the user experience, there is a need for a system that generates an ideal character based on user input data and gives the character a dialogue function.

Means for Solving the Problems

[0005] To solve the above problems, this invention provides the following means: a means for receiving user input data and a means for generating an image based on the received data. Furthermore, it provides a means for saving the generated image and a means for displaying the saved image. In addition, it provides a means for receiving voice or text messages from the user, a means for generating a response based on the received message, and a means for converting the generated response into voice. In this way, it becomes possible to quickly create the appearance of a character according to the user's requests and to give that character natural conversational capabilities.

[0006] "Input data" refers to information that the user provides to the system, including specific details about the character's appearance and other preferences.

[0007] "Receiving means" refers to the mechanism or method by which a system receives input data or messages.

[0008] "Image generation means" refers to artificial intelligence or software processes used to create character images based on received data.

[0009] "Storage means" refers to mechanisms and methods for storing generated images in a database or storage device.

[0010] "Display means" refers to a mechanism or interface for visually showing saved images to the user.

[0011] "Message receiving means" refers to the mechanism or method by which a system receives voice or text messages from a user.

[0012] "Response generation means" refers to artificial intelligence or software processes used to generate an appropriate response based on a received message.

[0013] "Voice conversion means" refers to speech synthesis technology or software processes used to convert the generated response into speech. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the 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.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This system generates an ideal character based on user requests and gives that character the ability to converse. The system's operation will be explained in detail, along with concrete examples.

[0036] User request input

[0037] Users use their devices to enter details about the character's appearance. This input is done through a web form, where attribute information such as "hair color," "eye color," and "clothing" is entered. For example, a user might enter a request such as "a woman with long blonde hair, blue eyes, and casual clothing."

[0038] Sending input data

[0039] The device sends user input data to the server. The data is sent using an AJAX request and received at the server-side API endpoint.

[0040] Illustration generation

[0041] The server analyzes the received data and provides it to an AI illustration generation model to generate character illustrations. This model uses a high-performance machine learning algorithm to generate character images based on the user's requests.

[0042] Saving the generated illustration

[0043] The generated character illustrations are saved by the server to a database or cloud storage. This saving process generates a URL for the character image, which is then returned to the user.

[0044] AR / VR display

[0045] The device uses the received image URL to display the character in AR or VR. Users can view and interact with the character through this display. For example, a user can use their smartphone to display the character in AR mode.

[0046] The user speaks to the character.

[0047] Users use the chat window on their device to send voice or text messages to the character. For example, they might type a question like, "How are you today?"

[0048] Response generation by generative AI

[0049] The server receives user messages and passes them to a generative AI to generate appropriate responses. This AI model is designed to understand the context of the conversation and produce natural-sounding dialogue.

[0050] Receiving and playing back audio

[0051] The generated response is synthesized into speech on the server and sent to the user's device as an audio file. The device plays this audio file, allowing the user to hear the character's response aloud. For example, a response such as "Today is a good day! Thank you!" might be played aloud.

[0052] This system is designed to quickly generate a character of the user's choice and allow for natural conversations with that character. Furthermore, by utilizing generative AI, the system is designed to make the character's responses more human-like and natural.

[0053] The following describes the processing flow.

[0054] Step 1:

[0055] The user uses their device to enter details about the character's appearance. The input fields provide a form where users can enter attributes such as "hair color," "eye color," and "clothing." The user fills in the desired attributes in these fields and clicks the "Submit" button.

[0056] Step 2:

[0057] The device collects user input data and sends it to the server. An AJAX request is used to send the input data to the server-side API endpoint. The data is sent as pairs of form field names and their values.

[0058] Step 3:

[0059] The server analyzes the received data and inputs it into an AI illustration generation model. The model uses advanced machine learning algorithms to generate character images based on user specifications. For example, it generates character images that conform to specifications such as "long blonde hair," "blue eyes," and "casual clothing."

[0060] Step 4:

[0061] The server saves the generated character illustration to a database or cloud storage. Once the saving process is complete, a URL for the generated image is issued. This URL is returned to the user, who can then use it to view the image later.

[0062] Step 5:

[0063] The device displays a character using an image URL it receives. The character is visually displayed using AR or VR technology, and the user can view this displayed character. A chat window also appears, allowing the user to talk to the character.

[0064] Step 6:

[0065] The user enters a message using the chat window on their device. The user types a voice or text message to the character and clicks the "Send" button. For example, they can type a question like, "How are you today?"

[0066] Step 7:

[0067] The terminal sends the entered message to the server. The sent message is received and parsed at the server's API endpoint. This received data is then passed to the generative AI in the next step.

[0068] Step 8:

[0069] The server passes the received message to the generative AI, which generates an appropriate response. The AI ​​generates an appropriate answer in text format to the user's question. For example, it might generate a response like, "Today is a good day! Thank you!" in text format.

[0070] Step 9:

[0071] The server inputs the generated response into the speech synthesis system and generates an audio file. The speech synthesis system creates natural-sounding speech based on the generated text. This audio file is then sent to the user in the next step.

[0072] Step 10:

[0073] The device receives an audio file generated from the server. The audio file is played on the user's device, and the user can hear the character's response in audio. For example, the audio might say, "Today is a good day! Thank you!"

[0074] The above is the specific processing flow of this system. This allows users to generate their desired character and engage in natural conversations with that character.

[0075] (Example 1)

[0076] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0077] Conventional character generation systems have struggled to quickly generate the ideal character desired by the user and to engage in natural dialogue. Furthermore, the lack of functionality to display the generated character in augmented reality (AR) or virtual reality (VR) resulted in a limited user experience. Additionally, the generation of responses to voice or text messages from the user was often unnatural, resulting in a poor dialogue experience.

[0078] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0079] In this invention, the server includes means for receiving user input data, means for generating an image based on the received data, means for storing the generated image, means for displaying the generated image in augmented reality or virtual reality, means for receiving voice or text messages from the user, means for generating a response based on the received message, and means for converting the generated response into voice. This enables the user to quickly generate an ideal character and engage in natural conversation with the character displayed in augmented reality or virtual reality.

[0080] "Means for receiving user input data" refers to a mechanism for sending information entered by a user via a terminal to a server, and for the server to receive that information.

[0081] "Means for generating images based on received data" refers to a mechanism that uses machine learning to generate character images based on user input data received.

[0082] "Means for saving generated images" refers to a mechanism for saving generated character images to a database or cloud storage.

[0083] "Means for displaying saved images" refers to a mechanism for displaying images on a device using the URL of the saved image.

[0084] "Means for receiving voice or text messages from users" refers to a mechanism for a server to receive voice or text messages sent by a user via a terminal.

[0085] "Means for generating a response based on a received message" refers to a mechanism for analyzing a received message and generating an appropriate response.

[0086] "Means for converting generated responses into speech" refers to a mechanism for converting generated text responses into speech data using speech synthesis technology.

[0087] "Means for displaying generated images in augmented reality or virtual reality" refers to a mechanism for displaying generated character images in real space or virtual space using AR or VR technology.

[0088] This invention is a system that generates an ideal character based on user requests and gives that character interactive capabilities. The user inputs details about the character's appearance using a terminal, and the character is generated based on this input information. The specific operation of this system is described below.

[0089] The system will be implemented using the following key hardware and software:

[0090] Hardware: User terminals (PCs and smartphones), and servers that provide image generation and interaction functions.

[0091] Software: Web forms, AJAX requests, AI illustration generation models, generative AI models, speech synthesis technology, databases, cloud storage

[0092] The specific steps are as follows:

[0093] User request input

[0094] Users enter details about their character's appearance using a web form on their device. For example, information such as "Hair color: Blonde," "Eye color: Blue," and "Clothing: Casual." This information is entered directly into the web form, and clicking the submit button proceeds to the next step.

[0095] Sending input data

[0096] The device uses AJAX requests to send user-entered information to the server. This data is sent in JSON format. For example, an AJAX request sends a POST request to the " / api / generate-character" endpoint.

[0097] Illustration generation

[0098] The server analyzes the received data and generates character illustrations using an AI illustration generation model (e.g., a Stable Diffusion model). The server analyzes the data using a script written in Python and generates illustrations using a high-performance machine learning algorithm.

[0099] Saving the generated illustration

[0100] The generated character illustrations are uploaded to cloud storage (e.g., Amazon S3) by the server, and a URL is generated for them. The URL is also stored in a database (e.g., MySQL® or PostgreSQL) so that it can be retrieved later.

[0101] AR / VR display

[0102] The device uses ARCore or ARKit to display the received image URL in augmented reality. This allows the user to see the character in the real world through their smartphone.

[0103] The user speaks to the character.

[0104] Users send voice or text messages to the character using the chat window on their device. For example, they might type "How are you today?" and click the send button.

[0105] Response generation by generative AI

[0106] The server receives user messages and passes them to a generative AI model (e.g., GPT-3®) to generate appropriate responses. This AI model is designed to understand the context of the conversation and generate natural-sounding dialogue.

[0107] Receiving and playing back audio

[0108] The generated response is converted into an audio file using speech synthesis technology (e.g., Amazon Polly) and sent to the user's device. The device plays this audio file, allowing the user to hear the character's response aloud.

[0109] As a concrete example, a user enters "a woman with long blonde hair, blue eyes, and casual clothing" into a web form on their device and submits it. The server generates a character illustration and returns the saved URL to the user. The user then displays the character in AR mode on their smartphone and texts, "How's your day?" The server uses a generative AI to generate a response, "It's a good day! Thank you!" and sends the audio file to the device. The device then plays the audio response and delivers it to the user.

[0110] Example of a prompt:

[0111] "Please create a character with blonde hair, blue eyes, and casual clothing."

[0112] "Please tell me how to display a character in AR mode and how to talk to the character."

[0113] "Generate a response when you ask a character, 'How are you today?'"

[0114] In this way, the system enables users to quickly generate their ideal character and engage in natural interactions with the character displayed in augmented reality or virtual reality.

[0115] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0116] Step 1: The user enters the character attributes.

[0117] Users enter details about their character's appearance using a web form on their device. Specifically, they fill in information such as "hair color," "eye color," and "clothing," and then click the submit button. This input prepares the user's requests as data.

[0118] Input: User input of character attributes (e.g., "Hair color: Blonde", "Eye color: Blue", "Clothing: Casual")

[0119] Output: Preparing for an AJAX request

[0120] Step 2: Submit the input data

[0121] The terminal uses AJAX requests to send user-entered data to the server. This ensures that the user's requests are received by the server in a specific format (JSON format).

[0122] Input: User input data (JSON format)

[0123] Output: Request to send data to the server

[0124] Step 3: The server receives and analyzes the data.

[0125] The server parses the received JSON data and extracts the character's attributes. For example, it might parse and extract information such as "Hair color: Blonde," "Eye color: Blue," and "Clothing: Casual."

[0126] Input: User input data (JSON format)

[0127] Output: Analyzed character attributes

[0128] Step 4: Generating character illustrations

[0129] The server provides the analyzed data to an AI illustration generation model (e.g., a Stable Diffusion model) to generate character illustrations. A script written in Python is used to call the machine learning algorithm and generate the illustrations.

[0130] Input: Analyzed character attributes

[0131] Output: Generated character illustration

[0132] Step 5: Save the generated illustration

[0133] The server uploads the generated character illustration to cloud storage (e.g., Amazon S3) and generates an image URL. This URL is stored in the database.

[0134] Input: Generated character illustration

[0135] Output: Image URL

[0136] Step 6: Return the image URL

[0137] The server sends the generated image URL back to the user's device. This allows the user to easily access the character image.

[0138] Input: Image URL

[0139] Output: URL returned to the user's terminal

[0140] Step 7: AR / VR Display

[0141] The device uses the received image URL to display the character in augmented reality (AR) or virtual reality (VR). ARKit or ARCore is used to display the character in real space.

[0142] Input: Image URL

[0143] Output: AR / VR display on the device

[0144] Step 8: The user speaks to the character.

[0145] Users send voice or text messages to the character using the chat window on their device. For example, they might type a question like "How are you today?" and click the send button.

[0146] Input: User's message (voice or text)

[0147] Output: Sending a message to the server

[0148] Step 9: Response generation by generative AI

[0149] The server receives the user's message and passes it to a generative AI model (e.g., GPT-3) to generate an appropriate response. The AI ​​model understands the context of the conversation and generates natural-sounding dialogue.

[0150] Input: User message

[0151] Output: Generated response text

[0152] Step 10: Audio generation and playback

[0153] The server converts the generated text response into an audio file using speech synthesis technology (e.g., Amazon Polly) and sends it to the user's device. The device then plays this audio file, allowing the user to hear the character's response aloud.

[0154] Input: Generated response text

[0155] Output: Generate and send audio files

[0156] In this way, specific actions and inputs / outputs are defined at each step, and the overall system flow is designed to function. This process allows users to quickly generate their desired character and engage in natural interactions with the character displayed in augmented reality or virtual reality.

[0157] (Application Example 1)

[0158] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0159] There is a need to improve the efficiency and accuracy of factory workers, as well as to rapidly train new employees. Furthermore, it is necessary to reduce work errors and improve safety by providing real-time visual displays and audio guidance of work procedures. To address these challenges, an effective support system utilizing smart devices is required.

[0160] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0161] In this invention, the server includes means for receiving user input data, means for generating images based on the received data, means for storing the generated images, means for displaying the stored images, means for receiving voice or text messages from the user, means for generating responses based on the received messages, means for converting the generated responses into voice, means for displaying work instructions using augmented reality (AR), and means for providing the generated responses and work instructions in voice or video. This enables workers to receive real-time visual and audio support, allowing them to perform work efficiently and accurately.

[0162] "Means for receiving user input data" refers to devices or software used to acquire data such as text, images, and audio entered by the user.

[0163] "Means for generating images based on received data" refers to devices or software that analyze data received from a user and create images accordingly.

[0164] "Means for saving generated images" refers to devices or software for storing generated image data in memory devices or cloud storage.

[0165] "Means for displaying saved images" refers to devices or software that allow users to visually confirm stored image data.

[0166] "Means for receiving voice or text messages from users" refers to devices or software for acquiring voice or text data sent by users.

[0167] "Means for generating a response based on a received message" refers to devices or software that analyze a message received from a user and create a corresponding response.

[0168] "Means for converting generated responses into speech" refers to devices or software for converting responses created in text format into speech data.

[0169] "Means for displaying work instructions using augmented reality (AR)" refer to devices or software that overlay virtual information onto a real-world environment to provide work instructions.

[0170] "Means for providing generated responses and work instructions in audio or video" refers to devices or software for communicating generated responses and work instructions to the user via audio or video.

[0171] This system generates assistant characters to support factory workers, thereby improving work efficiency and accuracy. A detailed description of this invention follows.

[0172] 1. System Overview

[0173] The system receives user input data and generates character images based on that data. The generated characters provide work instructions using AR displays and respond appropriately to the user with audio and video. It also receives audio and text messages from the user and generates responses based on them. The main hardware and software used, as well as specific processing steps, are described below.

[0174] 2. Hardware and software to be used

[0175] Hardware:

[0176] High-performance server

[0177] Smart Glasses

[0178] Audio output devices (speakers, earphones)

[0179] software:

[0180] Flask web application framework: Server-side processing and API provisioning

[0181] GPT-Neo Generative AI Model: Text Generation and Response Generation

[0182] PIL (Python Imaging Library): Character image generation

[0183] AR display module: Displaying work instructions in augmented reality

[0184] Text-to-speech engine (e.g., Google® Text-to-Speech): Converts text into speech.

[0185] 3. Data processing and data calculation

[0186] When the server receives user input data, it analyzes it and generates a character image. Using PIL (Personal Information Leaflet), it creates an image that reflects attributes such as hair color, eye color, and clothing specified by the user. The generated image is stored in cloud storage, and a URL for display is generated.

[0187] Next, the server analyzes the user's voice and text messages and generates an appropriate response using the GPT-Neo generative AI model. The generated response is then converted into voice data by a speech synthesis engine and sent to the user's device.

[0188] Users can receive work instructions via AR display through smart glasses. This allows 3D models and instructions to be overlaid in real time on the work area, providing visual and audio support.

[0189] 4. Specific Examples and Prompts

[0190] Specific example:

[0191] When a worker puts on smart glasses and asks, "What's the next assembly step?", the character responds, "Next, attach part A, and then secure part B with screws," and uses augmented reality to show the attachment locations of the parts. Furthermore, it provides voice guidance, saying, "Please attach part A in this position."

[0192] Example of a prompt:

[0193] User: What's the next step?

[0194] AI: In the next step, attach part A, and then secure part B with screws.

[0195] In this way, the system supports the user's work visually and audibly, enabling efficient and accurate work.

[0196] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0197] Step 1:

[0198] The server receives user input data. When a user enters attributes such as the character's hair color, eye color, and clothing on the smart glasses settings screen, this data is sent to the server via the device. The input data sent includes hair color "blonde," eye color "blue," and clothing "casual."

[0199] Step 2:

[0200] The server generates character images based on the received data. First, the server parses the received data and uses PIL (Python Imaging Library) to create an image based on the specified attributes. For example, it can generate an image of a female character with "blonde hair," "blue eyes," and "casual clothing."

[0201] Step 3:

[0202] The server saves the generated character image to cloud storage. During this saving process, the image data is uploaded to cloud storage, and a URL for display is generated. The output is the URL of the character image.

[0203] Step 4:

[0204] The device displays the character using an image URL received from the server. The character is then displayed on the smart glasses' screen, allowing the user to see it.

[0205] Step 5:

[0206] The user sends voice or text messages to the character through their device. For example, they might ask, "What's the next assembly step?" This message is then sent from the device to the server.

[0207] Step 6:

[0208] The server receives a message from the user and passes it to the GPT-Neo generation AI model to generate an appropriate response. It analyzes the received message "What is the next assembly step?" and generates the appropriate response "Next, attach part A, then screw in part B."

[0209] Step 7:

[0210] The server converts the generated response into speech. It passes the generated response text to the speech synthesis engine to generate speech data. The output is an audio file that says, "Next, attach part A, and then secure part B with screws."

[0211] Step 8:

[0212] The device plays the received audio data. The user can then hear the audio response through smart glasses or an audio output device.

[0213] Step 9:

[0214] The device displays work instructions using augmented reality (AR). It overlays 3D models and specific work procedures onto the existing real-world environment within the work area using AR. Users receive real-time visual guidance.

[0215] Step 10:

[0216] The user performs the following steps. The system will continue to provide additional responses and guidance as needed until the steps are completed.

[0217] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0218] This invention generates an ideal character based on user requests and gives that character emotion recognition and dialogue capabilities. The system's operation will be explained in detail, along with specific examples.

[0219] User request input

[0220] Users use their devices to enter details about the character's appearance. This input is done through a web form, where attribute information such as "hair color," "eye color," and "clothing" is entered. For example, a user might enter a request such as "a woman with long blonde hair, blue eyes, and casual clothing."

[0221] Sending input data

[0222] The device collects user input data and sends it to the server. An AJAX request is used to send the input data to the server-side API endpoint. The data is sent as pairs of form field names and their values.

[0223] Illustration generation

[0224] The server analyzes the received data and inputs it into an AI illustration generation model. The model uses advanced machine learning algorithms to generate character images based on user specifications. For example, it generates character images that conform to specifications such as "long blonde hair," "blue eyes," and "casual clothing."

[0225] Saving the generated illustration

[0226] The server saves the generated character illustration to a database or cloud storage. Once the saving process is complete, a URL for the generated image is issued. This URL is returned to the user, who can then use it to view the image later.

[0227] AR / VR display

[0228] The device displays a character using an image URL it receives. The character is visually displayed using AR or VR technology, and the user can view this displayed character. A chat window also appears, allowing the user to talk to the character.

[0229] The user speaks to the character.

[0230] The user enters messages using the chat window on their device. The user types voice or text messages to the character and clicks the "Send" button. For example, they might type the question, "How are you today?"

[0231] Emotion analysis using an emotion recognition engine

[0232] The server receives the user's message and passes it to the emotion recognition engine for emotion analysis. The emotion recognition engine identifies the emotional state (e.g., joy, sadness, anger, etc.) from the user's message. This analysis result is then passed to the generative AI in the next step.

[0233] Response generation by generative AI

[0234] The server passes the received message and emotion recognition results to the generative AI, which then generates an appropriate response. The AI ​​generates a natural response based on the user's question and emotional state. For example, if the user asks "How's today?" and the emotion recognition engine identifies that the user is feeling a little anxious, the generative AI will generate a response such as "I'm a little busy today, but I'm okay. How about you?"

[0235] Receiving and playing back audio

[0236] The generated response is input into the speech synthesis system on the server and produced as an audio file. This audio file is sent to the user's terminal and played back. The user can hear the character's response in audio. For example, the audio might say, "I'm a little busy today, but it's okay. How about you?"

[0237] This system is designed to quickly generate a character of the user's choice and allow for natural, emotion-responsive interaction with that character. By incorporating an emotion recognition engine, the system is designed to make the character's responses more human-like and considerate of the user's emotions.

[0238] The following describes the processing flow.

[0239] Step 1:

[0240] The user uses their device to enter details about the character's appearance. The input fields provide a form where users can enter attributes such as "hair color," "eye color," and "clothing." The user fills in the desired attributes in these fields and clicks the "Submit" button.

[0241] Step 2:

[0242] The device collects user input data and sends it to the server. An AJAX request is used to send the input data to the server-side API endpoint. The data is sent as pairs of form field names and their values.

[0243] Step 3:

[0244] The server analyzes the received data and inputs it into an AI illustration generation model. The model uses advanced machine learning algorithms to generate character images based on user specifications. For example, it generates character images that conform to specifications such as "long blonde hair," "blue eyes," and "casual clothing."

[0245] Step 4:

[0246] The server saves the generated character illustration to a database or cloud storage. Once the saving process is complete, a URL for the generated image is issued. This URL is returned to the user, who can then use it to view the image later.

[0247] Step 5:

[0248] The device displays a character using an image URL it receives. The character is visually displayed using AR or VR technology, and the user can view this displayed character. A chat window also appears, allowing the user to talk to the character.

[0249] Step 6:

[0250] The user enters a message using the chat window on their device. The user types a voice or text message to the character and clicks the "Send" button. For example, they can type a question like, "How are you today?"

[0251] Step 7:

[0252] The device sends the entered message to the server. The sent message is received and parsed at the server's API endpoint. This received data is then passed to the emotion recognition engine in the next step.

[0253] Step 8:

[0254] The server passes the user's message to the emotion recognition engine, which analyzes the emotion. The emotion recognition engine identifies the emotional state (e.g., joy, sadness, anger) from the user's message. For example, it uses speech analysis or text analysis to detect the user's emotions in real time.

[0255] Step 9:

[0256] The server passes the emotion recognition results to the generative AI, which then generates an appropriate response based on the user's emotions. The AI ​​can generate natural responses depending on the user's questions and emotional state. For example, if the user asks "How's today?" and the AI ​​determines that the user is feeling anxious, it will generate a response such as "It's okay, I think you'll have a good day."

[0257] Step 10:

[0258] The server inputs the generated response into the speech synthesis system, which then generates an audio file. The speech synthesis system creates natural-sounding speech based on the generated text. For example, the generated response "It's okay, I think something good will happen today." is converted into an audio file.

[0259] Step 11:

[0260] The device receives an audio file generated from the server. The audio file is played on the user's device, and the user can hear the character's response in audio. For example, the audio might say, "Don't worry, I think something good will happen today."

[0261] This system is designed to generate a character of the user's choice and allow them to engage in emotionally-driven conversations with that character. By incorporating an emotion recognition engine, the system is designed to make the character's responses more human-like and considerate of the user's emotions.

[0262] (Example 2)

[0263] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0264] Conventional character generation systems have the problem of being time-consuming and often requiring a lot of manual work when generating characters based on user requests. Furthermore, the interaction experience with the generated characters is often unnatural, resulting in a limited user experience. This invention aims to provide a system that can quickly generate characters based on user input and enable natural, emotionally responsive interaction with those characters.

[0265] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving user request input, means for transmitting received data to the server, means for using a generative AI model that generates an image based on the received data, means for saving the generated image, means for issuing a URL for the saved image, means for displaying the image on the user terminal using AR / VR technology, means for receiving user messages, means for performing sentiment analysis on the received message using a sentiment recognition engine, means for generating a response based on the sentiment analysis, and means for converting the generated response into speech using a speech synthesis model. This makes it possible to quickly generate a character specified by the user and to have a natural conversation with that character.

[0266] "User" refers to a person who uses a system or an end-user.

[0267] "Request input" refers to the input of wishes and specifications that users provide to the system.

[0268] "Terminal" refers to a device used by a user, such as a computer, smartphone, or tablet.

[0269] "Data" refers to information, files, etc., generated based on the requested input.

[0270] A "server" refers to a remote computer system that receives user requests, processes them, and returns the results.

[0271] A "generative AI model" refers to an algorithm or program that uses artificial intelligence technology to generate character images based on specific prompts.

[0272] "Saving" refers to the act of storing generated data or images in a database or cloud storage.

[0273] "URL" stands for Uniform Resource Locator, and refers to an address used to point to a resource on the web.

[0274] "AR / VR technology" is a general term for Augmented Reality and Virtual Reality technologies, referring to technologies that provide users with visually and experientially rich interfaces.

[0275] A "message" refers to text or audio information that a user inputs into the system.

[0276] An "emotion recognition engine" refers to software or algorithms that analyze and identify a user's emotional state from their messages.

[0277] "Response" refers to the content of the system's reply to a user's message.

[0278] A "speech synthesis model" refers to an algorithm or program used to convert text data into speech data.

[0279] This invention is a system that generates an ideal character based on user requests and gives that character emotion recognition and dialogue capabilities. The main components of the system and their specific operation will be described below.

[0280] First, the user uses their device to enter details about the character's appearance into a web form. This includes details such as hair color, eye color, and clothing. For example, the user might enter "a woman with long blonde hair, blue eyes, and casual clothing" as their preference.

[0281] Next, the device sends this input data to the server. This transmission is done using an AJAX request, and the data is sent to the server's API endpoint in JSON format.

[0282] The server inputs the received data into a generative AI model to generate an image of a character. As the generative AI model, for example, an illustration generation model that makes full use of machine learning algorithms (such as DALL-E of OpenAI (registered trademark) or Stable Diffusion) is used. In this process, advanced machine learning technology generates a character image in accordance with the user's specification.

[0283] The generated character image is stored in a database or cloud storage by the server. After the storage process is completed, an image URL is issued and returned to the user's terminal.

[0284] The terminal uses the received image URL to display the character using AR (augmented reality) or VR (virtual reality) technology. The user can view the character in the AR / VR environment. Also, a chat window is displayed on the terminal, and the user can talk to the character through this chat window.

[0285] When the user inputs a text message using the chat window of the terminal, for example, a question like "How are you today?" can be entered. In response, the server passes the user's message to an emotion recognition engine to analyze the emotion. The emotion recognition engine can use, for example, Sentiment Analysis of Microsoft (registered trademark) Azure (registered trademark) Cognitive Services.

[0286] When the result of the emotion analysis comes out, it is passed to the generative AI to generate an appropriate response. For example, a generative AI model such as GPT-3 of OpenAI is used to generate a natural response according to the user's question and emotional state. If the user asks "How are you today?" and the emotion recognition engine identifies that the user is a little anxious, the generative AI generates a response such as "I'm a little busy today, but it's okay. How about you?"

[0287] The generated response is input by the server into a speech synthesis system and produced as an audio file. The text is converted to speech using APIs such as the Google Text-to-Speech API. This audio file is sent to the user's device and played back. The user can then hear the character's response in audio form.

[0288] Examples of specific cases and prompt statements

[0289] As a concrete example, imagine a scenario where a user creates a female character with long blonde hair, blue eyes, and casual clothing, and then asks the character, "How are you today?"

[0290] Examples of prompt statements are as follows:

[0291] Please create a female character with long blonde hair, blue eyes, and casual clothing. When the user asks this character "How are you today?", please generate a natural response to the question and play it back using speech synthesis.

[0292] This system allows users to quickly generate their desired character and engage in natural, emotion-responsive conversations with it. By combining an emotion recognition engine and generative AI, the character's responses become more human-like and take the user's emotions into consideration.

[0293] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0294] Step 1: The user enters the request.

[0295] The user uses their device to enter details about the character's appearance into a web form. This includes "hair color," "eye color," and "clothing." For example, the user might enter "a woman with long blonde hair, blue eyes, and casual clothing." This generates the requested data.

[0296] Input: Character details (hair color, eye color, clothing, etc.)

[0297] Output: Generated request data (in JSON format)

[0298] Step 2: The terminal sends the request data to the server

[0299] The terminal sends the generated request data to the server's API endpoint using an AJAX request. In this process, the data is sent to the server in JSON format.

[0300] Input: Request data (in JSON format)

[0301] Output: Data transmission request to the server

[0302] Step 3: The server receives the data and inputs it into the generation AI model

[0303] The server analyzes the received data and inputs it into the generation AI model. The generation AI model generates a character image based on the user's specifications. For example, OpenAI's DALL-E or Stable Diffusion may be used.

[0304] Input: The request data received by the server

[0305] Output: Generated character image data

[0306] Step 4: The server saves the generated character image

[0307] The server saves the generated character image in a database or cloud storage. At this time, the URL of the saved image is generated.

[0308] Input: Generated character image data

[0309] Output: URL of the saved image

[0310] Step 5: The device receives the image URL and displays it using AR / VR technology.

[0311] The device receives an image URL sent from the server and displays it using an AR / VR application. This allows the user to visually experience the generated character.

[0312] Input: Image URL returned from the server

[0313] Output: Character display in AR / VR environments

[0314] Step 6: The user speaks to the character.

[0315] The user uses the chat window on their device to type a message to the character. For example, they might type, "How are you today?" This generates a text message.

[0316] Input: Message from the user

[0317] Output: Generated text message

[0318] Step 7: The server passes the message to the sentiment recognition engine for analysis.

[0319] The server receives the user's message and passes it to the sentiment recognition engine for analysis. The sentiment recognition engine (for example, Microsoft Azure Cognitive Services' Sentiment Analysis) analyzes the sentiment of the message.

[0320] Input: Text message from the user

[0321] Output: Emotion analysis results (emotional state)

[0322] Step 8: The server generates a response for the generative AI.

[0323] The server passes the data, along with the sentiment analysis results, to a generative AI, which then generates an appropriate response. This results in a response in natural language. For example, OpenAI's GPT-3 is used.

[0324] Input: Sentiment analysis results, text messages from the user

[0325] Output: Generated natural language response message

[0326] Step 9: The server synthesizes the response message into speech and sends it to the terminal.

[0327] The server inputs the generated response message into a speech synthesis model to produce an audio file. This audio file is sent to the terminal and played back by the user. For example, the Google Text-to-Speech API is used.

[0328] Input: Generated natural language response message

[0329] Output: Audio file, sent to the device.

[0330] The above outlines the specific processing flow within this system's program.

[0331] (Application Example 2)

[0332] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0333] Online content distribution services require systems that can provide dynamic and emotionally resonant character interactions linked to the content users are viewing. Furthermore, these systems need to acquire information about the content users are viewing and adaptively generate responses based on that information, thereby providing deeper engagement and satisfaction.

[0334] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving user input data, means for generating an image based on the received data, means for storing the generated image, means for displaying the stored image, means for receiving voice or text messages from the user, means for generating a response based on the received message, means for converting the generated response into voice, means for acquiring information on the content being viewed, and means for adjusting the response based on the acquired information. This enables natural and emotionally responsive dialogue linked to the content being viewed.

[0335] "Input data" refers to information provided by the user, such as the appearance, characteristics, and messages of the character.

[0336] "Generative artificial intelligence (AI)" refers to advanced machine learning algorithms that analyze user input data and generate character images based on that analysis.

[0337] "Image generation means" refers to a device or software that generates character images based on received data using generative artificial intelligence (AI).

[0338] "Image storage means" refers to a function for saving the generated character images to a database or cloud storage.

[0339] "Image display means" refers to the function that displays saved character images on the user's device.

[0340] "Message receiving means" refers to a device or software that receives voice or text messages from a user.

[0341] "Response generation means" refers to a device or software for generating an appropriate dialogue response based on a message received from the user and the emotion recognition result.

[0342] "Voice conversion means" refers to a speech synthesis system that generates the generated response as an audio file.

[0343] "Content information acquisition means" refers to APIs and databases used by users to obtain information about the content they are currently viewing.

[0344] "Response adjustment means" refers to a device or software for adaptively adjusting the response based on acquired content information.

[0345] This invention is a system that generates an ideal character based on user requests and provides it with emotion recognition and dialogue capabilities. Specifically, the server and the user's terminal work together to perform the following processes.

[0346] 1. User request input

[0347] The user uses their device to enter details about the character's appearance. This input is done through a web form, and attribute information such as "hair color," "eye color," and "clothing" is entered into the form. For example, the user might enter a request such as "a woman with blonde hair, blue eyes, and casual clothing."

[0348] 2. Sending input data

[0349] The terminal receives user input data and sends it to the server. An AJAX request is used to send the input data to the server-side API endpoint. The data is sent as pairs of form field names and their values.

[0350] 3. Illustration generation

[0351] The server analyzes the received data and uses generative artificial intelligence (AI) to generate character images based on that data. For example, it generates character images that meet specifications such as "blonde hair, blue eyes, and casual clothing." Advanced machine learning algorithms are used for this purpose.

[0352] 4. Saving the generated illustration

[0353] The server saves the generated character illustration to a database or cloud storage. Once the saving process is complete, a URL for the generated image is issued. This URL is returned to the user, who can later use this URL to view the image.

[0354] 5. AR / VR display

[0355] The device displays the character using the received image URL. This character is visually displayed using AR or VR technology, and the user can view this displayed character. A chat window also appears, allowing the user to talk to the character.

[0356] 6. The user speaks to the character.

[0357] The user enters messages using the chat window on their device. The user types voice or text messages to the character and clicks the "Send" button. For example, the user could type the question, "How are you today?"

[0358] 7. Emotion analysis using an emotion recognition engine

[0359] The server receives the user's message and passes it to the emotion recognition engine for emotion analysis. The emotion recognition engine identifies the emotional state (e.g., joy, sadness, anger, etc.) from the user's message. This analysis result is then passed to the generative AI in the next step.

[0360] 8. Response generation using generative AI

[0361] The server passes the received message and emotion recognition results to the generative AI, which generates an appropriate response. This response is natural, depending on the user's question and emotional state. For example, if the user asks "How's today?" and the emotion recognition engine identifies that the user is feeling a little anxious, the generative AI will generate a response such as "I'm a little busy today, but I'm okay. How about you?"

[0362] 9. Content information acquisition and response adjustment

[0363] The server obtains information about the content the user is viewing using a content information acquisition mechanism. Based on this information, it adjusts the generated response to be more adaptive and relevant.

[0364] 10. Receiving and playing audio

[0365] The generated response is input into the speech synthesis system on the server and produced as an audio file. This audio file is sent to the user's terminal and played back. The user can hear the character's response in audio. For example, the audio might say, "I'm a little busy today, but it's okay. How about you?"

[0366] Adding specific examples

[0367] As an example of prompt text, the character generation prompt is "a woman with blonde hair, blue eyes, and casual clothing," and the user's emotion when asked "How are you today?" in the dialogue prompt is a subtle sense of unease.

[0368] This system allows for the rapid generation of characters desired by the user, enabling natural and emotionally responsive interactions with those characters. Furthermore, it enables adaptive responses linked to the content being viewed, thereby increasing user engagement.

[0369] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0370] Step 1:

[0371] The user enters details about the character's appearance, such as "hair color," "eye color," and "clothing," into a web form on their device. The input data might include information like "a woman with blonde hair, blue eyes, and casual clothing." This is the input data.

[0372] Step 2:

[0373] The terminal sends the entered data to the server using an AJAX request. The data is sent as pairs of form field names and their values. The server receives this data and uses it for parsing.

[0374] Step 3:

[0375] The server analyzes the received input data and feeds it into a generative artificial intelligence (AI) model. The AI ​​model uses machine learning algorithms to generate character images based on specifications such as "blonde hair," "blue eyes," and "casual clothing." This becomes the output of the image generation process.

[0376] Step 4:

[0377] The generated character images are saved by the server to a database or cloud storage. A URL for the generated image is issued during saving. This URL is the output data.

[0378] Step 5:

[0379] The device displays the character using an image URL received from the server. AR and VR technologies are used here, allowing users to visually appreciate the character. A chat window also appears, enabling users to interact with the character.

[0380] Step 6:

[0381] The user uses the chat window on their device to enter voice or text messages to the character. For example, they might type a question like, "How are you today?" This is the message input data.

[0382] Step 7:

[0383] The server receives messages from users and passes them to the emotion recognition engine. The emotion recognition engine analyzes the received messages and identifies the user's emotional state (e.g., joy, sadness, anger). This emotional state is the output data.

[0384] Step 8:

[0385] The server passes the emotion recognition results and received messages to the generative AI, which generates an appropriate dialogue response. For example, if a user asks "How are you today?" and emotion recognition reveals that the user is feeling anxious, the generative AI will generate a response such as "I'm a little busy today, but I'm okay. How about you?" This is the output data of the response.

[0386] Step 9:

[0387] The server retrieves information about the content the user is currently viewing, for example, using a content API. Based on this information, it evaluates whether the generated response is relevant to the content being viewed and adaptively adjusts the response. This is the content-dependent output data.

[0388] Step 10:

[0389] The server inputs the generated response into the speech synthesis system and produces it as an audio file. This audio file is sent to the terminal, allowing the user to hear the character's response. For example, the voice might say, "I'm a little busy today, but it's okay. How about you?" This is the final output data.

[0390] In this process, a system operates that enables users to create ideal characters based on their requests and engage in natural, emotionally responsive conversations.

[0391] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0392] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0393] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0394] [Second Embodiment]

[0395] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0396] As shown in Figure 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.

[0397] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0398] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0399] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0400] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0401] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0402] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0403] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0404] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0405] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0406] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0407] This system generates an ideal character based on user requests and gives that character the ability to converse. The system's operation will be explained in detail, along with concrete examples.

[0408] User request input

[0409] Users use their devices to enter details about the character's appearance. This input is done through a web form, where attribute information such as "hair color," "eye color," and "clothing" is entered. For example, a user might enter a request such as "a woman with long blonde hair, blue eyes, and casual clothing."

[0410] Sending input data

[0411] The device sends user input data to the server. The data is sent using an AJAX request and received at the server-side API endpoint.

[0412] Illustration generation

[0413] The server analyzes the received data and provides it to an AI illustration generation model to generate character illustrations. This model uses a high-performance machine learning algorithm to generate character images based on the user's requests.

[0414] Saving the generated illustration

[0415] The generated character illustrations are saved by the server to a database or cloud storage. This saving process generates a URL for the character image, which is then returned to the user.

[0416] AR / VR display

[0417] The device uses the received image URL to display the character in AR or VR. Users can view and interact with the character through this display. For example, a user can use their smartphone to display the character in AR mode.

[0418] The user speaks to the character.

[0419] Users use the chat window on their device to send voice or text messages to the character. For example, they might type a question like, "How are you today?"

[0420] Response generation by generative AI

[0421] The server receives user messages and passes them to a generative AI to generate appropriate responses. This AI model is designed to understand the context of the conversation and produce natural-sounding dialogue.

[0422] Receiving and playing back audio

[0423] The generated response is synthesized into speech on the server and sent to the user's device as an audio file. The device plays this audio file, allowing the user to hear the character's response aloud. For example, a response such as "Today is a good day! Thank you!" might be played aloud.

[0424] This system is designed to quickly generate a character of the user's choice and allow for natural conversations with that character. Furthermore, by utilizing generative AI, the system is designed to make the character's responses more human-like and natural.

[0425] The following describes the processing flow.

[0426] Step 1:

[0427] The user uses their device to enter details about the character's appearance. The input fields provide a form where users can enter attributes such as "hair color," "eye color," and "clothing." The user fills in the desired attributes in these fields and clicks the "Submit" button.

[0428] Step 2:

[0429] The device collects user input data and sends it to the server. An AJAX request is used to send the input data to the server-side API endpoint. The data is sent as pairs of form field names and their values.

[0430] Step 3:

[0431] The server analyzes the received data and inputs it into an AI illustration generation model. The model uses advanced machine learning algorithms to generate character images based on user specifications. For example, it generates character images that conform to specifications such as "long blonde hair," "blue eyes," and "casual clothing."

[0432] Step 4:

[0433] The server saves the generated character illustration to a database or cloud storage. Once the saving process is complete, a URL for the generated image is issued. This URL is returned to the user, who can then use it to view the image later.

[0434] Step 5:

[0435] The device displays a character using an image URL it receives. The character is visually displayed using AR or VR technology, and the user can view this displayed character. A chat window also appears, allowing the user to talk to the character.

[0436] Step 6:

[0437] The user enters a message using the chat window on their device. The user types a voice or text message to the character and clicks the "Send" button. For example, they can type a question like, "How are you today?"

[0438] Step 7:

[0439] The terminal sends the entered message to the server. The sent message is received and parsed at the server's API endpoint. This received data is then passed to the generative AI in the next step.

[0440] Step 8:

[0441] The server passes the received message to the generative AI, which generates an appropriate response. The AI ​​generates an appropriate answer in text format to the user's question. For example, it might generate a response like, "Today is a good day! Thank you!" in text format.

[0442] Step 9:

[0443] The server inputs the generated response into the speech synthesis system and generates an audio file. The speech synthesis system creates natural-sounding speech based on the generated text. This audio file is then sent to the user in the next step.

[0444] Step 10:

[0445] The device receives an audio file generated from the server. The audio file is played on the user's device, and the user can hear the character's response in audio. For example, the audio might say, "Today is a good day! Thank you!"

[0446] The above is the specific processing flow of this system. This allows users to generate their desired character and engage in natural conversations with that character.

[0447] (Example 1)

[0448] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0449] Conventional character generation systems have struggled to quickly generate the ideal character desired by the user and to engage in natural dialogue. Furthermore, the lack of functionality to display the generated character in augmented reality (AR) or virtual reality (VR) resulted in a limited user experience. Additionally, the generation of responses to voice or text messages from the user was often unnatural, resulting in a poor dialogue experience.

[0450] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0451] In this invention, the server includes means for receiving user input data, means for generating an image based on the received data, means for storing the generated image, means for displaying the generated image in augmented reality or virtual reality, means for receiving voice or text messages from the user, means for generating a response based on the received message, and means for converting the generated response into voice. This enables the user to quickly generate an ideal character and engage in natural conversation with the character displayed in augmented reality or virtual reality.

[0452] "Means for receiving user input data" refers to a mechanism for sending information entered by a user via a terminal to a server, and for the server to receive that information.

[0453] "Means for generating images based on received data" refers to a mechanism that uses machine learning to generate character images based on user input data received.

[0454] "Means for saving generated images" refers to a mechanism for saving generated character images to a database or cloud storage.

[0455] "Means for displaying saved images" refers to a mechanism for displaying images on a device using the URL of the saved image.

[0456] "Means for receiving voice or text messages from users" refers to a mechanism for a server to receive voice or text messages sent by a user via a terminal.

[0457] "Means for generating a response based on a received message" refers to a mechanism for analyzing a received message and generating an appropriate response.

[0458] "Means for converting generated responses into speech" refers to a mechanism for converting generated text responses into speech data using speech synthesis technology.

[0459] "Means for displaying generated images in augmented reality or virtual reality" refers to a mechanism for displaying generated character images in real space or virtual space using AR or VR technology.

[0460] This invention is a system that generates an ideal character based on user requests and gives that character interactive capabilities. The user inputs details about the character's appearance using a terminal, and the character is generated based on this input information. The specific operation of this system is described below.

[0461] The system will be implemented using the following key hardware and software:

[0462] Hardware: User terminals (PCs and smartphones), and servers that provide image generation and interaction functions.

[0463] Software: Web forms, AJAX requests, AI illustration generation models, generative AI models, speech synthesis technology, databases, cloud storage

[0464] The specific steps are as follows:

[0465] User request input

[0466] Users enter details about their character's appearance using a web form on their device. For example, information such as "Hair color: Blonde," "Eye color: Blue," and "Clothing: Casual." This information is entered directly into the web form, and clicking the submit button proceeds to the next step.

[0467] Sending input data

[0468] The device uses AJAX requests to send user-entered information to the server. This data is sent in JSON format. For example, an AJAX request sends a POST request to the " / api / generate-character" endpoint.

[0469] Illustration generation

[0470] The server analyzes the received data and generates character illustrations using an AI illustration generation model (e.g., a Stable Diffusion model). The server analyzes the data using a script written in Python and generates illustrations using a high-performance machine learning algorithm.

[0471] Saving the generated illustration

[0472] The generated character illustrations are uploaded to cloud storage (e.g., Amazon S3) by the server, and a URL is generated for them. The URL is also stored in a database (e.g., MySQL or PostgreSQL) so that it can be retrieved later.

[0473] AR / VR display

[0474] The device uses ARCore or ARKit to display the received image URL in augmented reality. This allows the user to see the character in the real world through their smartphone.

[0475] The user speaks to the character.

[0476] Users send voice or text messages to the character using the chat window on their device. For example, they might type "How are you today?" and click the send button.

[0477] Response generation by generative AI

[0478] The server receives user messages and passes them to a generative AI model (e.g., GPT-3) to generate appropriate responses. This AI model is designed to understand the context of the conversation and generate natural-sounding dialogue.

[0479] Receiving and playing back audio

[0480] The generated response is converted into an audio file using speech synthesis technology (e.g., Amazon Polly) and sent to the user's device. The device plays this audio file, allowing the user to hear the character's response aloud.

[0481] As a concrete example, a user enters "a woman with long blonde hair, blue eyes, and casual clothing" into a web form on their device and submits it. The server generates a character illustration and returns the saved URL to the user. The user then displays the character in AR mode on their smartphone and texts, "How's your day?" The server uses a generative AI to generate a response, "It's a good day! Thank you!" and sends the audio file to the device. The device then plays the audio response and delivers it to the user.

[0482] Example of a prompt:

[0483] "Please create a character with blonde hair, blue eyes, and casual clothing."

[0484] "Please tell me how to display a character in AR mode and how to talk to the character."

[0485] "Generate a response when you ask a character, 'How are you today?'"

[0486] In this way, the system enables users to quickly generate their ideal character and engage in natural interactions with the character displayed in augmented reality or virtual reality.

[0487] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0488] Step 1: The user enters the character attributes.

[0489] Users enter details about their character's appearance using a web form on their device. Specifically, they fill in information such as "hair color," "eye color," and "clothing," and then click the submit button. This input prepares the user's requests as data.

[0490] Input: User input of character attributes (e.g., "Hair color: Blonde", "Eye color: Blue", "Clothing: Casual")

[0491] Output: Preparing for an AJAX request

[0492] Step 2: Submit the input data

[0493] The terminal uses AJAX requests to send user-entered data to the server. This ensures that the user's requests are received by the server in a specific format (JSON format).

[0494] Input: User input data (JSON format)

[0495] Output: Request to send data to the server

[0496] Step 3: The server receives and analyzes the data.

[0497] The server parses the received JSON data and extracts the character's attributes. For example, it might parse and extract information such as "Hair color: Blonde," "Eye color: Blue," and "Clothing: Casual."

[0498] Input: User input data (JSON format)

[0499] Output: Analyzed character attributes

[0500] Step 4: Generating character illustrations

[0501] The server provides the analyzed data to an AI illustration generation model (e.g., a Stable Diffusion model) to generate character illustrations. A script written in Python is used to call the machine learning algorithm and generate the illustrations.

[0502] Input: Analyzed character attributes

[0503] Output: Generated character illustration

[0504] Step 5: Save the generated illustration

[0505] The server uploads the generated character illustration to cloud storage (e.g., Amazon S3) and generates an image URL. This URL is stored in the database.

[0506] Input: Generated character illustration

[0507] Output: Image URL

[0508] Step 6: Return the image URL

[0509] The server sends the generated image URL back to the user's device. This allows the user to easily access the character image.

[0510] Input: Image URL

[0511] Output: URL returned to the user's terminal

[0512] Step 7: AR / VR Display

[0513] The device uses the received image URL to display the character in augmented reality (AR) or virtual reality (VR). ARKit or ARCore is used to display the character in real space.

[0514] Input: Image URL

[0515] Output: AR / VR display on the device

[0516] Step 8: The user speaks to the character.

[0517] Users send voice or text messages to the character using the chat window on their device. For example, they might type a question like "How are you today?" and click the send button.

[0518] Input: User's message (voice or text)

[0519] Output: Sending a message to the server

[0520] Step 9: Response generation by generative AI

[0521] The server receives the user's message and passes it to a generative AI model (e.g., GPT-3) to generate an appropriate response. The AI ​​model understands the context of the conversation and generates natural-sounding dialogue.

[0522] Input: User message

[0523] Output: Generated response text

[0524] Step 10: Audio generation and playback

[0525] The server converts the generated text response into an audio file using speech synthesis technology (e.g., Amazon Polly) and sends it to the user's device. The device then plays this audio file, allowing the user to hear the character's response aloud.

[0526] Input: Generated response text

[0527] Output: Generate and send audio files

[0528] In this way, specific actions and inputs / outputs are defined at each step, and the overall system flow is designed to function. This process allows users to quickly generate their desired character and engage in natural interactions with the character displayed in augmented reality or virtual reality.

[0529] (Application Example 1)

[0530] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0531] There is a need to improve the efficiency and accuracy of factory workers, as well as to rapidly train new employees. Furthermore, it is necessary to reduce work errors and improve safety by providing real-time visual displays and audio guidance of work procedures. To address these challenges, an effective support system utilizing smart devices is required.

[0532] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0533] In this invention, the server includes means for receiving user input data, means for generating images based on the received data, means for storing the generated images, means for displaying the stored images, means for receiving voice or text messages from the user, means for generating responses based on the received messages, means for converting the generated responses into voice, means for displaying work instructions using augmented reality (AR), and means for providing the generated responses and work instructions in voice or video. This enables workers to receive real-time visual and audio support, allowing them to perform work efficiently and accurately.

[0534] "Means for receiving user input data" refers to devices or software used to acquire data such as text, images, and audio entered by the user.

[0535] "Means for generating images based on received data" refers to devices or software that analyze data received from a user and create images accordingly.

[0536] "Means for saving generated images" refers to devices or software for storing generated image data in memory devices or cloud storage.

[0537] "Means for displaying saved images" refers to devices or software that allow users to visually confirm stored image data.

[0538] "Means for receiving voice or text messages from users" refers to devices or software for acquiring voice or text data sent by users.

[0539] "Means for generating a response based on a received message" refers to devices or software that analyze a message received from a user and create a corresponding response.

[0540] "Means for converting generated responses into speech" refers to devices or software for converting responses created in text format into speech data.

[0541] "Means for displaying work instructions using augmented reality (AR)" refer to devices or software that overlay virtual information onto a real-world environment to provide work instructions.

[0542] "Means for providing generated responses and work instructions in audio or video" refers to devices or software for communicating generated responses and work instructions to the user via audio or video.

[0543] This system generates assistant characters to support factory workers, thereby improving work efficiency and accuracy. A detailed description of this invention follows.

[0544] 1. System Overview

[0545] The system receives user input data and generates character images based on that data. The generated characters provide work instructions using AR displays and respond appropriately to the user with audio and video. It also receives audio and text messages from the user and generates responses based on them. The main hardware and software used, as well as specific processing steps, are described below.

[0546] 2. Hardware and software to be used

[0547] Hardware:

[0548] High-performance server

[0549] Smart Glasses

[0550] Audio output devices (speakers, earphones)

[0551] software:

[0552] Flask web application framework: Server-side processing and API provisioning

[0553] GPT-Neo Generative AI Model: Text Generation and Response Generation

[0554] PIL (Python Imaging Library): Character image generation

[0555] AR display module: Displaying work instructions in augmented reality

[0556] Text-to-speech engine (e.g., Google Text-to-Speech): Converts text into speech.

[0557] 3. Data processing and data calculation

[0558] When the server receives user input data, it analyzes it and generates a character image. Using PIL (Personal Information Leaflet), it creates an image that reflects attributes such as hair color, eye color, and clothing specified by the user. The generated image is stored in cloud storage, and a URL for display is generated.

[0559] Next, the server analyzes the user's voice and text messages and generates an appropriate response using the GPT-Neo generative AI model. The generated response is then converted into voice data by a speech synthesis engine and sent to the user's device.

[0560] Users can receive work instructions via AR display through smart glasses. This allows 3D models and instructions to be overlaid in real time on the work area, providing visual and audio support.

[0561] 4. Specific Examples and Prompts

[0562] Specific example:

[0563] When a worker puts on smart glasses and asks, "What's the next assembly step?", the character responds, "Next, attach part A, and then secure part B with screws," and uses augmented reality to show the attachment locations of the parts. Furthermore, it provides voice guidance, saying, "Please attach part A in this position."

[0564] Example of a prompt:

[0565] User: What's the next step?

[0566] AI: In the next step, attach part A, and then secure part B with screws.

[0567] In this way, the system supports the user's work visually and audibly, enabling efficient and accurate work.

[0568] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0569] Step 1:

[0570] The server receives user input data. When a user enters attributes such as the character's hair color, eye color, and clothing on the smart glasses settings screen, this data is sent to the server via the device. The input data sent includes hair color "blonde," eye color "blue," and clothing "casual."

[0571] Step 2:

[0572] The server generates character images based on the received data. First, the server parses the received data and uses PIL (Python Imaging Library) to create an image based on the specified attributes. For example, it can generate an image of a female character with "blonde hair," "blue eyes," and "casual clothing."

[0573] Step 3:

[0574] The server saves the generated character image to cloud storage. During this saving process, the image data is uploaded to cloud storage, and a URL for display is generated. The output is the URL of the character image.

[0575] Step 4:

[0576] The device displays the character using an image URL received from the server. The character is then displayed on the smart glasses' screen, allowing the user to see it.

[0577] Step 5:

[0578] The user sends voice or text messages to the character through their device. For example, they might ask, "What's the next assembly step?" This message is then sent from the device to the server.

[0579] Step 6:

[0580] The server receives a message from the user and passes it to the GPT-Neo generation AI model to generate an appropriate response. It analyzes the received message "What is the next assembly step?" and generates the appropriate response "Next, attach part A, then screw in part B."

[0581] Step 7:

[0582] The server converts the generated response into speech. It passes the generated response text to the speech synthesis engine to generate speech data. The output is an audio file that says, "Next, attach part A, and then secure part B with screws."

[0583] Step 8:

[0584] The device plays the received audio data. The user can then hear the audio response through smart glasses or an audio output device.

[0585] Step 9:

[0586] The device displays work instructions using augmented reality (AR). It overlays 3D models and specific work procedures onto the existing real-world environment within the work area using AR. Users receive real-time visual guidance.

[0587] Step 10:

[0588] The user performs the following steps. The system will continue to provide additional responses and guidance as needed until the steps are completed.

[0589] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0590] This invention generates an ideal character based on user requests and gives that character emotion recognition and dialogue capabilities. The system's operation will be explained in detail, along with specific examples.

[0591] User request input

[0592] Users use their devices to enter details about the character's appearance. This input is done through a web form, where attribute information such as "hair color," "eye color," and "clothing" is entered. For example, a user might enter a request such as "a woman with long blonde hair, blue eyes, and casual clothing."

[0593] Sending input data

[0594] The device collects user input data and sends it to the server. An AJAX request is used to send the input data to the server-side API endpoint. The data is sent as pairs of form field names and their values.

[0595] Illustration generation

[0596] The server analyzes the received data and inputs it into an AI illustration generation model. The model uses advanced machine learning algorithms to generate character images based on user specifications. For example, it generates character images that conform to specifications such as "long blonde hair," "blue eyes," and "casual clothing."

[0597] Saving the generated illustration

[0598] The server saves the generated character illustration to a database or cloud storage. Once the saving process is complete, a URL for the generated image is issued. This URL is returned to the user, who can then use it to view the image later.

[0599] AR / VR display

[0600] The device displays a character using an image URL it receives. The character is visually displayed using AR or VR technology, and the user can view this displayed character. A chat window also appears, allowing the user to talk to the character.

[0601] The user speaks to the character.

[0602] The user enters messages using the chat window on their device. The user types voice or text messages to the character and clicks the "Send" button. For example, they might type the question, "How are you today?"

[0603] Emotion analysis using an emotion recognition engine

[0604] The server receives the user's message and passes it to the emotion recognition engine for emotion analysis. The emotion recognition engine identifies the emotional state (e.g., joy, sadness, anger, etc.) from the user's message. This analysis result is then passed to the generative AI in the next step.

[0605] Response generation by generative AI

[0606] The server passes the received message and emotion recognition results to the generative AI, which then generates an appropriate response. The AI ​​generates a natural response based on the user's question and emotional state. For example, if the user asks "How's today?" and the emotion recognition engine identifies that the user is feeling a little anxious, the generative AI will generate a response such as "I'm a little busy today, but I'm okay. How about you?"

[0607] Receiving and playing back audio

[0608] The generated response is input into the speech synthesis system on the server and produced as an audio file. This audio file is sent to the user's terminal and played back. The user can hear the character's response in audio. For example, the audio might say, "I'm a little busy today, but it's okay. How about you?"

[0609] This system is designed to quickly generate a character of the user's choice and allow for natural, emotion-responsive interaction with that character. By incorporating an emotion recognition engine, the system is designed to make the character's responses more human-like and considerate of the user's emotions.

[0610] The following describes the processing flow.

[0611] Step 1:

[0612] The user uses their device to enter details about the character's appearance. The input fields provide a form where users can enter attributes such as "hair color," "eye color," and "clothing." The user fills in the desired attributes in these fields and clicks the "Submit" button.

[0613] Step 2:

[0614] The device collects user input data and sends it to the server. An AJAX request is used to send the input data to the server-side API endpoint. The data is sent as pairs of form field names and their values.

[0615] Step 3:

[0616] The server analyzes the received data and inputs it into an AI illustration generation model. The model uses advanced machine learning algorithms to generate character images based on user specifications. For example, it generates character images that conform to specifications such as "long blonde hair," "blue eyes," and "casual clothing."

[0617] Step 4:

[0618] The server saves the generated character illustration to a database or cloud storage. Once the saving process is complete, a URL for the generated image is issued. This URL is returned to the user, who can then use it to view the image later.

[0619] Step 5:

[0620] The device displays a character using an image URL it receives. The character is visually displayed using AR or VR technology, and the user can view this displayed character. A chat window also appears, allowing the user to talk to the character.

[0621] Step 6:

[0622] The user enters a message using the chat window on their device. The user types a voice or text message to the character and clicks the "Send" button. For example, they can type a question like, "How are you today?"

[0623] Step 7:

[0624] The device sends the entered message to the server. The sent message is received and parsed at the server's API endpoint. This received data is then passed to the emotion recognition engine in the next step.

[0625] Step 8:

[0626] The server passes the user's message to the emotion recognition engine, which analyzes the emotion. The emotion recognition engine identifies the emotional state (e.g., joy, sadness, anger) from the user's message. For example, it uses speech analysis or text analysis to detect the user's emotions in real time.

[0627] Step 9:

[0628] The server passes the emotion recognition results to the generative AI, which then generates an appropriate response based on the user's emotions. The AI ​​can generate natural responses depending on the user's questions and emotional state. For example, if the user asks "How's today?" and the AI ​​determines that the user is feeling anxious, it will generate a response such as "It's okay, I think you'll have a good day."

[0629] Step 10:

[0630] The server inputs the generated response into the speech synthesis system, which then generates an audio file. The speech synthesis system creates natural-sounding speech based on the generated text. For example, the generated response "It's okay, I think something good will happen today." is converted into an audio file.

[0631] Step 11:

[0632] The device receives an audio file generated from the server. The audio file is played on the user's device, and the user can hear the character's response in audio. For example, the audio might say, "Don't worry, I think something good will happen today."

[0633] This system is designed to generate a character of the user's choice and allow them to engage in emotionally-driven conversations with that character. By incorporating an emotion recognition engine, the system is designed to make the character's responses more human-like and considerate of the user's emotions.

[0634] (Example 2)

[0635] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0636] Conventional character generation systems have the problem of being time-consuming and often requiring a lot of manual work when generating characters based on user requests. Furthermore, the interaction experience with the generated characters is often unnatural, resulting in a limited user experience. This invention aims to provide a system that can quickly generate characters based on user input and enable natural, emotionally responsive interaction with those characters.

[0637] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving user request input, means for transmitting received data to the server, means for using a generative AI model that generates an image based on the received data, means for saving the generated image, means for issuing a URL for the saved image, means for displaying the image on the user terminal using AR / VR technology, means for receiving user messages, means for performing sentiment analysis on the received message using a sentiment recognition engine, means for generating a response based on the sentiment analysis, and means for converting the generated response into speech using a speech synthesis model. This makes it possible to quickly generate a character specified by the user and to have a natural conversation with that character.

[0638] "User" refers to a person who uses a system or an end-user.

[0639] "Request input" refers to the input of wishes and specifications that users provide to the system.

[0640] "Terminal" refers to a device used by a user, such as a computer, smartphone, or tablet.

[0641] "Data" refers to information, files, etc., generated based on the requested input.

[0642] A "server" refers to a remote computer system that receives user requests, processes them, and returns the results.

[0643] A "generative AI model" refers to an algorithm or program that uses artificial intelligence technology to generate character images based on specific prompts.

[0644] "Saving" refers to the act of storing generated data or images in a database or cloud storage.

[0645] "URL" stands for Uniform Resource Locator, and refers to an address used to point to a resource on the web.

[0646] "AR / VR technology" is a general term for Augmented Reality and Virtual Reality technologies, referring to technologies that provide users with visually and experientially rich interfaces.

[0647] A "message" refers to text or audio information that a user inputs into the system.

[0648] An "emotion recognition engine" refers to software or algorithms that analyze and identify a user's emotional state from their messages.

[0649] "Response" refers to the content of the system's reply to a user's message.

[0650] A "speech synthesis model" refers to an algorithm or program used to convert text data into speech data.

[0651] This invention is a system that generates an ideal character based on user requests and gives that character emotion recognition and dialogue capabilities. The main components of the system and their specific operation will be described below.

[0652] First, the user uses their device to enter details about the character's appearance into a web form. This includes details such as hair color, eye color, and clothing. For example, the user might enter "a woman with long blonde hair, blue eyes, and casual clothing" as their preference.

[0653] Next, the device sends this input data to the server. This transmission is done using an AJAX request, and the data is sent to the server's API endpoint in JSON format.

[0654] The server inputs the received data into a generation AI model to generate character images. The generation AI model used is, for example, an illustration generation model that utilizes machine learning algorithms (such as OpenAI's DALL-E or Stable Diffusion). In this process, advanced machine learning techniques generate character images that conform to the user's specifications.

[0655] The generated character image is saved by the server to a database or cloud storage. After the saving process is complete, a URL for the image is issued and returned to the user's device.

[0656] The device uses the received image URL to display the character using AR (Augmented Reality) or VR (Virtual Reality) technology. Users can then view the character in an AR / VR environment. A chat window also appears on the device, allowing users to communicate with the character through this window.

[0657] When a user enters a text message using the terminal's chat window, they can, for example, type a question like "How are you today?". The server then passes the user's message to an emotion recognition engine for sentiment analysis. The emotion recognition engine can be something like Microsoft Azure Cognitive Services' Sentiment Analysis.

[0658] Once the sentiment analysis results are available, they are passed to a generative AI to generate an appropriate response. For example, a generative AI model such as OpenAI's GPT-3 is used to generate a natural response that corresponds to the user's question and emotional state. If the user asks, "How are you today?" and the sentiment recognition engine identifies that the user is feeling a little anxious, the generative AI will generate a response such as, "I'm a little busy today, but I'm okay. How about you?"

[0659] The generated response is input by the server into a speech synthesis system and produced as an audio file. The text is converted to speech using APIs such as the Google Text-to-Speech API. This audio file is sent to the user's device and played back. The user can then hear the character's response in audio form.

[0660] Examples of specific cases and prompt statements

[0661] As a concrete example, imagine a scenario where a user creates a female character with long blonde hair, blue eyes, and casual clothing, and then asks the character, "How are you today?"

[0662] Examples of prompt statements are as follows:

[0663] Please create a female character with long blonde hair, blue eyes, and casual clothing. When the user asks this character "How are you today?", please generate a natural response to the question and play it back using speech synthesis.

[0664] This system allows users to quickly generate their desired character and engage in natural, emotion-responsive conversations with it. By combining an emotion recognition engine and generative AI, the character's responses become more human-like and take the user's emotions into consideration.

[0665] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0666] Step 1: The user enters the request.

[0667] The user uses their device to enter details about the character's appearance into a web form. This includes "hair color," "eye color," and "clothing." For example, the user might enter "a woman with long blonde hair, blue eyes, and casual clothing." This generates the requested data.

[0668] Input: Character details (hair color, eye color, clothing, etc.)

[0669] Output: Generated request data (JSON format)

[0670] Step 2: The device sends the request data to the server.

[0671] The device sends the generated request data to the server's API endpoint using an AJAX request. In this process, the data is sent to the server in JSON format.

[0672] Input: Request data (JSON format)

[0673] Output: Request to send data to the server

[0674] Step 3: The server receives the data and inputs it into the generated AI model.

[0675] The server analyzes the received data and inputs it into the generative AI model. The generative AI model generates character images based on user specifications. For example, OpenAI's DALL-E or Stable Diffusion may be used.

[0676] Input: Request data received by the server

[0677] Output: Generated character image data

[0678] Step 4: The server saves the generated character image.

[0679] The server saves the generated character image to a database or cloud storage. At this time, a URL for the saved image is generated.

[0680] Input: Generated character image data

[0681] Output: URL of the saved image

[0682] Step 5: The device receives the image URL and displays it using AR / VR technology.

[0683] The device receives an image URL sent from the server and displays it using an AR / VR application. This allows the user to visually experience the generated character.

[0684] Input: Image URL returned from the server

[0685] Output: Character display in AR / VR environments

[0686] Step 6: The user speaks to the character.

[0687] The user uses the chat window on their device to type a message to the character. For example, they might type, "How are you today?" This generates a text message.

[0688] Input: Message from the user

[0689] Output: Generated text message

[0690] Step 7: The server passes the message to the sentiment recognition engine for analysis.

[0691] The server receives the user's message and passes it to the sentiment recognition engine for analysis. The sentiment recognition engine (for example, Microsoft Azure Cognitive Services' Sentiment Analysis) analyzes the sentiment of the message.

[0692] Input: Text message from the user

[0693] Output: Emotion analysis results (emotional state)

[0694] Step 8: The server generates a response for the generative AI.

[0695] The server passes the data, along with the sentiment analysis results, to a generative AI, which then generates an appropriate response. This results in a response in natural language. For example, OpenAI's GPT-3 is used.

[0696] Input: Sentiment analysis results, text messages from the user

[0697] Output: Generated natural language response message

[0698] Step 9: The server synthesizes the response message into speech and sends it to the terminal.

[0699] The server inputs the generated response message into a speech synthesis model to produce an audio file. This audio file is sent to the terminal and played back by the user. For example, the Google Text-to-Speech API is used.

[0700] Input: Generated natural language response message

[0701] Output: Audio file, sent to the device.

[0702] The above outlines the specific processing flow within this system's program.

[0703] (Application Example 2)

[0704] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0705] Online content distribution services require systems that can provide dynamic and emotionally resonant character interactions linked to the content users are viewing. Furthermore, these systems need to acquire information about the content users are viewing and adaptively generate responses based on that information, thereby providing deeper engagement and satisfaction.

[0706] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving user input data, means for generating an image based on the received data, means for storing the generated image, means for displaying the stored image, means for receiving voice or text messages from the user, means for generating a response based on the received message, means for converting the generated response into voice, means for acquiring information on the content being viewed, and means for adjusting the response based on the acquired information. This enables natural and emotionally responsive dialogue linked to the content being viewed.

[0707] "Input data" refers to information provided by the user, such as the appearance, characteristics, and messages of the character.

[0708] "Generative artificial intelligence (AI)" refers to advanced machine learning algorithms that analyze user input data and generate character images based on that analysis.

[0709] "Image generation means" refers to a device or software that generates character images based on received data using generative artificial intelligence (AI).

[0710] "Image storage means" refers to a function for saving the generated character images to a database or cloud storage.

[0711] "Image display means" refers to the function that displays saved character images on the user's device.

[0712] "Message receiving means" refers to a device or software that receives voice or text messages from a user.

[0713] "Response generation means" refers to a device or software for generating an appropriate dialogue response based on a message received from the user and the emotion recognition result.

[0714] "Voice conversion means" refers to a speech synthesis system that generates the generated response as an audio file.

[0715] "Content information acquisition means" refers to APIs and databases used by users to obtain information about the content they are currently viewing.

[0716] "Response adjustment means" refers to a device or software for adaptively adjusting the response based on acquired content information.

[0717] This invention is a system that generates an ideal character based on user requests and provides it with emotion recognition and dialogue capabilities. Specifically, the server and the user's terminal work together to perform the following processes.

[0718] 1. User request input

[0719] The user uses their device to enter details about the character's appearance. This input is done through a web form, and attribute information such as "hair color," "eye color," and "clothing" is entered into the form. For example, the user might enter a request such as "a woman with blonde hair, blue eyes, and casual clothing."

[0720] 2. Sending input data

[0721] The terminal receives user input data and sends it to the server. An AJAX request is used to send the input data to the server-side API endpoint. The data is sent as pairs of form field names and their values.

[0722] 3. Illustration generation

[0723] The server analyzes the received data and uses generative artificial intelligence (AI) to generate character images based on that data. For example, it generates character images that meet specifications such as "blonde hair, blue eyes, and casual clothing." Advanced machine learning algorithms are used for this purpose.

[0724] 4. Saving the generated illustration

[0725] The server saves the generated character illustration to a database or cloud storage. Once the saving process is complete, a URL for the generated image is issued. This URL is returned to the user, who can later use this URL to view the image.

[0726] 5. AR / VR display

[0727] The device displays the character using the received image URL. This character is visually displayed using AR or VR technology, and the user can view this displayed character. A chat window also appears, allowing the user to talk to the character.

[0728] 6. The user speaks to the character.

[0729] The user enters messages using the chat window on their device. The user types voice or text messages to the character and clicks the "Send" button. For example, the user could type the question, "How are you today?"

[0730] 7. Emotion analysis using an emotion recognition engine

[0731] The server receives the user's message and passes it to the emotion recognition engine for emotion analysis. The emotion recognition engine identifies the emotional state (e.g., joy, sadness, anger, etc.) from the user's message. This analysis result is then passed to the generative AI in the next step.

[0732] 8. Response generation using generative AI

[0733] The server passes the received message and emotion recognition results to the generative AI, which generates an appropriate response. This response is natural, depending on the user's question and emotional state. For example, if the user asks "How's today?" and the emotion recognition engine identifies that the user is feeling a little anxious, the generative AI will generate a response such as "I'm a little busy today, but I'm okay. How about you?"

[0734] 9. Content information acquisition and response adjustment

[0735] The server obtains information about the content the user is viewing using a content information acquisition mechanism. Based on this information, it adjusts the generated response to be more adaptive and relevant.

[0736] 10. Receiving and playing audio

[0737] The generated response is input into the speech synthesis system on the server and produced as an audio file. This audio file is sent to the user's terminal and played back. The user can hear the character's response in audio. For example, the audio might say, "I'm a little busy today, but it's okay. How about you?"

[0738] Adding specific examples

[0739] As an example of prompt text, the character generation prompt is "a woman with blonde hair, blue eyes, and casual clothing," and the user's emotion when asked "How are you today?" in the dialogue prompt is a subtle sense of unease.

[0740] This system allows for the rapid generation of characters desired by the user, enabling natural and emotionally responsive interactions with those characters. Furthermore, it enables adaptive responses linked to the content being viewed, thereby increasing user engagement.

[0741] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0742] Step 1:

[0743] The user enters details about the character's appearance, such as "hair color," "eye color," and "clothing," into a web form on their device. The input data might include information like "a woman with blonde hair, blue eyes, and casual clothing." This is the input data.

[0744] Step 2:

[0745] The terminal sends the entered data to the server using an AJAX request. The data is sent as pairs of form field names and their values. The server receives this data and uses it for parsing.

[0746] Step 3:

[0747] The server analyzes the received input data and feeds it into a generative artificial intelligence (AI) model. The AI ​​model uses machine learning algorithms to generate character images based on specifications such as "blonde hair," "blue eyes," and "casual clothing." This becomes the output of the image generation process.

[0748] Step 4:

[0749] The generated character images are saved by the server to a database or cloud storage. A URL for the generated image is issued during saving. This URL is the output data.

[0750] Step 5:

[0751] The device displays the character using an image URL received from the server. AR and VR technologies are used here, allowing users to visually appreciate the character. A chat window also appears, enabling users to interact with the character.

[0752] Step 6:

[0753] The user uses the chat window on their device to enter voice or text messages to the character. For example, they might type a question like, "How are you today?" This is the message input data.

[0754] Step 7:

[0755] The server receives messages from users and passes them to the emotion recognition engine. The emotion recognition engine analyzes the received messages and identifies the user's emotional state (e.g., joy, sadness, anger). This emotional state is the output data.

[0756] Step 8:

[0757] The server passes the emotion recognition results and received messages to the generative AI, which generates an appropriate dialogue response. For example, if a user asks "How are you today?" and emotion recognition reveals that the user is feeling anxious, the generative AI will generate a response such as "I'm a little busy today, but I'm okay. How about you?" This is the output data of the response.

[0758] Step 9:

[0759] The server retrieves information about the content the user is currently viewing, for example, using a content API. Based on this information, it evaluates whether the generated response is relevant to the content being viewed and adaptively adjusts the response. This is the content-dependent output data.

[0760] Step 10:

[0761] The server inputs the generated response into the speech synthesis system and produces it as an audio file. This audio file is sent to the terminal, allowing the user to hear the character's response. For example, the voice might say, "I'm a little busy today, but it's okay. How about you?" This is the final output data.

[0762] In this process, a system operates that enables users to create ideal characters based on their requests and engage in natural, emotionally responsive conversations.

[0763] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0764] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0765] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0766] [Third Embodiment]

[0767] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0768] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0769] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0770] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0771] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0772] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0773] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0774] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0775] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0776] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0777] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0778] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0779] This system generates an ideal character based on user requests and gives that character the ability to converse. The system's operation will be explained in detail, along with concrete examples.

[0780] User request input

[0781] Users use their devices to enter details about the character's appearance. This input is done through a web form, where attribute information such as "hair color," "eye color," and "clothing" is entered. For example, a user might enter a request such as "a woman with long blonde hair, blue eyes, and casual clothing."

[0782] Sending input data

[0783] The device sends user input data to the server. The data is sent using an AJAX request and received at the server-side API endpoint.

[0784] Illustration generation

[0785] The server analyzes the received data and provides it to an AI illustration generation model to generate character illustrations. This model uses a high-performance machine learning algorithm to generate character images based on the user's requests.

[0786] Saving the generated illustration

[0787] The generated character illustrations are saved by the server to a database or cloud storage. This saving process generates a URL for the character image, which is then returned to the user.

[0788] AR / VR display

[0789] The device uses the received image URL to display the character in AR or VR. Users can view and interact with the character through this display. For example, a user can use their smartphone to display the character in AR mode.

[0790] The user speaks to the character.

[0791] Users use the chat window on their device to send voice or text messages to the character. For example, they might type a question like, "How are you today?"

[0792] Response generation by generative AI

[0793] The server receives user messages and passes them to a generative AI to generate appropriate responses. This AI model is designed to understand the context of the conversation and produce natural-sounding dialogue.

[0794] Receiving and playing back audio

[0795] The generated response is synthesized into speech on the server and sent to the user's device as an audio file. The device plays this audio file, allowing the user to hear the character's response aloud. For example, a response such as "Today is a good day! Thank you!" might be played aloud.

[0796] This system is designed to quickly generate a character of the user's choice and allow for natural conversations with that character. Furthermore, by utilizing generative AI, the system is designed to make the character's responses more human-like and natural.

[0797] The following describes the processing flow.

[0798] Step 1:

[0799] The user uses their device to enter details about the character's appearance. The input fields provide a form where users can enter attributes such as "hair color," "eye color," and "clothing." The user fills in the desired attributes in these fields and clicks the "Submit" button.

[0800] Step 2:

[0801] The device collects user input data and sends it to the server. An AJAX request is used to send the input data to the server-side API endpoint. The data is sent as pairs of form field names and their values.

[0802] Step 3:

[0803] The server analyzes the received data and inputs it into an AI illustration generation model. The model uses advanced machine learning algorithms to generate character images based on user specifications. For example, it generates character images that conform to specifications such as "long blonde hair," "blue eyes," and "casual clothing."

[0804] Step 4:

[0805] The server saves the generated character illustration to a database or cloud storage. Once the saving process is complete, a URL for the generated image is issued. This URL is returned to the user, who can then use it to view the image later.

[0806] Step 5:

[0807] The device displays a character using an image URL it receives. The character is visually displayed using AR or VR technology, and the user can view this displayed character. A chat window also appears, allowing the user to talk to the character.

[0808] Step 6:

[0809] The user enters a message using the chat window on their device. The user types a voice or text message to the character and clicks the "Send" button. For example, they can type a question like, "How are you today?"

[0810] Step 7:

[0811] The terminal sends the entered message to the server. The sent message is received and parsed at the server's API endpoint. This received data is then passed to the generative AI in the next step.

[0812] Step 8:

[0813] The server passes the received message to the generative AI, which generates an appropriate response. The AI ​​generates an appropriate answer in text format to the user's question. For example, it might generate a response like, "Today is a good day! Thank you!" in text format.

[0814] Step 9:

[0815] The server inputs the generated response into the speech synthesis system and generates an audio file. The speech synthesis system creates natural-sounding speech based on the generated text. This audio file is then sent to the user in the next step.

[0816] Step 10:

[0817] The device receives an audio file generated from the server. The audio file is played on the user's device, and the user can hear the character's response in audio. For example, the audio might say, "Today is a good day! Thank you!"

[0818] The above is the specific processing flow of this system. This allows users to generate their desired character and engage in natural conversations with that character.

[0819] (Example 1)

[0820] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0821] Conventional character generation systems have struggled to quickly generate the ideal character desired by the user and to engage in natural dialogue. Furthermore, the lack of functionality to display the generated character in augmented reality (AR) or virtual reality (VR) resulted in a limited user experience. Additionally, the generation of responses to voice or text messages from the user was often unnatural, resulting in a poor dialogue experience.

[0822] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0823] In this invention, the server includes means for receiving user input data, means for generating an image based on the received data, means for storing the generated image, means for displaying the generated image in augmented reality or virtual reality, means for receiving voice or text messages from the user, means for generating a response based on the received message, and means for converting the generated response into voice. This enables the user to quickly generate an ideal character and engage in natural conversation with the character displayed in augmented reality or virtual reality.

[0824] "Means for receiving user input data" refers to a mechanism for sending information entered by a user via a terminal to a server, and for the server to receive that information.

[0825] "Means for generating images based on received data" refers to a mechanism that uses machine learning to generate character images based on user input data received.

[0826] "Means for saving generated images" refers to a mechanism for saving generated character images to a database or cloud storage.

[0827] "Means for displaying saved images" refers to a mechanism for displaying images on a device using the URL of the saved image.

[0828] "Means for receiving voice or text messages from users" refers to a mechanism for a server to receive voice or text messages sent by a user via a terminal.

[0829] "Means for generating a response based on a received message" refers to a mechanism for analyzing a received message and generating an appropriate response.

[0830] "Means for converting generated responses into speech" refers to a mechanism for converting generated text responses into speech data using speech synthesis technology.

[0831] "Means for displaying generated images in augmented reality or virtual reality" refers to a mechanism for displaying generated character images in real space or virtual space using AR or VR technology.

[0832] This invention is a system that generates an ideal character based on user requests and gives that character interactive capabilities. The user inputs details about the character's appearance using a terminal, and the character is generated based on this input information. The specific operation of this system is described below.

[0833] The system will be implemented using the following key hardware and software:

[0834] Hardware: User terminals (PCs and smartphones), and servers that provide image generation and interaction functions.

[0835] Software: Web forms, AJAX requests, AI illustration generation models, generative AI models, speech synthesis technology, databases, cloud storage

[0836] The specific steps are as follows:

[0837] User request input

[0838] Users enter details about their character's appearance using a web form on their device. For example, information such as "Hair color: Blonde," "Eye color: Blue," and "Clothing: Casual." This information is entered directly into the web form, and clicking the submit button proceeds to the next step.

[0839] Sending input data

[0840] The device uses AJAX requests to send user-entered information to the server. This data is sent in JSON format. For example, an AJAX request sends a POST request to the " / api / generate-character" endpoint.

[0841] Illustration generation

[0842] The server analyzes the received data and generates character illustrations using an AI illustration generation model (e.g., a Stable Diffusion model). The server analyzes the data using a script written in Python and generates illustrations using a high-performance machine learning algorithm.

[0843] Saving the generated illustration

[0844] The generated character illustrations are uploaded to cloud storage (e.g., Amazon S3) by the server, and a URL is generated for them. The URL is also stored in a database (e.g., MySQL or PostgreSQL) so that it can be retrieved later.

[0845] AR / VR display

[0846] The device uses ARCore or ARKit to display the received image URL in augmented reality. This allows the user to see the character in the real world through their smartphone.

[0847] The user speaks to the character.

[0848] Users send voice or text messages to the character using the chat window on their device. For example, they might type "How are you today?" and click the send button.

[0849] Response generation by generative AI

[0850] The server receives user messages and passes them to a generative AI model (e.g., GPT-3) to generate appropriate responses. This AI model is designed to understand the context of the conversation and generate natural-sounding dialogue.

[0851] Receiving and playing back audio

[0852] The generated response is converted into an audio file using speech synthesis technology (e.g., Amazon Polly) and sent to the user's device. The device plays this audio file, allowing the user to hear the character's response aloud.

[0853] As a concrete example, a user enters "a woman with long blonde hair, blue eyes, and casual clothing" into a web form on their device and submits it. The server generates a character illustration and returns the saved URL to the user. The user then displays the character in AR mode on their smartphone and texts, "How's your day?" The server uses a generative AI to generate a response, "It's a good day! Thank you!" and sends the audio file to the device. The device then plays the audio response and delivers it to the user.

[0854] Example of a prompt:

[0855] "Please create a character with blonde hair, blue eyes, and casual clothing."

[0856] "Please tell me how to display a character in AR mode and how to talk to the character."

[0857] "Generate a response when you ask a character, 'How are you today?'"

[0858] In this way, the system enables users to quickly generate their ideal character and engage in natural interactions with the character displayed in augmented reality or virtual reality.

[0859] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0860] Step 1: The user enters the character attributes.

[0861] Users enter details about their character's appearance using a web form on their device. Specifically, they fill in information such as "hair color," "eye color," and "clothing," and then click the submit button. This input prepares the user's requests as data.

[0862] Input: User input of character attributes (e.g., "Hair color: Blonde", "Eye color: Blue", "Clothing: Casual")

[0863] Output: Preparing for an AJAX request

[0864] Step 2: Submit the input data

[0865] The terminal uses AJAX requests to send user-entered data to the server. This ensures that the user's requests are received by the server in a specific format (JSON format).

[0866] Input: User input data (JSON format)

[0867] Output: Request to send data to the server

[0868] Step 3: The server receives and analyzes the data.

[0869] The server parses the received JSON data and extracts the character's attributes. For example, it might parse and extract information such as "Hair color: Blonde," "Eye color: Blue," and "Clothing: Casual."

[0870] Input: User input data (JSON format)

[0871] Output: Analyzed character attributes

[0872] Step 4: Generating character illustrations

[0873] The server provides the analyzed data to an AI illustration generation model (e.g., a Stable Diffusion model) to generate character illustrations. A script written in Python is used to call the machine learning algorithm and generate the illustrations.

[0874] Input: Analyzed character attributes

[0875] Output: Generated character illustration

[0876] Step 5: Save the generated illustration

[0877] The server uploads the generated character illustration to cloud storage (e.g., Amazon S3) and generates an image URL. This URL is stored in the database.

[0878] Input: Generated character illustration

[0879] Output: Image URL

[0880] Step 6: Return the image URL

[0881] The server sends the generated image URL back to the user's device. This allows the user to easily access the character image.

[0882] Input: Image URL

[0883] Output: URL returned to the user's terminal

[0884] Step 7: AR / VR Display

[0885] The device uses the received image URL to display the character in augmented reality (AR) or virtual reality (VR). ARKit or ARCore is used to display the character in real space.

[0886] Input: Image URL

[0887] Output: AR / VR display on the device

[0888] Step 8: The user speaks to the character.

[0889] Users send voice or text messages to the character using the chat window on their device. For example, they might type a question like "How are you today?" and click the send button.

[0890] Input: User's message (voice or text)

[0891] Output: Sending a message to the server

[0892] Step 9: Response generation by generative AI

[0893] The server receives the user's message and passes it to a generative AI model (e.g., GPT-3) to generate an appropriate response. The AI ​​model understands the context of the conversation and generates natural-sounding dialogue.

[0894] Input: User message

[0895] Output: Generated response text

[0896] Step 10: Audio generation and playback

[0897] The server converts the generated text response into an audio file using speech synthesis technology (e.g., Amazon Polly) and sends it to the user's device. The device then plays this audio file, allowing the user to hear the character's response aloud.

[0898] Input: Generated response text

[0899] Output: Generate and send audio files

[0900] In this way, specific actions and inputs / outputs are defined at each step, and the overall system flow is designed to function. This process allows users to quickly generate their desired character and engage in natural interactions with the character displayed in augmented reality or virtual reality.

[0901] (Application Example 1)

[0902] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0903] There is a need to improve the efficiency and accuracy of factory workers, as well as to rapidly train new employees. Furthermore, it is necessary to reduce work errors and improve safety by providing real-time visual displays and audio guidance of work procedures. To address these challenges, an effective support system utilizing smart devices is required.

[0904] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0905] In this invention, the server includes means for receiving user input data, means for generating images based on the received data, means for storing the generated images, means for displaying the stored images, means for receiving voice or text messages from the user, means for generating responses based on the received messages, means for converting the generated responses into voice, means for displaying work instructions using augmented reality (AR), and means for providing the generated responses and work instructions in voice or video. This enables workers to receive real-time visual and audio support, allowing them to perform work efficiently and accurately.

[0906] "Means for receiving user input data" refers to devices or software used to acquire data such as text, images, and audio entered by the user.

[0907] "Means for generating images based on received data" refers to devices or software that analyze data received from a user and create images accordingly.

[0908] "Means for saving generated images" refers to devices or software for storing generated image data in memory devices or cloud storage.

[0909] "Means for displaying saved images" refers to devices or software that allow users to visually confirm stored image data.

[0910] "Means for receiving voice or text messages from users" refers to devices or software for acquiring voice or text data sent by users.

[0911] "Means for generating a response based on a received message" refers to devices or software that analyze a message received from a user and create a corresponding response.

[0912] "Means for converting generated responses into speech" refers to devices or software for converting responses created in text format into speech data.

[0913] "Means for displaying work instructions using augmented reality (AR)" refer to devices or software that overlay virtual information onto a real-world environment to provide work instructions.

[0914] "Means for providing generated responses and work instructions in audio or video" refers to devices or software for communicating generated responses and work instructions to the user via audio or video.

[0915] This system generates assistant characters to support factory workers, thereby improving work efficiency and accuracy. A detailed description of this invention follows.

[0916] 1. System Overview

[0917] The system receives user input data and generates character images based on that data. The generated characters provide work instructions using AR displays and respond appropriately to the user with audio and video. It also receives audio and text messages from the user and generates responses based on them. The main hardware and software used, as well as specific processing steps, are described below.

[0918] 2. Hardware and software to be used

[0919] Hardware:

[0920] High-performance server

[0921] Smart Glasses

[0922] Audio output devices (speakers, earphones)

[0923] software:

[0924] Flask web application framework: Server-side processing and API provisioning

[0925] GPT-Neo Generative AI Model: Text Generation and Response Generation

[0926] PIL (Python Imaging Library): Character image generation

[0927] AR display module: Displaying work instructions in augmented reality

[0928] Text-to-speech engine (e.g., Google Text-to-Speech): Converts text into speech.

[0929] 3. Data processing and data calculation

[0930] When the server receives user input data, it analyzes it and generates a character image. Using PIL (Personal Information Leaflet), it creates an image that reflects attributes such as hair color, eye color, and clothing specified by the user. The generated image is stored in cloud storage, and a URL for display is generated.

[0931] Next, the server analyzes the user's voice and text messages and generates an appropriate response using the GPT-Neo generative AI model. The generated response is then converted into voice data by a speech synthesis engine and sent to the user's device.

[0932] Users can receive work instructions via AR display through smart glasses. This allows 3D models and instructions to be overlaid in real time on the work area, providing visual and audio support.

[0933] 4. Specific Examples and Prompts

[0934] Specific example:

[0935] When a worker puts on smart glasses and asks, "What's the next assembly step?", the character responds, "Next, attach part A, and then secure part B with screws," and uses augmented reality to show the attachment locations of the parts. Furthermore, it provides voice guidance, saying, "Please attach part A in this position."

[0936] Example of a prompt:

[0937] User: What's the next step?

[0938] AI: In the next step, attach part A, and then secure part B with screws.

[0939] In this way, the system supports the user's work visually and audibly, enabling efficient and accurate work.

[0940] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0941] Step 1:

[0942] The server receives user input data. When a user enters attributes such as the character's hair color, eye color, and clothing on the smart glasses settings screen, this data is sent to the server via the device. The input data sent includes hair color "blonde," eye color "blue," and clothing "casual."

[0943] Step 2:

[0944] The server generates character images based on the received data. First, the server parses the received data and uses PIL (Python Imaging Library) to create an image based on the specified attributes. For example, it can generate an image of a female character with "blonde hair," "blue eyes," and "casual clothing."

[0945] Step 3:

[0946] The server saves the generated character image to cloud storage. During this saving process, the image data is uploaded to cloud storage, and a URL for display is generated. The output is the URL of the character image.

[0947] Step 4:

[0948] The device displays the character using an image URL received from the server. The character is then displayed on the smart glasses' screen, allowing the user to see it.

[0949] Step 5:

[0950] The user sends voice or text messages to the character through their device. For example, they might ask, "What's the next assembly step?" This message is then sent from the device to the server.

[0951] Step 6:

[0952] The server receives a message from the user and passes it to the GPT-Neo generation AI model to generate an appropriate response. It analyzes the received message "What is the next assembly step?" and generates the appropriate response "Next, attach part A, then screw in part B."

[0953] Step 7:

[0954] The server converts the generated response into speech. It passes the generated response text to the speech synthesis engine to generate speech data. The output is an audio file that says, "Next, attach part A, and then secure part B with screws."

[0955] Step 8:

[0956] The device plays the received audio data. The user can then hear the audio response through smart glasses or an audio output device.

[0957] Step 9:

[0958] The device displays work instructions using augmented reality (AR). It overlays 3D models and specific work procedures onto the existing real-world environment within the work area using AR. Users receive real-time visual guidance.

[0959] Step 10:

[0960] The user performs the following steps. The system will continue to provide additional responses and guidance as needed until the steps are completed.

[0961] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0962] This invention generates an ideal character based on user requests and gives that character emotion recognition and dialogue capabilities. The system's operation will be explained in detail, along with specific examples.

[0963] User request input

[0964] Users use their devices to enter details about the character's appearance. This input is done through a web form, where attribute information such as "hair color," "eye color," and "clothing" is entered. For example, a user might enter a request such as "a woman with long blonde hair, blue eyes, and casual clothing."

[0965] Sending input data

[0966] The device collects user input data and sends it to the server. An AJAX request is used to send the input data to the server-side API endpoint. The data is sent as pairs of form field names and their values.

[0967] Illustration generation

[0968] The server analyzes the received data and inputs it into an AI illustration generation model. The model uses advanced machine learning algorithms to generate character images based on user specifications. For example, it generates character images that conform to specifications such as "long blonde hair," "blue eyes," and "casual clothing."

[0969] Saving the generated illustration

[0970] The server saves the generated character illustration to a database or cloud storage. Once the saving process is complete, a URL for the generated image is issued. This URL is returned to the user, who can then use it to view the image later.

[0971] AR / VR display

[0972] The device displays a character using an image URL it receives. The character is visually displayed using AR or VR technology, and the user can view this displayed character. A chat window also appears, allowing the user to talk to the character.

[0973] The user speaks to the character.

[0974] The user enters messages using the chat window on their device. The user types voice or text messages to the character and clicks the "Send" button. For example, they might type the question, "How are you today?"

[0975] Emotion analysis using an emotion recognition engine

[0976] The server receives the user's message and passes it to the emotion recognition engine for emotion analysis. The emotion recognition engine identifies the emotional state (e.g., joy, sadness, anger, etc.) from the user's message. This analysis result is then passed to the generative AI in the next step.

[0977] Response generation by generative AI

[0978] The server passes the received message and emotion recognition results to the generative AI, which then generates an appropriate response. The AI ​​generates a natural response based on the user's question and emotional state. For example, if the user asks "How's today?" and the emotion recognition engine identifies that the user is feeling a little anxious, the generative AI will generate a response such as "I'm a little busy today, but I'm okay. How about you?"

[0979] Receiving and playing back audio

[0980] The generated response is input into the speech synthesis system on the server and produced as an audio file. This audio file is sent to the user's terminal and played back. The user can hear the character's response in audio. For example, the audio might say, "I'm a little busy today, but it's okay. How about you?"

[0981] This system is designed to quickly generate a character of the user's choice and allow for natural, emotion-responsive interaction with that character. By incorporating an emotion recognition engine, the system is designed to make the character's responses more human-like and considerate of the user's emotions.

[0982] The following describes the processing flow.

[0983] Step 1:

[0984] The user uses their device to enter details about the character's appearance. The input fields provide a form where users can enter attributes such as "hair color," "eye color," and "clothing." The user fills in the desired attributes in these fields and clicks the "Submit" button.

[0985] Step 2:

[0986] The device collects user input data and sends it to the server. An AJAX request is used to send the input data to the server-side API endpoint. The data is sent as pairs of form field names and their values.

[0987] Step 3:

[0988] The server analyzes the received data and inputs it into an AI illustration generation model. The model uses advanced machine learning algorithms to generate character images based on user specifications. For example, it generates character images that conform to specifications such as "long blonde hair," "blue eyes," and "casual clothing."

[0989] Step 4:

[0990] The server saves the generated character illustration to a database or cloud storage. Once the saving process is complete, a URL for the generated image is issued. This URL is returned to the user, who can then use it to view the image later.

[0991] Step 5:

[0992] The device displays a character using an image URL it receives. The character is visually displayed using AR or VR technology, and the user can view this displayed character. A chat window also appears, allowing the user to talk to the character.

[0993] Step 6:

[0994] The user enters a message using the chat window on their device. The user types a voice or text message to the character and clicks the "Send" button. For example, they can type a question like, "How are you today?"

[0995] Step 7:

[0996] The device sends the entered message to the server. The sent message is received and parsed at the server's API endpoint. This received data is then passed to the emotion recognition engine in the next step.

[0997] Step 8:

[0998] The server passes the user's message to the emotion recognition engine, which analyzes the emotion. The emotion recognition engine identifies the emotional state (e.g., joy, sadness, anger) from the user's message. For example, it uses speech analysis or text analysis to detect the user's emotions in real time.

[0999] Step 9:

[1000] The server passes the emotion recognition results to the generative AI, which then generates an appropriate response based on the user's emotions. The AI ​​can generate natural responses depending on the user's questions and emotional state. For example, if the user asks "How's today?" and the AI ​​determines that the user is feeling anxious, it will generate a response such as "It's okay, I think you'll have a good day."

[1001] Step 10:

[1002] The server inputs the generated response into the speech synthesis system, which then generates an audio file. The speech synthesis system creates natural-sounding speech based on the generated text. For example, the generated response "It's okay, I think something good will happen today." is converted into an audio file.

[1003] Step 11:

[1004] The device receives an audio file generated from the server. The audio file is played on the user's device, and the user can hear the character's response in audio. For example, the audio might say, "Don't worry, I think something good will happen today."

[1005] This system is designed to generate a character of the user's choice and allow them to engage in emotionally-driven conversations with that character. By incorporating an emotion recognition engine, the system is designed to make the character's responses more human-like and considerate of the user's emotions.

[1006] (Example 2)

[1007] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1008] Conventional character generation systems have the problem of being time-consuming and often requiring a lot of manual work when generating characters based on user requests. Furthermore, the interaction experience with the generated characters is often unnatural, resulting in a limited user experience. This invention aims to provide a system that can quickly generate characters based on user input and enable natural, emotionally responsive interaction with those characters.

[1009] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving user request input, means for transmitting received data to the server, means for using a generative AI model that generates an image based on the received data, means for saving the generated image, means for issuing a URL for the saved image, means for displaying the image on the user terminal using AR / VR technology, means for receiving user messages, means for performing sentiment analysis on the received message using a sentiment recognition engine, means for generating a response based on the sentiment analysis, and means for converting the generated response into speech using a speech synthesis model. This makes it possible to quickly generate a character specified by the user and to have a natural conversation with that character.

[1010] "User" refers to a person who uses a system or an end-user.

[1011] "Request input" refers to the input of wishes and specifications that users provide to the system.

[1012] "Terminal" refers to a device used by a user, such as a computer, smartphone, or tablet.

[1013] "Data" refers to information, files, etc., generated based on the requested input.

[1014] A "server" refers to a remote computer system that receives user requests, processes them, and returns the results.

[1015] A "generative AI model" refers to an algorithm or program that uses artificial intelligence technology to generate character images based on specific prompts.

[1016] "Saving" refers to the act of storing generated data or images in a database or cloud storage.

[1017] "URL" stands for Uniform Resource Locator, and refers to an address used to point to a resource on the web.

[1018] "AR / VR technology" is a general term for Augmented Reality and Virtual Reality technologies, referring to technologies that provide users with visually and experientially rich interfaces.

[1019] A "message" refers to text or audio information that a user inputs into the system.

[1020] An "emotion recognition engine" refers to software or algorithms that analyze and identify a user's emotional state from their messages.

[1021] "Response" refers to the content of the system's reply to a user's message.

[1022] A "speech synthesis model" refers to an algorithm or program used to convert text data into speech data.

[1023] This invention is a system that generates an ideal character based on user requests and gives that character emotion recognition and dialogue capabilities. The main components of the system and their specific operation will be described below.

[1024] First, the user uses their device to enter details about the character's appearance into a web form. This includes details such as hair color, eye color, and clothing. For example, the user might enter "a woman with long blonde hair, blue eyes, and casual clothing" as their preference.

[1025] Next, the device sends this input data to the server. This transmission is done using an AJAX request, and the data is sent to the server's API endpoint in JSON format.

[1026] The server inputs the received data into a generation AI model to generate character images. The generation AI model used is, for example, an illustration generation model that utilizes machine learning algorithms (such as OpenAI's DALL-E or Stable Diffusion). In this process, advanced machine learning techniques generate character images that conform to the user's specifications.

[1027] The generated character image is saved by the server to a database or cloud storage. After the saving process is complete, a URL for the image is issued and returned to the user's device.

[1028] The device uses the received image URL to display the character using AR (Augmented Reality) or VR (Virtual Reality) technology. Users can then view the character in an AR / VR environment. A chat window also appears on the device, allowing users to communicate with the character through this window.

[1029] When a user enters a text message using the terminal's chat window, they can, for example, type a question like "How are you today?". The server then passes the user's message to an emotion recognition engine for sentiment analysis. The emotion recognition engine can be something like Microsoft Azure Cognitive Services' Sentiment Analysis.

[1030] Once the sentiment analysis results are available, they are passed to a generative AI to generate an appropriate response. For example, a generative AI model such as OpenAI's GPT-3 is used to generate a natural response that corresponds to the user's question and emotional state. If the user asks, "How are you today?" and the sentiment recognition engine identifies that the user is feeling a little anxious, the generative AI will generate a response such as, "I'm a little busy today, but I'm okay. How about you?"

[1031] The generated response is input by the server into a speech synthesis system and produced as an audio file. The text is converted to speech using APIs such as the Google Text-to-Speech API. This audio file is sent to the user's device and played back. The user can then hear the character's response in audio form.

[1032] Examples of specific cases and prompt statements

[1033] As a concrete example, imagine a scenario where a user creates a female character with long blonde hair, blue eyes, and casual clothing, and then asks the character, "How are you today?"

[1034] Examples of prompt statements are as follows:

[1035] Please create a female character with long blonde hair, blue eyes, and casual clothing. When the user asks this character "How are you today?", please generate a natural response to the question and play it back using speech synthesis.

[1036] This system allows users to quickly generate their desired character and engage in natural, emotion-responsive conversations with it. By combining an emotion recognition engine and generative AI, the character's responses become more human-like and take the user's emotions into consideration.

[1037] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1038] Step 1: The user enters the request.

[1039] The user uses their device to enter details about the character's appearance into a web form. This includes "hair color," "eye color," and "clothing." For example, the user might enter "a woman with long blonde hair, blue eyes, and casual clothing." This generates the requested data.

[1040] Input: Character details (hair color, eye color, clothing, etc.)

[1041] Output: Generated request data (JSON format)

[1042] Step 2: The device sends the request data to the server.

[1043] The device sends the generated request data to the server's API endpoint using an AJAX request. In this process, the data is sent to the server in JSON format.

[1044] Input: Request data (JSON format)

[1045] Output: Request to send data to the server

[1046] Step 3: The server receives the data and inputs it into the generated AI model.

[1047] The server analyzes the received data and inputs it into the generative AI model. The generative AI model generates character images based on user specifications. For example, OpenAI's DALL-E or Stable Diffusion may be used.

[1048] Input: Request data received by the server

[1049] Output: Generated character image data

[1050] Step 4: The server saves the generated character image.

[1051] The server saves the generated character image to a database or cloud storage. At this time, a URL for the saved image is generated.

[1052] Input: Generated character image data

[1053] Output: URL of the saved image

[1054] Step 5: The device receives the image URL and displays it using AR / VR technology.

[1055] The device receives an image URL sent from the server and displays it using an AR / VR application. This allows the user to visually experience the generated character.

[1056] Input: Image URL returned from the server

[1057] Output: Character display in AR / VR environments

[1058] Step 6: The user speaks to the character.

[1059] The user uses the chat window on their device to type a message to the character. For example, they might type, "How are you today?" This generates a text message.

[1060] Input: Message from the user

[1061] Output: Generated text message

[1062] Step 7: The server passes the message to the sentiment recognition engine for analysis.

[1063] The server receives the user's message and passes it to the sentiment recognition engine for analysis. The sentiment recognition engine (for example, Microsoft Azure Cognitive Services' Sentiment Analysis) analyzes the sentiment of the message.

[1064] Input: Text message from the user

[1065] Output: Emotion analysis results (emotional state)

[1066] Step 8: The server generates a response for the generative AI.

[1067] The server passes the data, along with the sentiment analysis results, to a generative AI, which then generates an appropriate response. This results in a response in natural language. For example, OpenAI's GPT-3 is used.

[1068] Input: Sentiment analysis results, text messages from the user

[1069] Output: Generated natural language response message

[1070] Step 9: The server synthesizes the response message into speech and sends it to the terminal.

[1071] The server inputs the generated response message into a speech synthesis model to produce an audio file. This audio file is sent to the terminal and played back by the user. For example, the Google Text-to-Speech API is used.

[1072] Input: Generated natural language response message

[1073] Output: Audio file, sent to the device.

[1074] The above outlines the specific processing flow within this system's program.

[1075] (Application Example 2)

[1076] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1077] Online content distribution services require systems that can provide dynamic and emotionally resonant character interactions linked to the content users are viewing. Furthermore, these systems need to acquire information about the content users are viewing and adaptively generate responses based on that information, thereby providing deeper engagement and satisfaction.

[1078] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving user input data, means for generating an image based on the received data, means for storing the generated image, means for displaying the stored image, means for receiving voice or text messages from the user, means for generating a response based on the received message, means for converting the generated response into voice, means for acquiring information on the content being viewed, and means for adjusting the response based on the acquired information. This enables natural and emotionally responsive dialogue linked to the content being viewed.

[1079] "Input data" refers to information provided by the user, such as the appearance, characteristics, and messages of the character.

[1080] "Generative artificial intelligence (AI)" refers to advanced machine learning algorithms that analyze user input data and generate character images based on that analysis.

[1081] "Image generation means" refers to a device or software that generates character images based on received data using generative artificial intelligence (AI).

[1082] "Image storage means" refers to a function for saving the generated character images to a database or cloud storage.

[1083] "Image display means" refers to the function that displays saved character images on the user's device.

[1084] "Message receiving means" refers to a device or software that receives voice or text messages from a user.

[1085] "Response generation means" refers to a device or software for generating an appropriate dialogue response based on a message received from the user and the emotion recognition result.

[1086] "Voice conversion means" refers to a speech synthesis system that generates the generated response as an audio file.

[1087] "Content information acquisition means" refers to APIs and databases used by users to obtain information about the content they are currently viewing.

[1088] "Response adjustment means" refers to a device or software for adaptively adjusting the response based on acquired content information.

[1089] This invention is a system that generates an ideal character based on user requests and provides it with emotion recognition and dialogue capabilities. Specifically, the server and the user's terminal work together to perform the following processes.

[1090] 1. User request input

[1091] The user uses their device to enter details about the character's appearance. This input is done through a web form, and attribute information such as "hair color," "eye color," and "clothing" is entered into the form. For example, the user might enter a request such as "a woman with blonde hair, blue eyes, and casual clothing."

[1092] 2. Sending input data

[1093] The terminal receives user input data and sends it to the server. An AJAX request is used to send the input data to the server-side API endpoint. The data is sent as pairs of form field names and their values.

[1094] 3. Illustration generation

[1095] The server analyzes the received data and uses generative artificial intelligence (AI) to generate character images based on that data. For example, it generates character images that meet specifications such as "blonde hair, blue eyes, and casual clothing." Advanced machine learning algorithms are used for this purpose.

[1096] 4. Saving the generated illustration

[1097] The server saves the generated character illustration to a database or cloud storage. Once the saving process is complete, a URL for the generated image is issued. This URL is returned to the user, who can later use this URL to view the image.

[1098] 5. AR / VR display

[1099] The device displays the character using the received image URL. This character is visually displayed using AR or VR technology, and the user can view this displayed character. A chat window also appears, allowing the user to talk to the character.

[1100] 6. The user speaks to the character.

[1101] The user enters messages using the chat window on their device. The user types voice or text messages to the character and clicks the "Send" button. For example, the user could type the question, "How are you today?"

[1102] 7. Emotion analysis using an emotion recognition engine

[1103] The server receives the user's message and passes it to the emotion recognition engine for emotion analysis. The emotion recognition engine identifies the emotional state (e.g., joy, sadness, anger, etc.) from the user's message. This analysis result is then passed to the generative AI in the next step.

[1104] 8. Response generation using generative AI

[1105] The server passes the received message and emotion recognition results to the generative AI, which generates an appropriate response. This response is natural, depending on the user's question and emotional state. For example, if the user asks "How's today?" and the emotion recognition engine identifies that the user is feeling a little anxious, the generative AI will generate a response such as "I'm a little busy today, but I'm okay. How about you?"

[1106] 9. Content information acquisition and response adjustment

[1107] The server obtains information about the content the user is viewing using a content information acquisition mechanism. Based on this information, it adjusts the generated response to be more adaptive and relevant.

[1108] 10. Receiving and playing audio

[1109] The generated response is input into the speech synthesis system on the server and produced as an audio file. This audio file is sent to the user's terminal and played back. The user can hear the character's response in audio. For example, the audio might say, "I'm a little busy today, but it's okay. How about you?"

[1110] Adding specific examples

[1111] As an example of prompt text, the character generation prompt is "a woman with blonde hair, blue eyes, and casual clothing," and the user's emotion when asked "How are you today?" in the dialogue prompt is a subtle sense of unease.

[1112] This system allows for the rapid generation of characters desired by the user, enabling natural and emotionally responsive interactions with those characters. Furthermore, it enables adaptive responses linked to the content being viewed, thereby increasing user engagement.

[1113] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1114] Step 1:

[1115] The user enters details about the character's appearance, such as "hair color," "eye color," and "clothing," into a web form on their device. The input data might include information like "a woman with blonde hair, blue eyes, and casual clothing." This is the input data.

[1116] Step 2:

[1117] The terminal sends the entered data to the server using an AJAX request. The data is sent as pairs of form field names and their values. The server receives this data and uses it for parsing.

[1118] Step 3:

[1119] The server analyzes the received input data and feeds it into a generative artificial intelligence (AI) model. The AI ​​model uses machine learning algorithms to generate character images based on specifications such as "blonde hair," "blue eyes," and "casual clothing." This becomes the output of the image generation process.

[1120] Step 4:

[1121] The generated character images are saved by the server to a database or cloud storage. A URL for the generated image is issued during saving. This URL is the output data.

[1122] Step 5:

[1123] The device displays the character using an image URL received from the server. AR and VR technologies are used here, allowing users to visually appreciate the character. A chat window also appears, enabling users to interact with the character.

[1124] Step 6:

[1125] The user uses the chat window on their device to enter voice or text messages to the character. For example, they might type a question like, "How are you today?" This is the message input data.

[1126] Step 7:

[1127] The server receives messages from users and passes them to the emotion recognition engine. The emotion recognition engine analyzes the received messages and identifies the user's emotional state (e.g., joy, sadness, anger). This emotional state is the output data.

[1128] Step 8:

[1129] The server passes the emotion recognition results and received messages to the generative AI, which generates an appropriate dialogue response. For example, if a user asks "How are you today?" and emotion recognition reveals that the user is feeling anxious, the generative AI will generate a response such as "I'm a little busy today, but I'm okay. How about you?" This is the output data of the response.

[1130] Step 9:

[1131] The server retrieves information about the content the user is currently viewing, for example, using a content API. Based on this information, it evaluates whether the generated response is relevant to the content being viewed and adaptively adjusts the response. This is the content-dependent output data.

[1132] Step 10:

[1133] The server inputs the generated response into the speech synthesis system and produces it as an audio file. This audio file is sent to the terminal, allowing the user to hear the character's response. For example, the voice might say, "I'm a little busy today, but it's okay. How about you?" This is the final output data.

[1134] In this process, a system operates that enables users to create ideal characters based on their requests and engage in natural, emotionally responsive conversations.

[1135] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1136] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1137] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1138] [Fourth Embodiment]

[1139] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1140] As shown in Figure 7, the 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.

[1141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1143] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1146] The controlled 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1147] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1148] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1149] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1150] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1151] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1152] This system generates an ideal character based on user requests and gives that character the ability to converse. The system's operation will be explained in detail, along with concrete examples.

[1153] User request input

[1154] Users use their devices to enter details about the character's appearance. This input is done through a web form, where attribute information such as "hair color," "eye color," and "clothing" is entered. For example, a user might enter a request such as "a woman with long blonde hair, blue eyes, and casual clothing."

[1155] Sending input data

[1156] The device sends user input data to the server. The data is sent using an AJAX request and received at the server-side API endpoint.

[1157] Illustration generation

[1158] The server analyzes the received data and provides it to an AI illustration generation model to generate character illustrations. This model uses a high-performance machine learning algorithm to generate character images based on the user's requests.

[1159] Saving the generated illustration

[1160] The generated character illustrations are saved by the server to a database or cloud storage. This saving process generates a URL for the character image, which is then returned to the user.

[1161] AR / VR display

[1162] The device uses the received image URL to display the character in AR or VR. Users can view and interact with the character through this display. For example, a user can use their smartphone to display the character in AR mode.

[1163] The user speaks to the character.

[1164] Users use the chat window on their device to send voice or text messages to the character. For example, they might type a question like, "How are you today?"

[1165] Response generation by generative AI

[1166] The server receives user messages and passes them to a generative AI to generate appropriate responses. This AI model is designed to understand the context of the conversation and produce natural-sounding dialogue.

[1167] Receiving and playing back audio

[1168] The generated response is synthesized into speech on the server and sent to the user's device as an audio file. The device plays this audio file, allowing the user to hear the character's response aloud. For example, a response such as "Today is a good day! Thank you!" might be played aloud.

[1169] This system is designed to quickly generate a character of the user's choice and allow for natural conversations with that character. Furthermore, by utilizing generative AI, the system is designed to make the character's responses more human-like and natural.

[1170] The following describes the processing flow.

[1171] Step 1:

[1172] The user uses their device to enter details about the character's appearance. The input fields provide a form where users can enter attributes such as "hair color," "eye color," and "clothing." The user fills in the desired attributes in these fields and clicks the "Submit" button.

[1173] Step 2:

[1174] The device collects user input data and sends it to the server. An AJAX request is used to send the input data to the server-side API endpoint. The data is sent as pairs of form field names and their values.

[1175] Step 3:

[1176] The server analyzes the received data and inputs it into an AI illustration generation model. The model uses advanced machine learning algorithms to generate character images based on user specifications. For example, it generates character images that conform to specifications such as "long blonde hair," "blue eyes," and "casual clothing."

[1177] Step 4:

[1178] The server saves the generated character illustration to a database or cloud storage. Once the saving process is complete, a URL for the generated image is issued. This URL is returned to the user, who can then use it to view the image later.

[1179] Step 5:

[1180] The device displays a character using an image URL it receives. The character is visually displayed using AR or VR technology, and the user can view this displayed character. A chat window also appears, allowing the user to talk to the character.

[1181] Step 6:

[1182] The user enters a message using the chat window on their device. The user types a voice or text message to the character and clicks the "Send" button. For example, they can type a question like, "How are you today?"

[1183] Step 7:

[1184] The terminal sends the entered message to the server. The sent message is received and parsed at the server's API endpoint. This received data is then passed to the generative AI in the next step.

[1185] Step 8:

[1186] The server passes the received message to the generative AI, which generates an appropriate response. The AI ​​generates an appropriate answer in text format to the user's question. For example, it might generate a response like, "Today is a good day! Thank you!" in text format.

[1187] Step 9:

[1188] The server inputs the generated response into the speech synthesis system and generates an audio file. The speech synthesis system creates natural-sounding speech based on the generated text. This audio file is then sent to the user in the next step.

[1189] Step 10:

[1190] The device receives an audio file generated from the server. The audio file is played on the user's device, and the user can hear the character's response in audio. For example, the audio might say, "Today is a good day! Thank you!"

[1191] The above is the specific processing flow of this system. This allows users to generate their desired character and engage in natural conversations with that character.

[1192] (Example 1)

[1193] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1194] Conventional character generation systems have struggled to quickly generate the ideal character desired by the user and to engage in natural dialogue. Furthermore, the lack of functionality to display the generated character in augmented reality (AR) or virtual reality (VR) resulted in a limited user experience. Additionally, the generation of responses to voice or text messages from the user was often unnatural, resulting in a poor dialogue experience.

[1195] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1196] In this invention, the server includes means for receiving user input data, means for generating an image based on the received data, means for storing the generated image, means for displaying the generated image in augmented reality or virtual reality, means for receiving voice or text messages from the user, means for generating a response based on the received message, and means for converting the generated response into voice. This enables the user to quickly generate an ideal character and engage in natural conversation with the character displayed in augmented reality or virtual reality.

[1197] "Means for receiving user input data" refers to a mechanism for sending information entered by a user via a terminal to a server, and for the server to receive that information.

[1198] "Means for generating images based on received data" refers to a mechanism that uses machine learning to generate character images based on user input data received.

[1199] "Means for saving generated images" refers to a mechanism for saving generated character images to a database or cloud storage.

[1200] "Means for displaying saved images" refers to a mechanism for displaying images on a device using the URL of the saved image.

[1201] "Means for receiving voice or text messages from users" refers to a mechanism for a server to receive voice or text messages sent by a user via a terminal.

[1202] "Means for generating a response based on a received message" refers to a mechanism for analyzing a received message and generating an appropriate response.

[1203] "Means for converting generated responses into speech" refers to a mechanism for converting generated text responses into speech data using speech synthesis technology.

[1204] "Means for displaying generated images in augmented reality or virtual reality" refers to a mechanism for displaying generated character images in real space or virtual space using AR or VR technology.

[1205] This invention is a system that generates an ideal character based on user requests and gives that character interactive capabilities. The user inputs details about the character's appearance using a terminal, and the character is generated based on this input information. The specific operation of this system is described below.

[1206] The system will be implemented using the following key hardware and software:

[1207] Hardware: User terminals (PCs and smartphones), and servers that provide image generation and interaction functions.

[1208] Software: Web forms, AJAX requests, AI illustration generation models, generative AI models, speech synthesis technology, databases, cloud storage

[1209] The specific steps are as follows:

[1210] User request input

[1211] Users enter details about their character's appearance using a web form on their device. For example, information such as "Hair color: Blonde," "Eye color: Blue," and "Clothing: Casual." This information is entered directly into the web form, and clicking the submit button proceeds to the next step.

[1212] Sending input data

[1213] The device uses AJAX requests to send user-entered information to the server. This data is sent in JSON format. For example, an AJAX request sends a POST request to the " / api / generate-character" endpoint.

[1214] Illustration generation

[1215] The server analyzes the received data and generates character illustrations using an AI illustration generation model (e.g., a Stable Diffusion model). The server analyzes the data using a script written in Python and generates illustrations using a high-performance machine learning algorithm.

[1216] Saving the generated illustration

[1217] The generated character illustrations are uploaded to cloud storage (e.g., Amazon S3) by the server, and a URL is generated for them. The URL is also stored in a database (e.g., MySQL or PostgreSQL) so that it can be retrieved later.

[1218] AR / VR display

[1219] The device uses ARCore or ARKit to display the received image URL in augmented reality. This allows the user to see the character in the real world through their smartphone.

[1220] The user speaks to the character.

[1221] Users send voice or text messages to the character using the chat window on their device. For example, they might type "How are you today?" and click the send button.

[1222] Response generation by generative AI

[1223] The server receives user messages and passes them to a generative AI model (e.g., GPT-3) to generate appropriate responses. This AI model is designed to understand the context of the conversation and generate natural-sounding dialogue.

[1224] Receiving and playing back audio

[1225] The generated response is converted into an audio file using speech synthesis technology (e.g., Amazon Polly) and sent to the user's device. The device plays this audio file, allowing the user to hear the character's response aloud.

[1226] As a concrete example, a user enters "a woman with long blonde hair, blue eyes, and casual clothing" into a web form on their device and submits it. The server generates a character illustration and returns the saved URL to the user. The user then displays the character in AR mode on their smartphone and texts, "How's your day?" The server uses a generative AI to generate a response, "It's a good day! Thank you!" and sends the audio file to the device. The device then plays the audio response and delivers it to the user.

[1227] Example of a prompt:

[1228] "Please create a character with blonde hair, blue eyes, and casual clothing."

[1229] "Please tell me how to display a character in AR mode and how to talk to the character."

[1230] "Generate a response when you ask a character, 'How are you today?'"

[1231] In this way, the system enables users to quickly generate their ideal character and engage in natural interactions with the character displayed in augmented reality or virtual reality.

[1232] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1233] Step 1: The user enters the character attributes.

[1234] Users enter details about their character's appearance using a web form on their device. Specifically, they fill in information such as "hair color," "eye color," and "clothing," and then click the submit button. This input prepares the user's requests as data.

[1235] Input: User input of character attributes (e.g., "Hair color: Blonde", "Eye color: Blue", "Clothing: Casual")

[1236] Output: Preparing for an AJAX request

[1237] Step 2: Submit the input data

[1238] The terminal uses AJAX requests to send user-entered data to the server. This ensures that the user's requests are received by the server in a specific format (JSON format).

[1239] Input: User input data (JSON format)

[1240] Output: Request to send data to the server

[1241] Step 3: The server receives and analyzes the data.

[1242] The server parses the received JSON data and extracts the character's attributes. For example, it might parse and extract information such as "Hair color: Blonde," "Eye color: Blue," and "Clothing: Casual."

[1243] Input: User input data (JSON format)

[1244] Output: Analyzed character attributes

[1245] Step 4: Generating character illustrations

[1246] The server provides the analyzed data to an AI illustration generation model (e.g., a Stable Diffusion model) to generate character illustrations. A script written in Python is used to call the machine learning algorithm and generate the illustrations.

[1247] Input: Analyzed character attributes

[1248] Output: Generated character illustration

[1249] Step 5: Save the generated illustration

[1250] The server uploads the generated character illustration to cloud storage (e.g., Amazon S3) and generates an image URL. This URL is stored in the database.

[1251] Input: Generated character illustration

[1252] Output: Image URL

[1253] Step 6: Return the image URL

[1254] The server sends the generated image URL back to the user's device. This allows the user to easily access the character image.

[1255] Input: Image URL

[1256] Output: URL returned to the user's terminal

[1257] Step 7: AR / VR Display

[1258] The device uses the received image URL to display the character in augmented reality (AR) or virtual reality (VR). ARKit or ARCore is used to display the character in real space.

[1259] Input: Image URL

[1260] Output: AR / VR display on the device

[1261] Step 8: The user speaks to the character.

[1262] Users send voice or text messages to the character using the chat window on their device. For example, they might type a question like "How are you today?" and click the send button.

[1263] Input: User's message (voice or text)

[1264] Output: Sending a message to the server

[1265] Step 9: Response generation by generative AI

[1266] The server receives the user's message and passes it to a generative AI model (e.g., GPT-3) to generate an appropriate response. The AI ​​model understands the context of the conversation and generates natural-sounding dialogue.

[1267] Input: User message

[1268] Output: Generated response text

[1269] Step 10: Audio generation and playback

[1270] The server converts the generated text response into an audio file using speech synthesis technology (e.g., Amazon Polly) and sends it to the user's device. The device then plays this audio file, allowing the user to hear the character's response aloud.

[1271] Input: Generated response text

[1272] Output: Generate and send audio files

[1273] In this way, specific actions and inputs / outputs are defined at each step, and the overall system flow is designed to function. This process allows users to quickly generate their desired character and engage in natural interactions with the character displayed in augmented reality or virtual reality.

[1274] (Application Example 1)

[1275] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1276] There is a need to improve the efficiency and accuracy of factory workers, as well as to rapidly train new employees. Furthermore, it is necessary to reduce work errors and improve safety by providing real-time visual displays and audio guidance of work procedures. To address these challenges, an effective support system utilizing smart devices is required.

[1277] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1278] In this invention, the server includes means for receiving user input data, means for generating images based on the received data, means for storing the generated images, means for displaying the stored images, means for receiving voice or text messages from the user, means for generating responses based on the received messages, means for converting the generated responses into voice, means for displaying work instructions using augmented reality (AR), and means for providing the generated responses and work instructions in voice or video. This enables workers to receive real-time visual and audio support, allowing them to perform work efficiently and accurately.

[1279] "Means for receiving user input data" refers to devices or software used to acquire data such as text, images, and audio entered by the user.

[1280] "Means for generating images based on received data" refers to devices or software that analyze data received from a user and create images accordingly.

[1281] "Means for saving generated images" refers to devices or software for storing generated image data in memory devices or cloud storage.

[1282] "Means for displaying saved images" refers to devices or software that allow users to visually confirm stored image data.

[1283] "Means for receiving voice or text messages from users" refers to devices or software for acquiring voice or text data sent by users.

[1284] "Means for generating a response based on a received message" refers to devices or software that analyze a message received from a user and create a corresponding response.

[1285] "Means for converting generated responses into speech" refers to devices or software for converting responses created in text format into speech data.

[1286] "Means for displaying work instructions using augmented reality (AR)" refer to devices or software that overlay virtual information onto a real-world environment to provide work instructions.

[1287] "Means for providing generated responses and work instructions in audio or video" refers to devices or software for communicating generated responses and work instructions to the user via audio or video.

[1288] This system generates assistant characters to support factory workers, thereby improving work efficiency and accuracy. A detailed description of this invention follows.

[1289] 1. System Overview

[1290] The system receives user input data and generates character images based on that data. The generated characters provide work instructions using AR displays and respond appropriately to the user with audio and video. It also receives audio and text messages from the user and generates responses based on them. The main hardware and software used, as well as specific processing steps, are described below.

[1291] 2. Hardware and software to be used

[1292] Hardware:

[1293] High-performance server

[1294] Smart Glasses

[1295] Audio output devices (speakers, earphones)

[1296] software:

[1297] Flask web application framework: Server-side processing and API provisioning

[1298] GPT-Neo Generative AI Model: Text Generation and Response Generation

[1299] PIL (Python Imaging Library): Character image generation

[1300] AR display module: Displaying work instructions in augmented reality

[1301] Text-to-speech engine (e.g., Google Text-to-Speech): Converts text into speech.

[1302] 3. Data processing and data calculation

[1303] When the server receives user input data, it analyzes it and generates a character image. Using PIL (Personal Information Leaflet), it creates an image that reflects attributes such as hair color, eye color, and clothing specified by the user. The generated image is stored in cloud storage, and a URL for display is generated.

[1304] Next, the server analyzes the user's voice and text messages and generates an appropriate response using the GPT-Neo generative AI model. The generated response is then converted into voice data by a speech synthesis engine and sent to the user's device.

[1305] Users can receive work instructions via AR display through smart glasses. This allows 3D models and instructions to be overlaid in real time on the work area, providing visual and audio support.

[1306] 4. Specific Examples and Prompts

[1307] Specific example:

[1308] When a worker puts on smart glasses and asks, "What's the next assembly step?", the character responds, "Next, attach part A, and then secure part B with screws," and uses augmented reality to show the attachment locations of the parts. Furthermore, it provides voice guidance, saying, "Please attach part A in this position."

[1309] Example of a prompt:

[1310] User: What's the next step?

[1311] AI: In the next step, attach part A, and then secure part B with screws.

[1312] In this way, the system supports the user's work visually and audibly, enabling efficient and accurate work.

[1313] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1314] Step 1:

[1315] The server receives user input data. When a user enters attributes such as the character's hair color, eye color, and clothing on the smart glasses settings screen, this data is sent to the server via the device. The input data sent includes hair color "blonde," eye color "blue," and clothing "casual."

[1316] Step 2:

[1317] The server generates character images based on the received data. First, the server parses the received data and uses PIL (Python Imaging Library) to create an image based on the specified attributes. For example, it can generate an image of a female character with "blonde hair," "blue eyes," and "casual clothing."

[1318] Step 3:

[1319] The server saves the generated character image to cloud storage. During this saving process, the image data is uploaded to cloud storage, and a URL for display is generated. The output is the URL of the character image.

[1320] Step 4:

[1321] The device displays the character using an image URL received from the server. The character is then displayed on the smart glasses' screen, allowing the user to see it.

[1322] Step 5:

[1323] The user sends voice or text messages to the character through their device. For example, they might ask, "What's the next assembly step?" This message is then sent from the device to the server.

[1324] Step 6:

[1325] The server receives a message from the user and passes it to the GPT-Neo generation AI model to generate an appropriate response. It analyzes the received message "What is the next assembly step?" and generates the appropriate response "Next, attach part A, then screw in part B."

[1326] Step 7:

[1327] The server converts the generated response into speech. It passes the generated response text to the speech synthesis engine to generate speech data. The output is an audio file that says, "Next, attach part A, and then secure part B with screws."

[1328] Step 8:

[1329] The device plays the received audio data. The user can then hear the audio response through smart glasses or an audio output device.

[1330] Step 9:

[1331] The device displays work instructions using augmented reality (AR). It overlays 3D models and specific work procedures onto the existing real-world environment within the work area using AR. Users receive real-time visual guidance.

[1332] Step 10:

[1333] The user performs the following steps. The system will continue to provide additional responses and guidance as needed until the steps are completed.

[1334] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1335] This invention generates an ideal character based on user requests and gives that character emotion recognition and dialogue capabilities. The system's operation will be explained in detail, along with specific examples.

[1336] User request input

[1337] Users use their devices to enter details about the character's appearance. This input is done through a web form, where attribute information such as "hair color," "eye color," and "clothing" is entered. For example, a user might enter a request such as "a woman with long blonde hair, blue eyes, and casual clothing."

[1338] Sending input data

[1339] The device collects user input data and sends it to the server. An AJAX request is used to send the input data to the server-side API endpoint. The data is sent as pairs of form field names and their values.

[1340] Illustration generation

[1341] The server analyzes the received data and inputs it into an AI illustration generation model. The model uses advanced machine learning algorithms to generate character images based on user specifications. For example, it generates character images that conform to specifications such as "long blonde hair," "blue eyes," and "casual clothing."

[1342] Saving the generated illustration

[1343] The server saves the generated character illustration to a database or cloud storage. Once the saving process is complete, a URL for the generated image is issued. This URL is returned to the user, who can then use it to view the image later.

[1344] AR / VR display

[1345] The device displays a character using an image URL it receives. The character is visually displayed using AR or VR technology, and the user can view this displayed character. A chat window also appears, allowing the user to talk to the character.

[1346] The user speaks to the character.

[1347] The user enters messages using the chat window on their device. The user types voice or text messages to the character and clicks the "Send" button. For example, they might type the question, "How are you today?"

[1348] Emotion analysis using an emotion recognition engine

[1349] The server receives the user's message and passes it to the emotion recognition engine for emotion analysis. The emotion recognition engine identifies the emotional state (e.g., joy, sadness, anger, etc.) from the user's message. This analysis result is then passed to the generative AI in the next step.

[1350] Response generation by generative AI

[1351] The server passes the received message and emotion recognition results to the generative AI, which then generates an appropriate response. The AI ​​generates a natural response based on the user's question and emotional state. For example, if the user asks "How's today?" and the emotion recognition engine identifies that the user is feeling a little anxious, the generative AI will generate a response such as "I'm a little busy today, but I'm okay. How about you?"

[1352] Receiving and playing back audio

[1353] The generated response is input into the speech synthesis system on the server and produced as an audio file. This audio file is sent to the user's terminal and played back. The user can hear the character's response in audio. For example, the audio might say, "I'm a little busy today, but it's okay. How about you?"

[1354] This system is designed to quickly generate a character of the user's choice and allow for natural, emotion-responsive interaction with that character. By incorporating an emotion recognition engine, the system is designed to make the character's responses more human-like and considerate of the user's emotions.

[1355] The following describes the processing flow.

[1356] Step 1:

[1357] The user uses their device to enter details about the character's appearance. The input fields provide a form where users can enter attributes such as "hair color," "eye color," and "clothing." The user fills in the desired attributes in these fields and clicks the "Submit" button.

[1358] Step 2:

[1359] The device collects user input data and sends it to the server. An AJAX request is used to send the input data to the server-side API endpoint. The data is sent as pairs of form field names and their values.

[1360] Step 3:

[1361] The server analyzes the received data and inputs it into an AI illustration generation model. The model uses advanced machine learning algorithms to generate character images based on user specifications. For example, it generates character images that conform to specifications such as "long blonde hair," "blue eyes," and "casual clothing."

[1362] Step 4:

[1363] The server saves the generated character illustration to a database or cloud storage. Once the saving process is complete, a URL for the generated image is issued. This URL is returned to the user, who can then use it to view the image later.

[1364] Step 5:

[1365] The device displays a character using an image URL it receives. The character is visually displayed using AR or VR technology, and the user can view this displayed character. A chat window also appears, allowing the user to talk to the character.

[1366] Step 6:

[1367] The user enters a message using the chat window on their device. The user types a voice or text message to the character and clicks the "Send" button. For example, they can type a question like, "How are you today?"

[1368] Step 7:

[1369] The device sends the entered message to the server. The sent message is received and parsed at the server's API endpoint. This received data is then passed to the emotion recognition engine in the next step.

[1370] Step 8:

[1371] The server passes the user's message to the emotion recognition engine, which analyzes the emotion. The emotion recognition engine identifies the emotional state (e.g., joy, sadness, anger) from the user's message. For example, it uses speech analysis or text analysis to detect the user's emotions in real time.

[1372] Step 9:

[1373] The server passes the emotion recognition results to the generative AI, which then generates an appropriate response based on the user's emotions. The AI ​​can generate natural responses depending on the user's questions and emotional state. For example, if the user asks "How's today?" and the AI ​​determines that the user is feeling anxious, it will generate a response such as "It's okay, I think you'll have a good day."

[1374] Step 10:

[1375] The server inputs the generated response into the speech synthesis system, which then generates an audio file. The speech synthesis system creates natural-sounding speech based on the generated text. For example, the generated response "It's okay, I think something good will happen today." is converted into an audio file.

[1376] Step 11:

[1377] The device receives an audio file generated from the server. The audio file is played on the user's device, and the user can hear the character's response in audio. For example, the audio might say, "Don't worry, I think something good will happen today."

[1378] This system is designed to generate a character of the user's choice and allow them to engage in emotionally-driven conversations with that character. By incorporating an emotion recognition engine, the system is designed to make the character's responses more human-like and considerate of the user's emotions.

[1379] (Example 2)

[1380] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1381] Conventional character generation systems have the problem of being time-consuming and often requiring a lot of manual work when generating characters based on user requests. Furthermore, the interaction experience with the generated characters is often unnatural, resulting in a limited user experience. This invention aims to provide a system that can quickly generate characters based on user input and enable natural, emotionally responsive interaction with those characters.

[1382] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving user request input, means for transmitting received data to the server, means for using a generative AI model that generates an image based on the received data, means for saving the generated image, means for issuing a URL for the saved image, means for displaying the image on the user terminal using AR / VR technology, means for receiving user messages, means for performing sentiment analysis on the received message using a sentiment recognition engine, means for generating a response based on the sentiment analysis, and means for converting the generated response into speech using a speech synthesis model. This makes it possible to quickly generate a character specified by the user and to have a natural conversation with that character.

[1383] "User" refers to a person who uses a system or an end-user.

[1384] "Request input" refers to the input of wishes and specifications that users provide to the system.

[1385] "Terminal" refers to a device used by a user, such as a computer, smartphone, or tablet.

[1386] "Data" refers to information, files, etc., generated based on the requested input.

[1387] A "server" refers to a remote computer system that receives user requests, processes them, and returns the results.

[1388] A "generative AI model" refers to an algorithm or program that uses artificial intelligence technology to generate character images based on specific prompts.

[1389] "Saving" refers to the act of storing generated data or images in a database or cloud storage.

[1390] "URL" stands for Uniform Resource Locator, and refers to an address used to point to a resource on the web.

[1391] "AR / VR technology" is a general term for Augmented Reality and Virtual Reality technologies, referring to technologies that provide users with visually and experientially rich interfaces.

[1392] A "message" refers to text or audio information that a user inputs into the system.

[1393] An "emotion recognition engine" refers to software or algorithms that analyze and identify a user's emotional state from their messages.

[1394] "Response" refers to the content of the system's reply to a user's message.

[1395] A "speech synthesis model" refers to an algorithm or program used to convert text data into speech data.

[1396] This invention is a system that generates an ideal character based on user requests and gives that character emotion recognition and dialogue capabilities. The main components of the system and their specific operation will be described below.

[1397] First, the user uses their device to enter details about the character's appearance into a web form. This includes details such as hair color, eye color, and clothing. For example, the user might enter "a woman with long blonde hair, blue eyes, and casual clothing" as their preference.

[1398] Next, the device sends this input data to the server. This transmission is done using an AJAX request, and the data is sent to the server's API endpoint in JSON format.

[1399] The server inputs the received data into a generation AI model to generate character images. The generation AI model used is, for example, an illustration generation model that utilizes machine learning algorithms (such as OpenAI's DALL-E or Stable Diffusion). In this process, advanced machine learning techniques generate character images that conform to the user's specifications.

[1400] The generated character image is saved by the server to a database or cloud storage. After the saving process is complete, a URL for the image is issued and returned to the user's device.

[1401] The device uses the received image URL to display the character using AR (Augmented Reality) or VR (Virtual Reality) technology. Users can then view the character in an AR / VR environment. A chat window also appears on the device, allowing users to communicate with the character through this window.

[1402] When a user enters a text message using the terminal's chat window, they can, for example, type a question like "How are you today?". The server then passes the user's message to an emotion recognition engine for sentiment analysis. The emotion recognition engine can be something like Microsoft Azure Cognitive Services' Sentiment Analysis.

[1403] Once the sentiment analysis results are available, they are passed to a generative AI to generate an appropriate response. For example, a generative AI model such as OpenAI's GPT-3 is used to generate a natural response that corresponds to the user's question and emotional state. If the user asks, "How are you today?" and the sentiment recognition engine identifies that the user is feeling a little anxious, the generative AI will generate a response such as, "I'm a little busy today, but I'm okay. How about you?"

[1404] The generated response is input by the server into a speech synthesis system and produced as an audio file. The text is converted to speech using APIs such as the Google Text-to-Speech API. This audio file is sent to the user's device and played back. The user can then hear the character's response in audio form.

[1405] Examples of specific cases and prompt statements

[1406] As a concrete example, imagine a scenario where a user creates a female character with long blonde hair, blue eyes, and casual clothing, and then asks the character, "How are you today?"

[1407] Examples of prompt statements are as follows:

[1408] Please create a female character with long blonde hair, blue eyes, and casual clothing. When the user asks this character "How are you today?", please generate a natural response to the question and play it back using speech synthesis.

[1409] This system allows users to quickly generate their desired character and engage in natural, emotion-responsive conversations with it. By combining an emotion recognition engine and generative AI, the character's responses become more human-like and take the user's emotions into consideration.

[1410] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1411] Step 1: The user enters the request.

[1412] The user uses their device to enter details about the character's appearance into a web form. This includes "hair color," "eye color," and "clothing." For example, the user might enter "a woman with long blonde hair, blue eyes, and casual clothing." This generates the requested data.

[1413] Input: Character details (hair color, eye color, clothing, etc.)

[1414] Output: Generated request data (JSON format)

[1415] Step 2: The device sends the request data to the server.

[1416] The device sends the generated request data to the server's API endpoint using an AJAX request. In this process, the data is sent to the server in JSON format.

[1417] Input: Request data (JSON format)

[1418] Output: Request to send data to the server

[1419] Step 3: The server receives the data and inputs it into the generated AI model.

[1420] The server analyzes the received data and inputs it into the generative AI model. The generative AI model generates character images based on user specifications. For example, OpenAI's DALL-E or Stable Diffusion may be used.

[1421] Input: Request data received by the server

[1422] Output: Generated character image data

[1423] Step 4: The server saves the generated character image.

[1424] The server saves the generated character image to a database or cloud storage. At this time, a URL for the saved image is generated.

[1425] Input: Generated character image data

[1426] Output: URL of the saved image

[1427] Step 5: The device receives the image URL and displays it using AR / VR technology.

[1428] The device receives an image URL sent from the server and displays it using an AR / VR application. This allows the user to visually experience the generated character.

[1429] Input: Image URL returned from the server

[1430] Output: Character display in AR / VR environments

[1431] Step 6: The user speaks to the character.

[1432] The user uses the chat window on their device to type a message to the character. For example, they might type, "How are you today?" This generates a text message.

[1433] Input: Message from the user

[1434] Output: Generated text message

[1435] Step 7: The server passes the message to the sentiment recognition engine for analysis.

[1436] The server receives the user's message and passes it to the sentiment recognition engine for analysis. The sentiment recognition engine (for example, Microsoft Azure Cognitive Services' Sentiment Analysis) analyzes the sentiment of the message.

[1437] Input: Text message from the user

[1438] Output: Emotion analysis results (emotional state)

[1439] Step 8: The server generates a response for the generative AI.

[1440] The server passes the data, along with the sentiment analysis results, to a generative AI, which then generates an appropriate response. This results in a response in natural language. For example, OpenAI's GPT-3 is used.

[1441] Input: Sentiment analysis results, text messages from the user

[1442] Output: Generated natural language response message

[1443] Step 9: The server synthesizes the response message into speech and sends it to the terminal.

[1444] The server inputs the generated response message into a speech synthesis model to produce an audio file. This audio file is sent to the terminal and played back by the user. For example, the Google Text-to-Speech API is used.

[1445] Input: Generated natural language response message

[1446] Output: Audio file, sent to the device.

[1447] The above outlines the specific processing flow within this system's program.

[1448] (Application Example 2)

[1449] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1450] Online content distribution services require systems that can provide dynamic and emotionally resonant character interactions linked to the content users are viewing. Furthermore, these systems need to acquire information about the content users are viewing and adaptively generate responses based on that information, thereby providing deeper engagement and satisfaction.

[1451] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving user input data, means for generating an image based on the received data, means for storing the generated image, means for displaying the stored image, means for receiving voice or text messages from the user, means for generating a response based on the received message, means for converting the generated response into voice, means for acquiring information on the content being viewed, and means for adjusting the response based on the acquired information. This enables natural and emotionally responsive dialogue linked to the content being viewed.

[1452] "Input data" refers to information provided by the user, such as the appearance, characteristics, and messages of the character.

[1453] "Generative artificial intelligence (AI)" refers to advanced machine learning algorithms that analyze user input data and generate character images based on that analysis.

[1454] "Image generation means" refers to a device or software that generates character images based on received data using generative artificial intelligence (AI).

[1455] "Image storage means" refers to a function for saving the generated character images to a database or cloud storage.

[1456] "Image display means" refers to the function that displays saved character images on the user's device.

[1457] "Message receiving means" refers to a device or software that receives voice or text messages from a user.

[1458] "Response generation means" refers to a device or software for generating an appropriate dialogue response based on a message received from the user and the emotion recognition result.

[1459] "Voice conversion means" refers to a speech synthesis system that generates the generated response as an audio file.

[1460] "Content information acquisition means" refers to APIs and databases used by users to obtain information about the content they are currently viewing.

[1461] "Response adjustment means" refers to a device or software for adaptively adjusting the response based on acquired content information.

[1462] This invention is a system that generates an ideal character based on user requests and provides it with emotion recognition and dialogue capabilities. Specifically, the server and the user's terminal work together to perform the following processes.

[1463] 1. User request input

[1464] The user uses their device to enter details about the character's appearance. This input is done through a web form, and attribute information such as "hair color," "eye color," and "clothing" is entered into the form. For example, the user might enter a request such as "a woman with blonde hair, blue eyes, and casual clothing."

[1465] 2. Sending input data

[1466] The terminal receives user input data and sends it to the server. An AJAX request is used to send the input data to the server-side API endpoint. The data is sent as pairs of form field names and their values.

[1467] 3. Illustration generation

[1468] The server analyzes the received data and uses generative artificial intelligence (AI) to generate character images based on that data. For example, it generates character images that meet specifications such as "blonde hair, blue eyes, and casual clothing." Advanced machine learning algorithms are used for this purpose.

[1469] 4. Saving the generated illustration

[1470] The server saves the generated character illustration to a database or cloud storage. Once the saving process is complete, a URL for the generated image is issued. This URL is returned to the user, who can later use this URL to view the image.

[1471] 5. AR / VR display

[1472] The device displays the character using the received image URL. This character is visually displayed using AR or VR technology, and the user can view this displayed character. A chat window also appears, allowing the user to talk to the character.

[1473] 6. The user speaks to the character.

[1474] The user enters messages using the chat window on their device. The user types voice or text messages to the character and clicks the "Send" button. For example, the user could type the question, "How are you today?"

[1475] 7. Emotion analysis using an emotion recognition engine

[1476] The server receives the user's message and passes it to the emotion recognition engine for emotion analysis. The emotion recognition engine identifies the emotional state (e.g., joy, sadness, anger, etc.) from the user's message. This analysis result is then passed to the generative AI in the next step.

[1477] 8. Response generation using generative AI

[1478] The server passes the received message and emotion recognition results to the generative AI, which generates an appropriate response. This response is natural, depending on the user's question and emotional state. For example, if the user asks "How's today?" and the emotion recognition engine identifies that the user is feeling a little anxious, the generative AI will generate a response such as "I'm a little busy today, but I'm okay. How about you?"

[1479] 9. Content information acquisition and response adjustment

[1480] The server obtains information about the content the user is viewing using a content information acquisition mechanism. Based on this information, it adjusts the generated response to be more adaptive and relevant.

[1481] 10. Receiving and playing audio

[1482] The generated response is input into the speech synthesis system on the server and produced as an audio file. This audio file is sent to the user's terminal and played back. The user can hear the character's response in audio. For example, the audio might say, "I'm a little busy today, but it's okay. How about you?"

[1483] Adding specific examples

[1484] As an example of prompt text, the character generation prompt is "a woman with blonde hair, blue eyes, and casual clothing," and the user's emotion when asked "How are you today?" in the dialogue prompt is a subtle sense of unease.

[1485] This system allows for the rapid generation of characters desired by the user, enabling natural and emotionally responsive interactions with those characters. Furthermore, it enables adaptive responses linked to the content being viewed, thereby increasing user engagement.

[1486] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1487] Step 1:

[1488] The user enters details about the character's appearance, such as "hair color," "eye color," and "clothing," into a web form on their device. The input data might include information like "a woman with blonde hair, blue eyes, and casual clothing." This is the input data.

[1489] Step 2:

[1490] The terminal sends the entered data to the server using an AJAX request. The data is sent as pairs of form field names and their values. The server receives this data and uses it for parsing.

[1491] Step 3:

[1492] The server analyzes the received input data and feeds it into a generative artificial intelligence (AI) model. The AI ​​model uses machine learning algorithms to generate character images based on specifications such as "blonde hair," "blue eyes," and "casual clothing." This becomes the output of the image generation process.

[1493] Step 4:

[1494] The generated character images are saved by the server to a database or cloud storage. A URL for the generated image is issued during saving. This URL is the output data.

[1495] Step 5:

[1496] The device displays the character using an image URL received from the server. AR and VR technologies are used here, allowing users to visually appreciate the character. A chat window also appears, enabling users to interact with the character.

[1497] Step 6:

[1498] The user uses the chat window on their device to enter voice or text messages to the character. For example, they might type a question like, "How are you today?" This is the message input data.

[1499] Step 7:

[1500] The server receives messages from users and passes them to the emotion recognition engine. The emotion recognition engine analyzes the received messages and identifies the user's emotional state (e.g., joy, sadness, anger). This emotional state is the output data.

[1501] Step 8:

[1502] The server passes the emotion recognition results and received messages to the generative AI, which generates an appropriate dialogue response. For example, if a user asks "How are you today?" and emotion recognition reveals that the user is feeling anxious, the generative AI will generate a response such as "I'm a little busy today, but I'm okay. How about you?" This is the output data of the response.

[1503] Step 9:

[1504] The server retrieves information about the content the user is currently viewing, for example, using a content API. Based on this information, it evaluates whether the generated response is relevant to the content being viewed and adaptively adjusts the response. This is the content-dependent output data.

[1505] Step 10:

[1506] The server inputs the generated response into the speech synthesis system and produces it as an audio file. This audio file is sent to the terminal, allowing the user to hear the character's response. For example, the voice might say, "I'm a little busy today, but it's okay. How about you?" This is the final output data.

[1507] In this process, a system operates that enables users to create ideal characters based on their requests and engage in natural, emotionally responsive conversations.

[1508] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1509] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1510] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1511] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1512] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1513] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1514] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1515] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1516] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1517] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1518] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1519] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1520] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1521] 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.

[1522] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1523] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1524] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1525] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1526] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1527] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1528] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[1529] The following is further disclosed regarding the embodiments described above.

[1530] (Claim 1)

[1531] A means for receiving user input data,

[1532] A means for generating an image based on received data,

[1533] A means of saving the generated image,

[1534] A means of displaying the saved image,

[1535] A means of receiving voice or text messages from users,

[1536] Means for generating a response based on a received message,

[1537] A means for converting the generated response into speech,

[1538] A system that includes this.

[1539] (Claim 2)

[1540] The system according to claim 1, comprising means of using generative artificial intelligence (AI) to analyze input data and generate character images.

[1541] (Claim 3)

[1542] The system according to claim 1, comprising means for synthesizing a generated response into speech based on interaction with a user.

[1543] "Example 1"

[1544] (Claim 1)

[1545] A means for receiving user input data,

[1546] A means for generating an image based on received data,

[1547] A means of saving the generated image,

[1548] A means of displaying the saved image,

[1549] A means of receiving voice or text messages from users,

[1550] Means for generating a response based on a received message,

[1551] A means for converting the generated response into speech,

[1552] A means for displaying the generated image in augmented reality or virtual reality,

[1553] A system that includes this.

[1554] (Claim 2)

[1555] The system according to claim 1, comprising means of using generative artificial intelligence (AI) to analyze input data and generate character images.

[1556] (Claim 3)

[1557] The system according to claim 1, comprising means for synthesizing a generated response into speech based on interaction with a user.

[1558] "Application Example 1"

[1559] (Claim 1)

[1560] A means for receiving user input data,

[1561] A means for generating an image based on received data,

[1562] A means of saving the generated image,

[1563] A means of displaying the saved image,

[1564] A means of receiving voice or text messages from users,

[1565] Means for generating a response based on a received message,

[1566] A means for converting the generated response into speech,

[1567] A means of displaying work instructions using augmented reality (AR),

[1568] Means for providing the generated responses and work instructions in audio or video,

[1569] A system that includes this.

[1570] (Claim 2)

[1571] The system according to claim 1, comprising means of using generative artificial intelligence (AI) to analyze input data and generate character images.

[1572] (Claim 3)

[1573] The system according to claim 1, comprising means for synthesizing a generated response into speech based on interaction with a user.

[1574] "Example 2 of combining an emotion engine"

[1575] (Claim 1)

[1576] A means for receiving user request input,

[1577] A means of sending the received data to the server,

[1578] A means of using a generative AI model that generates images based on received data,

[1579] A means of saving the generated image,

[1580] A means of issuing a URL for a saved image,

[1581] A means for displaying images using AR / VR technology on a user terminal,

[1582] A means of receiving user messages,

[1583] A means of analyzing the emotions of a received message using an emotion recognition engine,

[1584] A means for generating a response based on emotion analysis,

[1585] A means for converting the generated response into speech using a speech synthesis model,

[1586] A system that includes this.

[1587] (Claim 2)

[1588] The system according to claim 1, which uses generative artificial intelligence (AI) to analyze input data and generate character images.

[1589] (Claim 3)

[1590] The system according to claim 1, comprising means for generating a character image based on user input data, generating a response using an emotion recognition engine and a generative AI to enable interaction with the character, and synthesizing speech.

[1591] "Application example 2 when combining with an emotional engine"

[1592] (Claim 1)

[1593] A means for receiving user input data,

[1594] A means for generating an image based on received data,

[1595] A means of saving the generated image,

[1596] A means of displaying the saved image,

[1597] A means of receiving voice or text messages from users,

[1598] Means for generating a response based on a received message,

[1599] A means for converting the generated response into speech,

[1600] A means of obtaining information about the content being viewed,

[1601] Means for adjusting the response based on acquired information,

[1602] A system that includes this.

[1603] (Claim 2)

[1604] The system according to claim 1, comprising means of using generative artificial intelligence (AI) to analyze input data and generate character images.

[1605] (Claim 3)

[1606] The system according to claim 1, comprising means for synthesizing a generated response into speech based on interaction with a user. [Explanation of symbols]

[1607] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for receiving user input data, A means for generating an image based on received data, A means of saving the generated image, A means of displaying the saved image, A means of receiving voice or text messages from users, Means for generating a response based on a received message, A means for converting the generated response into speech, A system that includes this.

2. The system according to claim 1, comprising means of using generative artificial intelligence to analyze input data and generate character images.

3. The system according to claim 1, comprising means for synthesizing a generated response into speech based on interaction with a user.

Images