system

Abstract

We provide the system. [Solution] A means for acquiring and analyzing images and video data of pets, A generation means for generating a pet avatar using feature data extracted from the aforementioned image and video data, A display means that displays the generated avatar on the user's device and enables interaction, A control means that changes the behavior of an avatar in real time based on user input, A system that includes this.

Description

Technical Field

[0004] , ,

[0005] , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is 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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

[0006] "Image and video data" refers to digital material that includes visual and auditory information about pets, and is data that records the appearance and behavior of pets.

[0007] "Feature data" refers to information that represents characteristics of a pet's appearance, behavior, and voice, extracted from image and video data.

[0008] "Generative AI" is an artificial intelligence model designed to create output based on a specific purpose from input data, and in this invention, it is used to generate pet avatars.

[0009] An "avatar" is a digital representation that exists in the digital space, reproducing the characteristics of a real pet, and is a virtual entity that can interact with the user.

[0010] "User's device" refers to an electronic device that enables the display and interaction of avatars, and includes smartphones and tablets.

[0011] "Interaction" refers to actions and reactions in which a user directly interacts with an avatar, and is the process of triggering an avatar's response based on the user's input.

[0012] A "vector database" is a technical system for storing extracted feature data in numerical format and making it searchable and available for use. In this invention, it is used to manage pet characteristic information.

[0013] "Feedback" refers to the opinions and evaluations that users provide about the system's mechanisms and avatar behavior, and is information used to improve the system. [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] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of the 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, the 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, the 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, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[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 invention is implemented by first having the user send images and video data of their pet from their own device to a server. The user inputs visual and auditory information of the pet via an application, which the server receives.

[0036] The server is responsible for analyzing the received data. Image recognition and voice analysis technologies are used for the analysis to clearly extract features such as the pet's appearance and behavior. This generates feature data, which is then stored in a vector database.

[0037] Next, the server uses a generative AI to generate a 3D avatar of the pet based on the stored feature data. The server then adds animations of the pet's movements and sounds to the avatar to enhance its realism.

[0038] The generated avatar is sent from the server to the user's device and displayed on the device using a dedicated application. The user can interact with this avatar through the device. The device detects user input such as instructions and gestures and sends this information to the server. Based on these instructions, the server controls the avatar's responses.

[0039] For example, when a user taps a pet avatar on their device, the device detects the action and sends it to the server. Based on the user's actions, the server controls the avatar to respond by making a specific sound or performing a joyful jump. In this way, the user can enjoy an experience as if they were interacting with their pet again.

[0040] Furthermore, the terminals and servers are equipped with interaction-based learning modules that continuously improve the avatar's movements and behavior by incorporating user feedback. This increases the naturalness and familiarity of interactions with the avatar, making it possible to provide a more human-like experience.

[0041] In this way, the present invention specifically realizes a system for mitigating pet loss and providing a virtual pet experience.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] Users upload images and videos of their pets to their device using a dedicated application. The device then sends the uploaded data to the server.

[0045] Step 2:

[0046] The server analyzes the received image and video data. This analysis uses image recognition technology to identify the pet's visual features and speech recognition technology to extract the pet's vocalizations.

[0047] Step 3:

[0048] The server vectorizes the feature data obtained as a result of the analysis and stores it in a vector database. Vectorization allows pet features to be stored digitally efficiently and in a searchable format.

[0049] Step 4:

[0050] The server uses the stored feature data to begin generating avatars using a generative AI. Based on the features, the server creates a 3D avatar of the pet and also generates animation data with movement and sound.

[0051] Step 5:

[0052] The server sends the generated avatar to the user's device. The device uses a dedicated application to display the avatar and enable interaction with the user.

[0053] Step 6:

[0054] The user interacts with the displayed avatar by inputting gestures and voice commands into the terminal. The terminal detects these inputs and sends them to the server in real time.

[0055] Step 7:

[0056] The server controls the avatar's movements based on user input. The server adjusts the avatar's animations so that it responds appropriately to user instructions, and displays them on the terminal.

[0057] Step 8:

[0058] The server collects user feedback and interaction history, and updates the machine learning model to improve the accuracy of the avatar's movements and responses. This continuous learning process makes the avatar's responses more natural.

[0059] (Example 1)

[0060] 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."

[0061] The challenge is to provide a system that intuitively recreates the appearance and movements of a pet as it was in life, offering comfort to users experiencing pet loss. Furthermore, it is necessary to enable users to interact naturally with the virtual representation through their own devices, and to allow for continuous technological improvement.

[0062] 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.

[0063] In this invention, the server includes means for acquiring and analyzing image and sound data captured by the user, generation means for generating a virtual representation using characteristic data extracted from the image and sound data, and display means for displaying the generated virtual representation on the user's display device and enabling interaction with the user. This makes it possible for the user to interact with a virtual representation of their pet in real time via their own terminal, enriching their experience.

[0064] "User" refers to a person who uses this system to interact with a virtual representation of a pet.

[0065] "Image and audio data" includes still images, videos, and audio data representing the pet's visual and auditory information.

[0066] "Characteristic data" refers to information extracted from image and audio data that describes the appearance, behavior, and vocal characteristics of a pet.

[0067] A "virtual representation" is a digital model that includes a three-dimensional model and animation of a pet, generated based on characteristic data.

[0068] A "display device" refers to a screen or display used to show virtual representations on a user's device.

[0069] "Interaction" refers to a series of processes in which a user provides input to a virtual representation, and the virtual representation responds accordingly.

[0070] A "server" refers to a computer system that performs image and sound data analysis, generates virtual representations, and transmits data to users.

[0071] To implement this invention, cooperation between the user, a terminal, and a server is necessary. The user uses a terminal such as a smartphone or tablet to collect visual and auditory information from their pet. These terminals are equipped with cameras and microphones, allowing them to collect images, videos, and audio data of the pet.

[0072] The device sends captured data to a server via a dedicated application. The application provides a user interface and automatically handles data organization and format conversion. During this process, data is transferred over the internet and encrypted for security purposes.

[0073] The server uses software such as "OpenCV" and "TENSORFLOW®" to analyze the received data. It analyzes the shape, color, and movements of pets using image recognition technology, and analyzes audio data using libraries such as "Librosa" and "PyDub". As a result of this analysis, characteristic data is extracted and stored in a vector database. This characteristic data is used to create prompt statements for the generative AI model.

[0074] As a concrete example, the prompt is input to the generation AI model in the form of "Generate a 3D avatar of the pet based on the extracted features." The generation AI model then creates a three-dimensional virtual representation of the pet based on this prompt. In this process, tools such as "Blender" and "Unity" are used to add animations and sounds, while also considering the details of the shape and color.

[0075] The generated virtual representation is sent from the server to the user's terminal. The user can visualize and interact with this virtual representation on the terminal's display using a dedicated application. Through this system, the user can once again enjoy a sensory interaction with their pet as it was when it was alive.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] Users collect images, videos, and audio of their pets using their devices. This involves taking photos of the pet with the camera and recording their sounds with the microphone. The input data consists of the pet's visual and auditory information, which is then incorporated into the device's application.

[0079] Step 2:

[0080] The terminal organizes the collected data for transmission to the server. The data is compressed and encrypted and transferred to the server via an internet connection. The input here is the raw data obtained from the user, and the output is the data converted into a format that the server can receive.

[0081] Step 3:

[0082] The server analyzes the received data. It uses "OpenCV" and "TensorFlow" for image recognition to identify the shape and color of pets. Audio data is analyzed using "Librosa" and "PyDub" to extract vocalization patterns. The input is organized image and audio data, and the output is extracted characteristic data.

[0083] Step 4:

[0084] The server stores the characteristic data in vector format in a database. This storage process prepares the basic information needed for later use in the generated AI model. The input is the analyzed characteristic data, and the output is the stored data as vector data.

[0085] Step 5:

[0086] The server generates a virtual representation of the pet using a generative AI model. It creates a prompt message, "Generate a 3D avatar of the pet based on the extracted features," and inputs it into the model. The generated virtual representation is output as a 3D model or animation.

[0087] Step 6:

[0088] The server uses virtual reality development tools such as Blender and Unity to add animations of movement and sound to the generated avatars. Specifically, pet movements and sounds are added, enabling more realistic representations. The input is the initial virtual representation output from the AI ​​model, and the output is the completed avatar with added movement and sound.

[0089] Step 7:

[0090] The server sends the completed virtual representation to the user's terminal. The virtual representation is displayed in a dedicated application on the terminal, making it interactive for the user. The input is an avatar with added movement and sound, and the output is a display that allows the user to visually interact with it.

[0091] (Application Example 1)

[0092] 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."

[0093] In modern society, many pet lovers are affected by pet loss, and the use of virtual reality technology to alleviate this is highly anticipated. Furthermore, when purchasing pet-related products, users often find it difficult to determine if a product is actually suitable for their pet, which negatively impacts the purchasing experience. Therefore, there is a need to provide a realistic and interactive virtual pet experience through pet avatars to support the assessment of product suitability.

[0094] 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.

[0095] In this invention, the server includes means for acquiring and analyzing images and video information of pets, generation means for generating virtual animals using feature information extracted from the images and video information, and display means for displaying the generated virtual animals on the user's information processing device and enabling two-way communication. As a result, users can alleviate pet loss and make judgments about product suitability through virtual experiences via pet avatars.

[0096] "Pet image and video information" refers to visual and auditory data of animals owned by the user, including still images and videos recorded in digital format.

[0097] A "virtual animal" refers to a digital avatar created on a computer, such as a 3D model or animation that reflects the characteristics of a specific pet.

[0098] A "user information processing device" refers to a device used for interacting with a pet avatar, and this includes smartphones, tablets, personal computers, etc.

[0099] "Two-way communication" refers to a function that allows information to be exchanged in real time between the user and the virtual animal, and in which the behavior and reactions of the virtual animal change according to the user's input.

[0100] "Virtual product testing" refers to the process of using pet avatars to check the usability and suitability of a product in a virtual reality space before purchasing it.

[0101] This invention is implemented by sending images and video information of pets from a user's terminal to a server. The terminal can be an information processing device such as a smartphone, tablet, or personal computer. The server analyzes the received image and video information and extracts characteristic information of the pet using image recognition technology and voice analysis technology. This process uses a programming language such as Python and machine learning frameworks such as TensorFlow or PyTorch.

[0102] Next, the server uses a generative AI model based on the extracted feature information to generate a virtual animal. The virtual animal is realistically rendered using a 3D engine such as Unity or Unreal Engine. This generated virtual animal is displayed on the user's device, allowing for real-time interaction. When the user interacts with the virtual animal through their device, this information is sent to the server, instantly changing the virtual animal's behavior. This real-time, two-way communication is highly interactive because it changes the virtual animal's response based on user input.

[0103] Furthermore, when users virtually try out pet-related products, the system visually displays a virtual animal trying out the product. This allows users to verify whether the product is actually suitable for their pet. All interaction data is stored in a vector-based database and used in subsequent learning processes. User feedback is continuously used to improve the virtual animal and its behavior.

[0104] As a concrete example, before a user purchases a pet bed, they can see how a virtual animal would relax in it. An example of a prompt message in this case would be, "Generate a pet avatar relaxing in this bed." This allows the user to virtually experience whether the product is actually suitable for their pet.

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] The user uses their device to acquire images and videos of their pet and sends this information to the server. The input consists of still images and video data related to the pet, and the output is data sent to the server. This process is achieved by capturing images and videos using the device's camera function and uploading them to the server via the network.

[0108] Step 2:

[0109] The server analyzes received image and video information to extract pet characteristic information. The input is image and video data sent by the user, and the output is data that describes the pet's characteristics in detail. This process is carried out using an image recognition algorithm and audio analysis technology programmed in Python, and feature extraction using a TensorFlow model.

[0110] Step 3:

[0111] The server uses a generative AI model to generate virtual animals based on extracted feature information. The input is pet feature information, and the output is a 3D virtual animal avatar. This process uses a deep learning model (e.g., GAN) to calculate the shape and movement of the virtual animal, and then uses Unity to render the specific 3D model.

[0112] Step 4:

[0113] The device displays a generated virtual animal to the user, allowing the user to interact with the avatar. The input is a 3D virtual animal avatar, and the output is a visual image displayed on the device screen. Here, UI design using Unity and the device's display technology are used to achieve real-time interactive display.

[0114] Step 5:

[0115] The user initiates a product trial using a virtual animal via a terminal and sends the information to the server. The input consists of information about the product the user wants to try and specific prompts, while the output is a trial scenario featuring the virtual animal and the product. This process is performed by the user interacting with the system using text input or voice commands, which the server then interprets to generate the virtual scenario.

[0116] Step 6:

[0117] The server instantly changes the behavior of the virtual animal based on user input and sends it to the terminal. Input consists of user actions and prompts, while output is the virtual animal with the updated behavior. In this process, the server analyzes user action data, updates the virtual animal's behavior in real time, and reflects it on the display.

[0118] Step 7:

[0119] Users observe how a virtual animal uses a product through their device and send feedback to the server. The input is feedback information, and the output is training data stored on the server. Here, users input their impressions and opinions in text format after their actions, which the server receives and uses to improve future algorithms.

[0120] 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.

[0121] This invention is a system that incorporates an emotion engine that recognizes the user's emotions and uses that information to adjust the behavior of a pet avatar. This system includes the user's terminal and server and is implemented via an application.

[0122] First, the user uploads images and video data of their pet to their device and sends it to the server. The server receives this data and performs analysis. The analysis is performed using image recognition and speech recognition technologies to extract characteristic data of the pet. Based on this, the server uses a generative AI to generate a 3D avatar of the pet and also creates animation data.

[0123] The generated avatar is displayed on the user's device. At this point, an emotion engine runs on the device and recognizes the user's emotional state by analyzing the user's voice, facial expressions, gestures, etc., in real time. The device sends the results of the emotion engine's analysis to the server, which uses that information to adjust the avatar's responses and actions.

[0124] For example, when a user speaks into the device, the emotion engine recognizes that the user is happy based on the tone of their voice and the content of their words. This emotion information is sent to the server, which controls the avatar to respond with actions or sounds that indicate happiness. Conversely, if the emotion engine determines that the user is depressed, the avatar is designed to perform comforting actions.

[0125] Furthermore, the emotion engine collects emotional data and interaction history, which are then analyzed on the server. Based on this analysis, the pet avatar's responses and actions are continuously improved, enabling the provision of appropriate interactions that match the user's emotions. This system allows users to have a more realistic interaction experience with their pets.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] The user uses a dedicated application to input images and video data of their pet into the device and send it to the server. The device then transfers the data to the server.

[0129] Step 2:

[0130] The server analyzes the received image and video data. This analysis uses image recognition technology to extract the pet's appearance and behavioral characteristics, and utilizes speech recognition technology to analyze the pet's vocalizations.

[0131] Step 3:

[0132] The server vectorizes the extracted feature data and stores it in a vector database. This storage makes the feature data efficiently searchable and available for use.

[0133] Step 4:

[0134] The server uses a generative AI to create a 3D avatar of the pet based on the stored feature data. The server then adds animations of movements and sounds associated with this avatar.

[0135] Step 5:

[0136] The generated avatar is sent from the server to the user's device. The device uses a dedicated application to display the avatar, creating an environment where the user can interact with it.

[0137] Step 6:

[0138] The device activates an emotion engine that analyzes the user's voice, gestures, and facial expressions in real time as they interact with the avatar, detecting the user's emotional state.

[0139] Step 7:

[0140] The analyzed user's emotional state is sent from the device to the server. The server uses this emotional information to control the avatar's responses and actions, setting appropriate reactions according to the user's emotions.

[0141] Step 8:

[0142] As a concrete example, when a user smiles and talks to their avatar, the emotion engine analyzes that emotion as "joy." The server then instructs the avatar to perform playful actions in response to the user's joy, and the avatar responds to the user with actions and sounds that express that joy.

[0143] Step 9:

[0144] The server collects and analyzes user emotion data and interaction history. This allows for continuous improvement of avatar behavior and responses, enabling more natural and emotion-appropriate interactions.

[0145] (Example 2)

[0146] 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".

[0147] To achieve realistic interaction with pets, not only is avatar generation necessary, but dynamic responses based on the user's emotions are also required. However, existing technologies struggle to accurately reflect user emotions in interactions, and in particular, real-time changes in avatar behavior are insufficient. Furthermore, there is a lack of data collection and analysis to individually tune the behavior of pet avatars, limiting the quality of the user experience.

[0148] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0149] In this invention, the server includes means for identifying the user's emotions in real time and analyzing pet image and video data; means for generating a three-dimensional pet model using an artificial intelligence model; and means for displaying the generated model and its movements on the user's device and analyzing and incorporating the user's emotional information. This enables real-time interaction that responds to the user's emotions and highly accurate, individually tuned avatar movements.

[0150] "User" refers to an individual who uses the system and is the entity that interacts with the pet avatar.

[0151] "Emotional recognition" is the process of analyzing input data such as the user's voice and facial expressions to determine the user's emotional state.

[0152] "Pet image and video data" refers to still images and dynamic video data that record the characteristics of the pet, and is the source data used to generate the avatar.

[0153] An "artificial intelligence model" is a computational model that learns specific patterns and features from data and uses those results to perform inference and generation.

[0154] A "three-dimensional model" refers to a digital representation of a pet avatar recreated in three-dimensional space, intended to provide visually realistic images.

[0155] A "device" refers to an electronic device used by the user to interact with and display their pet avatar.

[0156] "Interaction" refers to the dynamic exchange between the user and the pet avatar, and is a two-way communication that includes changes in the avatar's behavior in response to emotions.

[0157] "Real-time" refers to processing that is immediate and has the characteristic of responding instantly to user input.

[0158] To implement this invention, the user must first upload images and videos of their pet to their device and send them to the server. The device is configured to ensure that this data is reliably delivered to the server via technologies such as cloud storage or direct file transfer.

[0159] The server analyzes the received image and video data and extracts feature data. This process uses image recognition libraries (e.g., image processing libraries) and speech recognition technologies (e.g., automatic speech recognition APIs). After the feature data is extracted, the server uses a generative AI model to generate a 3D model of the pet and design the avatar's animation. Deep learning tools and 3D modeling software are utilized for this process.

[0160] The generated 3D model is sent to the user's device and displayed there. An emotion engine built into the device analyzes the user's voice and facial expressions in real time to recognize their emotional state. Specifically, the device's camera and microphone capture user input, which is then processed by a machine learning algorithm.

[0161] User emotion information is sent from the device to the server, which uses this information to adjust the avatar's responses and actions in real time. This enables natural avatar behavior that is appropriate to the user's situation. For example, if a user says "I'm happy today" to the device, the emotion engine recognizes the joy and sends that data to the server. The server then uses this information to make the avatar perform actions that indicate joy.

[0162] This system aims to enhance the virtual interaction experience with pets by facilitating real-time interactions that respond to the user's emotions.

[0163] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0164] Step 1:

[0165] The user selects images and videos of their pet using their device and uploads them to the server via the application. As input, the user specifies images and videos on the file selection screen and presses the upload button. This sends the image and video data to the server. Specifically, the device uses a file transfer protocol to deliver the data to the server.

[0166] Step 2:

[0167] The server acquires the received image and video data and begins analysis. The input data consists of uploaded images and videos of pets. The server uses an image recognition library to extract pet features from images (e.g., eye color, fur texture) and uses speech recognition technology to identify characteristic sounds from videos. The output is the pet feature data after this analysis. As a specific example, the server runs an image processing algorithm to recognize a particular color pattern.

[0168] Step 3:

[0169] The server uses the extracted feature data to generate a three-dimensional pet model using a generative AI model. The feature data is supplied to the AI ​​model as input. The AI ​​model utilizes deep learning techniques to generate a realistic three-dimensional avatar of the pet. The output consists of the generated three-dimensional model and its associated animation data. Specifically, the AI ​​model performs calculations to construct the pet avatar in a virtual space.

[0170] Step 4:

[0171] The server sends the generated 3D model and animation data to the user's terminal. The input data is the 3D data generated within the server. As output, this data is transferred to the user's terminal and loaded into the display system. Specifically, the server uses a high-speed data communication protocol to transfer the data to the terminal.

[0172] Step 5:

[0173] The device activates an emotion engine to analyze the user's voice, facial expressions, and other data in real time. The input data consists of the user's facial expressions and voice. Based on this data, the emotion engine performs an analysis to recognize the user's emotional state. The output is emotional information as a result of the analysis. Specifically, the device's camera and microphone capture the user's movements and send the information to the analysis algorithm.

[0174] Step 6:

[0175] The terminal sends acquired emotional information to the server. Emotional state data is sent to the server as input, and the server receives this data. The output is the avatar's response and actions, which are adjusted based on this emotional information. Specifically, the server analyzes the emotional information and dynamically sets the avatar's actions.

[0176] Step 7:

[0177] The server uses emotional information from the user to modify the responses and actions of the pet avatar in real time and send them back to the user. Emotional data is sent to the server as input. The output is an avatar with movements and sounds that correspond to the emotion. For example, if the user appears happy, the server will make the avatar perform actions that show happiness.

[0178] (Application Example 2)

[0179] 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 device 14 will be referred to as the "terminal."

[0180] Current virtual pet systems have limited user interaction and are insufficient in terms of dynamic emotional expression and behavioral adjustments based on emotional state. Furthermore, in storytelling-type content delivery, there is a need for technology that reflects user emotional feedback in real time. Additionally, it is necessary to improve entertainment value by providing more sophisticated personalized experiences through analysis of interaction history based on emotional state.

[0181] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0182] In this invention, the server includes a device for acquiring and analyzing visual and auditory information of a pet; a creation device for generating a virtual representation of the pet using feature information extracted from the visual and auditory information; a display device for displaying the generated virtual representation on the user's information terminal and enabling two-way interaction; and a device for recognizing the user's emotional state using an emotion analysis engine and adjusting the operation of the virtual representation according to the recognition result. This makes it possible to provide a virtual experience that responds flexibly to the user's emotions, improving content immersion and user satisfaction.

[0183] "Visual information" refers to data related to the user's or pet's vision, and is acquired as images or videos.

[0184] "Audio information" refers to data related to the voices of users or pets, and is information acquired as sounds or words.

[0185] "Characteristic information" refers to elements that represent the individuality and characteristics of a pet, extracted from visual and auditory information.

[0186] A "virtual representation" is a digital representation of a pet generated based on its characteristic information, and is used as an avatar or character.

[0187] A "creation device" is a device that generates virtual representations using feature information.

[0188] A "display device" is a device used to display a generated virtual representation on a user's information terminal.

[0189] An "emotion analysis engine" is an analytical technology that recognizes a user's emotional state from their voice, facial expressions, and other data.

[0190] An "information terminal" is a device, such as a smartphone or computer, that a user uses to interact with virtual representations.

[0191] To implement this invention, it is necessary to build a system in which a user's information terminal and a server in the cloud work together. First, the user uses an information terminal such as a smartphone or tablet to acquire visual and auditory information of their pet and sends this information to the server. The server extracts specific feature information based on the received information. This process uses image processing libraries such as OpenCV and speech recognition services such as Google Cloud Speech-to-Text.

[0192] On the server, a generative AI model is used with feature information to generate a virtual representation (avatar) of the pet. This virtual representation includes 3D models and animation data, and is generated by an AI model built with TensorFlow or PyTorch.

[0193] The generated virtual representation is then sent to the user's information terminal and displayed via a dedicated application. This application monitors the user's facial expressions and voice in real time and recognizes the user's emotional state using an emotion analysis engine installed in the terminal. Based on the recognized emotional state, the server dynamically adjusts the behavior and expression of the virtual representation.

[0194] To give a specific example, if the user is feeling down, the virtual representation is programmed to perform comforting actions. Also, if the system determines that the user is enjoying themselves, the virtual representation changes its behavior to express joy.

[0195] Examples of prompts to input into the generating AI model include, "What action should the pet avatar take when the user smiles?" and "How can the pet avatar make an expression that further captures the viewer's heart during the emotional moments of the story?" This makes it possible to further enhance the user experience.

[0196] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0197] Step 1:

[0198] The user's device acquires visual and auditory information from the pet using its camera and microphone. This input data is saved as image and audio files of the pet. This data is then transmitted from the device to the server.

[0199] Step 2:

[0200] The server receives visual and audio information transmitted from the terminal. To analyze the received data, image processing is performed using OpenCV, and speech recognition is performed using Google Cloud Speech-to-Text. Feature information is extracted using these methods and stored on the server.

[0201] Step 3:

[0202] The server generates a virtual representation using a generative AI model (TensorFlow or PyTorch) based on the extracted feature information. This generation process is achieved by inputting feature information into the model and outputting a 3D model and animation data of the virtual pet.

[0203] Step 4:

[0204] The generated virtual representation is sent from the server to the user's terminal. A dedicated application on the terminal receives this virtual representation and displays it on the terminal's display.

[0205] Step 5:

[0206] An emotion analysis engine installed on the user's device is activated, capturing the user's facial expressions and voice data in real time. This data is processed to identify the user's emotional state. The resulting emotional information is sent to the server with a short delay.

[0207] Step 6:

[0208] The server adjusts the movements and expressions of the virtual representation based on emotional information. It determines the action based on this prompt and updates the virtual representation. For example, it processes a command such as, "What action should the pet avatar take when the user smiles?"

[0209] Step 7:

[0210] Users can interact with virtual representations that have been re-tuned on their devices. The virtual representations respond in accordance with the user's emotional state, enhancing the entertainment value.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] [Second Embodiment]

[0215] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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.

[0220] 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).

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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".

[0227] This invention is implemented by first having the user send images and video data of their pet from their own device to a server. The user inputs visual and auditory information of the pet via an application, which the server receives.

[0228] The server is responsible for analyzing the received data. Image recognition and voice analysis technologies are used for the analysis to clearly extract features such as the pet's appearance and behavior. This generates feature data, which is then stored in a vector database.

[0229] Next, the server uses a generative AI to generate a 3D avatar of the pet based on the stored feature data. The server then adds animations of the pet's movements and sounds to the avatar to enhance its realism.

[0230] The generated avatar is sent from the server to the user's device and displayed on the device using a dedicated application. The user can interact with this avatar through the device. The device detects user input such as instructions and gestures and sends this information to the server. Based on these instructions, the server controls the avatar's responses.

[0231] For example, when a user taps a pet avatar on their device, the device detects the action and sends it to the server. Based on the user's actions, the server controls the avatar to respond by making a specific sound or performing a joyful jump. In this way, the user can enjoy an experience as if they were interacting with their pet again.

[0232] Furthermore, the terminals and servers are equipped with interaction-based learning modules that continuously improve the avatar's movements and behavior by incorporating user feedback. This increases the naturalness and familiarity of interactions with the avatar, making it possible to provide a more human-like experience.

[0233] In this way, the present invention specifically realizes a system for mitigating pet loss and providing a virtual pet experience.

[0234] The following describes the processing flow.

[0235] Step 1:

[0236] Users upload images and videos of their pets to their device using a dedicated application. The device then sends the uploaded data to the server.

[0237] Step 2:

[0238] The server analyzes the received image and video data. This analysis uses image recognition technology to identify the pet's visual features and speech recognition technology to extract the pet's vocalizations.

[0239] Step 3:

[0240] The server vectorizes the feature data obtained as a result of the analysis and stores it in a vector database. Vectorization allows pet features to be stored digitally efficiently and in a searchable format.

[0241] Step 4:

[0242] The server uses the stored feature data to begin generating avatars using a generative AI. Based on the features, the server creates a 3D avatar of the pet and also generates animation data with movement and sound.

[0243] Step 5:

[0244] The server sends the generated avatar to the user's device. The device uses a dedicated application to display the avatar and enable interaction with the user.

[0245] Step 6:

[0246] The user interacts with the displayed avatar by inputting gestures and voice commands into the terminal. The terminal detects these inputs and sends them to the server in real time.

[0247] Step 7:

[0248] The server controls the avatar's movements based on user input. The server adjusts the avatar's animations so that it responds appropriately to user instructions, and displays them on the terminal.

[0249] Step 8:

[0250] The server collects user feedback and interaction history, and updates the machine learning model to improve the accuracy of the avatar's movements and responses. This continuous learning process makes the avatar's responses more natural.

[0251] (Example 1)

[0252] 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."

[0253] The challenge is to provide a system that intuitively recreates the appearance and movements of a pet as it was in life, offering comfort to users experiencing pet loss. Furthermore, it is necessary to enable users to interact naturally with the virtual representation through their own devices, and to allow for continuous technological improvement.

[0254] 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.

[0255] In this invention, the server includes means for acquiring and analyzing image and sound data captured by the user, generation means for generating a virtual representation using characteristic data extracted from the image and sound data, and display means for displaying the generated virtual representation on the user's display device and enabling interaction with the user. This makes it possible for the user to interact with a virtual representation of their pet in real time via their own terminal, enriching their experience.

[0256] "User" refers to a person who uses this system to interact with a virtual representation of a pet.

[0257] "Image and audio data" includes still images, videos, and audio data representing the pet's visual and auditory information.

[0258] "Characteristic data" refers to information extracted from image and audio data that describes the appearance, behavior, and vocal characteristics of a pet.

[0259] A "virtual representation" is a digital model that includes a three-dimensional model and animation of a pet, generated based on characteristic data.

[0260] A "display device" refers to a screen or display used to show virtual representations on a user's device.

[0261] "Interaction" refers to a series of processes in which a user provides input to a virtual representation, and the virtual representation responds accordingly.

[0262] A "server" refers to a computer system that performs image and sound data analysis, generates virtual representations, and transmits data to users.

[0263] To implement this invention, cooperation between the user, a terminal, and a server is necessary. The user uses a terminal such as a smartphone or tablet to collect visual and auditory information from their pet. These terminals are equipped with cameras and microphones, allowing them to collect images, videos, and audio data of the pet.

[0264] The device sends captured data to a server via a dedicated application. The application provides a user interface and automatically handles data organization and format conversion. During this process, data is transferred over the internet and encrypted for security purposes.

[0265] The server uses software such as "OpenCV" and "TensorFlow" to analyze the received data. It analyzes the shape, color, and movements of pets using image recognition technology, and analyzes audio data using libraries such as "Librosa" and "PyDub". As a result of this analysis, characteristic data is extracted and stored in a vector database. This characteristic data is used to create prompt statements for the generative AI model.

[0266] As a concrete example, the prompt is input to the generation AI model in the form of "Generate a 3D avatar of the pet based on the extracted features." The generation AI model then creates a three-dimensional virtual representation of the pet based on this prompt. In this process, tools such as "Blender" and "Unity" are used to add animations and sounds, while also considering the details of the shape and color.

[0267] The generated virtual representation is sent from the server to the user's terminal. The user can visualize and interact with this virtual representation on the terminal's display using a dedicated application. Through this system, the user can once again enjoy a sensory interaction with their pet as it was when it was alive.

[0268] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0269] Step 1:

[0270] Users collect images, videos, and audio of their pets using their devices. This involves taking photos of the pet with the camera and recording their sounds with the microphone. The input data consists of the pet's visual and auditory information, which is then incorporated into the device's application.

[0271] Step 2:

[0272] The terminal organizes the collected data for transmission to the server. The data is compressed and encrypted and transferred to the server via an internet connection. The input here is the raw data obtained from the user, and the output is the data converted into a format that the server can receive.

[0273] Step 3:

[0274] The server analyzes the received data. It uses "OpenCV" and "TensorFlow" for image recognition to identify the shape and color of pets. Audio data is analyzed using "Librosa" and "PyDub" to extract vocalization patterns. The input is organized image and audio data, and the output is extracted characteristic data.

[0275] Step 4:

[0276] The server stores the characteristic data in vector format in a database. This storage process prepares the basic information needed for later use in the generated AI model. The input is the analyzed characteristic data, and the output is the stored data as vector data.

[0277] Step 5:

[0278] The server generates a virtual representation of the pet using a generative AI model. It creates a prompt message, "Generate a 3D avatar of the pet based on the extracted features," and inputs it into the model. The generated virtual representation is output as a 3D model or animation.

[0279] Step 6:

[0280] The server uses virtual reality development tools such as Blender and Unity to add animations of movement and sound to the generated avatars. Specifically, pet movements and sounds are added, enabling more realistic representations. The input is the initial virtual representation output from the AI ​​model, and the output is the completed avatar with added movement and sound.

[0281] Step 7:

[0282] The server sends the completed virtual representation to the user's terminal. The virtual representation is displayed in a dedicated application on the terminal, making it interactive for the user. The input is an avatar with added movement and sound, and the output is a display that allows the user to visually interact with it.

[0283] (Application Example 1)

[0284] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0285] In modern society, many pet lovers are affected by pet loss, and the use of virtual reality technology for alleviation is expected. Also, when purchasing pet-related products, it is difficult for users to determine whether the product actually fits the pet, which is one of the factors that impairs the purchasing experience. Therefore, it is required to provide a real and two-way virtual pet experience through a pet avatar and support the determination of product suitability.

[0286] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0287] In this invention, the server includes means for acquiring and analyzing pet image and video information, generating means for generating a virtual animal using the feature information extracted from the image and video information, and display means for displaying the generated virtual animal on the user's information processing device and enabling two-way communication. Thereby, the user can relieve pet loss and determine product suitability through a virtual experience through the pet avatar.

[0288] "Pet image and video information" refers to the visual and auditory data of the animal owned by the user, and includes still images and videos recorded in digital format.

[0289] "Virtual animal" is a digital avatar generated on a computer, and refers to a 3D model or animation that reflects the characteristics of a specific pet. <000091​​A "user information processing device" refers to a device used for interacting with a pet avatar, and this includes smartphones, tablets, personal computers, etc.

[0291] "Two-way communication" refers to a function that allows information to be exchanged in real time between the user and the virtual animal, and in which the behavior and reactions of the virtual animal change according to the user's input.

[0292] "Virtual product testing" refers to the process of using pet avatars to check the usability and suitability of a product in a virtual reality space before purchasing it.

[0293] This invention is implemented by sending images and video information of pets from a user's terminal to a server. The terminal can be an information processing device such as a smartphone, tablet, or personal computer. The server analyzes the received image and video information and extracts characteristic information of the pet using image recognition technology and voice analysis technology. This process uses a programming language such as Python and machine learning frameworks such as TensorFlow or PyTorch.

[0294] Next, the server uses a generative AI model based on the extracted feature information to generate a virtual animal. The virtual animal is realistically rendered using a 3D engine such as Unity or Unreal Engine. This generated virtual animal is displayed on the user's device, allowing for real-time interaction. When the user interacts with the virtual animal through their device, this information is sent to the server, instantly changing the virtual animal's behavior. This real-time, two-way communication is highly interactive because it changes the virtual animal's response based on user input.

[0295] Furthermore, when users virtually try out pet-related products, the system visually displays a virtual animal trying out the product. This allows users to verify whether the product is actually suitable for their pet. All interaction data is stored in a vector-based database and used in subsequent learning processes. User feedback is continuously used to improve the virtual animal and its behavior.

[0296] As a concrete example, before a user purchases a pet bed, they can see how a virtual animal would relax in it. An example of a prompt message in this case would be, "Generate a pet avatar relaxing in this bed." This allows the user to virtually experience whether the product is actually suitable for their pet.

[0297] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0298] Step 1:

[0299] The user uses their device to acquire images and videos of their pet and sends this information to the server. The input consists of still images and video data related to the pet, and the output is data sent to the server. This process is achieved by capturing images and videos using the device's camera function and uploading them to the server via the network.

[0300] Step 2:

[0301] The server analyzes received image and video information to extract pet characteristic information. The input is image and video data sent by the user, and the output is data that describes the pet's characteristics in detail. This process is carried out using an image recognition algorithm and audio analysis technology programmed in Python, and feature extraction using a TensorFlow model.

[0302] Step 3:

[0303] The server uses a generative AI model to generate a virtual animal based on the extracted feature information. The input is the feature information of the pet, and the output is a 3D virtual animal avatar. This operation calculates the shape and movement of the virtual animal using a deep learning model (e.g., GAN) and renders a specific 3D model using Unity.

[0304] Step 4:

[0305] The terminal displays the generated virtual animal to the user, enabling the user to interact with the avatar. The input is the 3D virtual animal avatar, and the output is the visual image displayed on the terminal screen. Here, real-time interactive display is performed using UI design by Unity and display technology within the terminal.

[0306] Step 5:

[0307] The user starts a product trial of the virtual animal through the terminal and sends the information to the server. The input is the information of the product the user wants to try and a specific prompt sentence, and the output is a trial scenario of the virtual animal and the product. This operation is performed by the user operating using text input or voice instructions, and the server interprets it to generate a virtual scenario.

[0308] Step 6:

[0309] Based on the user's input, the server immediately changes the movement of the virtual animal and sends it to the terminal. The input is the user's operation and the prompt sentence, and the output is the virtual animal with updated movement. In this process, the server analyzes the user's operation data, updates the behavior of the virtual animal in real time, and reflects it on the display.

[0310] Step 7:

[0311] Users observe how a virtual animal uses a product through their device and send feedback to the server. The input is feedback information, and the output is training data stored on the server. Here, users input their impressions and opinions in text format after their actions, which the server receives and uses to improve future algorithms.

[0312] 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.

[0313] This invention is a system that incorporates an emotion engine that recognizes the user's emotions and uses that information to adjust the behavior of a pet avatar. This system includes the user's terminal and server and is implemented via an application.

[0314] First, the user uploads images and video data of their pet to their device and sends it to the server. The server receives this data and performs analysis. The analysis is performed using image recognition and speech recognition technologies to extract characteristic data of the pet. Based on this, the server uses a generative AI to generate a 3D avatar of the pet and also creates animation data.

[0315] The generated avatar is displayed on the user's device. At this point, an emotion engine runs on the device and recognizes the user's emotional state by analyzing the user's voice, facial expressions, gestures, etc., in real time. The device sends the results of the emotion engine's analysis to the server, which uses that information to adjust the avatar's responses and actions.

[0316] For example, when a user speaks into the device, the emotion engine recognizes that the user is happy based on the tone of their voice and the content of their words. This emotion information is sent to the server, which controls the avatar to respond with actions or sounds that indicate happiness. Conversely, if the emotion engine determines that the user is depressed, the avatar is designed to perform comforting actions.

[0317] Furthermore, the emotion engine collects emotional data and interaction history, which are then analyzed on the server. Based on this analysis, the pet avatar's responses and actions are continuously improved, enabling the provision of appropriate interactions that match the user's emotions. This system allows users to have a more realistic interaction experience with their pets.

[0318] The following describes the processing flow.

[0319] Step 1:

[0320] The user uses a dedicated application to input images and video data of their pet into the device and send it to the server. The device then transfers the data to the server.

[0321] Step 2:

[0322] The server analyzes the received image and video data. This analysis uses image recognition technology to extract the pet's appearance and behavioral characteristics, and utilizes speech recognition technology to analyze the pet's vocalizations.

[0323] Step 3:

[0324] The server vectorizes the extracted feature data and stores it in a vector database. This storage makes the feature data efficiently searchable and available for use.

[0325] Step 4:

[0326] The server uses a generative AI to create a 3D avatar of the pet based on the stored feature data. The server then adds animations of movements and sounds associated with this avatar.

[0327] Step 5:

[0328] The generated avatar is sent from the server to the user's device. The device uses a dedicated application to display the avatar, creating an environment where the user can interact with it.

[0329] Step 6:

[0330] The device activates an emotion engine that analyzes the user's voice, gestures, and facial expressions in real time as they interact with the avatar, detecting the user's emotional state.

[0331] Step 7:

[0332] The analyzed user's emotional state is sent from the device to the server. The server uses this emotional information to control the avatar's responses and actions, setting appropriate reactions according to the user's emotions.

[0333] Step 8:

[0334] As a concrete example, when a user smiles and talks to their avatar, the emotion engine analyzes that emotion as "joy." The server then instructs the avatar to perform playful actions in response to the user's joy, and the avatar responds to the user with actions and sounds that express that joy.

[0335] Step 9:

[0336] The server collects and analyzes user emotion data and interaction history. This allows for continuous improvement of avatar behavior and responses, enabling more natural and emotion-appropriate interactions.

[0337] (Example 2)

[0338] 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".

[0339] To achieve realistic interaction with pets, not only is avatar generation necessary, but dynamic responses based on the user's emotions are also required. However, existing technologies struggle to accurately reflect user emotions in interactions, and in particular, real-time changes in avatar behavior are insufficient. Furthermore, there is a lack of data collection and analysis to individually tune the behavior of pet avatars, limiting the quality of the user experience.

[0340] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0341] In this invention, the server includes means for identifying the user's emotions in real time and analyzing pet image and video data; means for generating a three-dimensional pet model using an artificial intelligence model; and means for displaying the generated model and its movements on the user's device and analyzing and incorporating the user's emotional information. This enables real-time interaction that responds to the user's emotions and highly accurate, individually tuned avatar movements.

[0342] "User" refers to an individual who uses the system and is the entity that interacts with the pet avatar.

[0343] "Emotional recognition" is the process of analyzing input data such as the user's voice and facial expressions to determine the user's emotional state.

[0344] "Pet image and video data" refers to still images and dynamic video data that record the characteristics of the pet, and is the source data used to generate the avatar.

[0345] An "artificial intelligence model" is a computational model that learns specific patterns and features from data and uses those results to perform inference and generation.

[0346] A "three-dimensional model" refers to a digital representation of a pet avatar recreated in three-dimensional space, intended to provide visually realistic images.

[0347] A "device" refers to an electronic device used by the user to interact with and display their pet avatar.

[0348] "Interaction" refers to the dynamic exchange between the user and the pet avatar, and is a two-way communication that includes changes in the avatar's behavior in response to emotions.

[0349] "Real-time" refers to processing that is immediate and has the characteristic of responding instantly to user input.

[0350] To implement this invention, the user must first upload images and videos of their pet to their device and send them to the server. The device is configured to ensure that this data is reliably delivered to the server via technologies such as cloud storage or direct file transfer.

[0351] The server analyzes the received image and video data and extracts feature data. This process uses image recognition libraries (e.g., image processing libraries) and speech recognition technologies (e.g., automatic speech recognition APIs). After the feature data is extracted, the server uses a generative AI model to generate a 3D model of the pet and design the avatar's animation. Deep learning tools and 3D modeling software are utilized for this process.

[0352] The generated 3D model is sent to the user's device and displayed there. An emotion engine built into the device analyzes the user's voice and facial expressions in real time to recognize their emotional state. Specifically, the device's camera and microphone capture user input, which is then processed by a machine learning algorithm.

[0353] User emotion information is sent from the device to the server, which uses this information to adjust the avatar's responses and actions in real time. This enables natural avatar behavior that is appropriate to the user's situation. For example, if a user says "I'm happy today" to the device, the emotion engine recognizes the joy and sends that data to the server. The server then uses this information to make the avatar perform actions that indicate joy.

[0354] This system aims to enhance the virtual interaction experience with pets by facilitating real-time interactions that respond to the user's emotions.

[0355] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0356] Step 1:

[0357] The user selects images and videos of their pet using their device and uploads them to the server via the application. As input, the user specifies images and videos on the file selection screen and presses the upload button. This sends the image and video data to the server. Specifically, the device uses a file transfer protocol to deliver the data to the server.

[0358] Step 2:

[0359] The server acquires the received image and video data and begins analysis. The input data consists of uploaded images and videos of pets. The server uses an image recognition library to extract pet features from images (e.g., eye color, fur texture) and uses speech recognition technology to identify characteristic sounds from videos. The output is the pet feature data after this analysis. As a specific example, the server runs an image processing algorithm to recognize a particular color pattern.

[0360] Step 3:

[0361] The server uses the extracted feature data to generate a three-dimensional pet model using a generative AI model. The feature data is supplied to the AI ​​model as input. The AI ​​model utilizes deep learning techniques to generate a realistic three-dimensional avatar of the pet. The output consists of the generated three-dimensional model and its associated animation data. Specifically, the AI ​​model performs calculations to construct the pet avatar in a virtual space.

[0362] Step 4:

[0363] The server sends the generated 3D model and animation data to the user's terminal. The input data is the 3D data generated within the server. As output, this data is transferred to the user's terminal and loaded into the display system. Specifically, the server uses a high-speed data communication protocol to transfer the data to the terminal.

[0364] Step 5:

[0365] The device activates an emotion engine to analyze the user's voice, facial expressions, and other data in real time. The input data consists of the user's facial expressions and voice. Based on this data, the emotion engine performs an analysis to recognize the user's emotional state. The output is emotional information as a result of the analysis. Specifically, the device's camera and microphone capture the user's movements and send the information to the analysis algorithm.

[0366] Step 6:

[0367] The terminal sends acquired emotional information to the server. Emotional state data is sent to the server as input, and the server receives this data. The output is the avatar's response and actions, which are adjusted based on this emotional information. Specifically, the server analyzes the emotional information and dynamically sets the avatar's actions.

[0368] Step 7:

[0369] The server uses emotional information from the user to modify the responses and actions of the pet avatar in real time and send them back to the user. Emotional data is sent to the server as input. The output is an avatar with movements and sounds that correspond to the emotion. For example, if the user appears happy, the server will make the avatar perform actions that show happiness.

[0370] (Application Example 2)

[0371] 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 as the "terminal".

[0372] Current virtual pet systems have limited user interaction and are insufficient in terms of dynamic emotional expression and behavioral adjustments based on emotional state. Furthermore, in storytelling-type content delivery, there is a need for technology that reflects user emotional feedback in real time. Additionally, it is necessary to improve entertainment value by providing more sophisticated personalized experiences through analysis of interaction history based on emotional state.

[0373] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0374] In this invention, the server includes a device for acquiring and analyzing visual and auditory information of a pet; a creation device for generating a virtual representation of the pet using feature information extracted from the visual and auditory information; a display device for displaying the generated virtual representation on the user's information terminal and enabling two-way interaction; and a device for recognizing the user's emotional state using an emotion analysis engine and adjusting the operation of the virtual representation according to the recognition result. This makes it possible to provide a virtual experience that responds flexibly to the user's emotions, improving content immersion and user satisfaction.

[0375] "Visual information" refers to data related to the user's or pet's vision, and is acquired as images or videos.

[0376] "Audio information" refers to data related to the voices of users or pets, and is information acquired as sounds or words.

[0377] "Characteristic information" refers to elements that represent the individuality and characteristics of a pet, extracted from visual and auditory information.

[0378] A "virtual representation" is a digital representation of a pet generated based on its characteristic information, and is used as an avatar or character.

[0379] A "creation device" is a device that generates virtual representations using feature information.

[0380] A "display device" is a device used to display a generated virtual representation on a user's information terminal.

[0381] An "emotion analysis engine" is an analytical technology that recognizes a user's emotional state from their voice, facial expressions, and other data.

[0382] An "information terminal" is a device, such as a smartphone or computer, that a user uses to interact with virtual representations.

[0383] To implement this invention, it is necessary to build a system in which a user's information terminal and a server in the cloud work together. First, the user uses an information terminal such as a smartphone or tablet to acquire visual and auditory information of their pet and sends this information to the server. The server extracts specific feature information based on the received information. This process uses image processing libraries such as OpenCV and speech recognition services such as Google Cloud Speech-to-Text.

[0384] On the server, a generative AI model is used with feature information to generate a virtual representation (avatar) of the pet. This virtual representation includes 3D models and animation data, and is generated by an AI model built with TensorFlow or PyTorch.

[0385] The generated virtual representation is then sent to the user's information terminal and displayed via a dedicated application. This application monitors the user's facial expressions and voice in real time and recognizes the user's emotional state using an emotion analysis engine installed in the terminal. Based on the recognized emotional state, the server dynamically adjusts the behavior and expression of the virtual representation.

[0386] To give a specific example, if the user is feeling down, the virtual representation is programmed to perform comforting actions. Also, if the system determines that the user is enjoying themselves, the virtual representation changes its behavior to express joy.

[0387] Examples of prompts to input into the generating AI model include, "What action should the pet avatar take when the user smiles?" and "How can the pet avatar make an expression that further captures the viewer's heart during the emotional moments of the story?" This makes it possible to further enhance the user experience.

[0388] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0389] Step 1:

[0390] The user's device acquires visual and auditory information from the pet using its camera and microphone. This input data is saved as image and audio files of the pet. This data is then transmitted from the device to the server.

[0391] Step 2:

[0392] The server receives visual and audio information transmitted from the terminal. To analyze the received data, image processing is performed using OpenCV, and speech recognition is performed using Google Cloud Speech-to-Text. Feature information is extracted using these methods and stored on the server.

[0393] Step 3:

[0394] The server generates a virtual representation using a generative AI model (TensorFlow or PyTorch) based on the extracted feature information. This generation process is achieved by inputting feature information into the model and outputting a 3D model and animation data of the virtual pet.

[0395] Step 4:

[0396] The generated virtual representation is sent from the server to the user's terminal. A dedicated application on the terminal receives this virtual representation and displays it on the terminal's display.

[0397] Step 5:

[0398] An emotion analysis engine installed on the user's device is activated, capturing the user's facial expressions and voice data in real time. This data is processed to identify the user's emotional state. The resulting emotional information is sent to the server with a short delay.

[0399] Step 6:

[0400] The server adjusts the movements and expressions of the virtual representation based on emotional information. It determines the action based on this prompt and updates the virtual representation. For example, it processes a command such as, "What action should the pet avatar take when the user smiles?"

[0401] Step 7:

[0402] Users can interact with virtual representations that have been re-tuned on their devices. The virtual representations respond in accordance with the user's emotional state, enhancing the entertainment value.

[0403] 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.

[0404] 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.

[0405] 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.

[0406] [Third Embodiment]

[0407] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0408] 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.

[0409] 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).

[0410] 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.

[0411] 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.

[0412] 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).

[0413] 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.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] 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".

[0419] This invention is implemented by first having the user send images and video data of their pet from their own device to a server. The user inputs visual and auditory information of the pet via an application, which the server receives.

[0420] The server is responsible for analyzing the received data. Image recognition and voice analysis technologies are used for the analysis to clearly extract features such as the pet's appearance and behavior. This generates feature data, which is then stored in a vector database.

[0421] Next, the server uses a generative AI to generate a 3D avatar of the pet based on the stored feature data. The server then adds animations of the pet's movements and sounds to the avatar to enhance its realism.

[0422] The generated avatar is sent from the server to the user's device and displayed on the device using a dedicated application. The user can interact with this avatar through the device. The device detects user input such as instructions and gestures and sends this information to the server. Based on these instructions, the server controls the avatar's responses.

[0423] For example, when a user taps a pet avatar on their device, the device detects the action and sends it to the server. Based on the user's actions, the server controls the avatar to respond by making a specific sound or performing a joyful jump. In this way, the user can enjoy an experience as if they were interacting with their pet again.

[0424] Furthermore, the terminals and servers are equipped with interaction-based learning modules that continuously improve the avatar's movements and behavior by incorporating user feedback. This increases the naturalness and familiarity of interactions with the avatar, making it possible to provide a more human-like experience.

[0425] In this way, the present invention specifically realizes a system for mitigating pet loss and providing a virtual pet experience.

[0426] The following describes the processing flow.

[0427] Step 1:

[0428] Users upload images and videos of their pets to their device using a dedicated application. The device then sends the uploaded data to the server.

[0429] Step 2:

[0430] The server analyzes the received image and video data. This analysis uses image recognition technology to identify the pet's visual features and speech recognition technology to extract the pet's vocalizations.

[0431] Step 3:

[0432] The server vectorizes the feature data obtained as a result of the analysis and stores it in a vector database. Vectorization allows pet features to be stored digitally efficiently and in a searchable format.

[0433] Step 4:

[0434] The server uses the stored feature data to begin generating avatars using a generative AI. Based on the features, the server creates a 3D avatar of the pet and also generates animation data with movement and sound.

[0435] Step 5:

[0436] The server sends the generated avatar to the user's device. The device uses a dedicated application to display the avatar and enable interaction with the user.

[0437] Step 6:

[0438] The user interacts with the displayed avatar by inputting gestures and voice commands into the terminal. The terminal detects these inputs and sends them to the server in real time.

[0439] Step 7:

[0440] The server controls the avatar's movements based on user input. The server adjusts the avatar's animations so that it responds appropriately to user instructions, and displays them on the terminal.

[0441] Step 8:

[0442] The server collects user feedback and interaction history, and updates the machine learning model to improve the accuracy of the avatar's movements and responses. This continuous learning process makes the avatar's responses more natural.

[0443] (Example 1)

[0444] 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."

[0445] The challenge is to provide a system that intuitively recreates the appearance and movements of a pet as it was in life, offering comfort to users experiencing pet loss. Furthermore, it is necessary to enable users to interact naturally with the virtual representation through their own devices, and to allow for continuous technological improvement.

[0446] 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.

[0447] In this invention, the server includes means for acquiring and analyzing image and sound data captured by the user, generation means for generating a virtual representation using characteristic data extracted from the image and sound data, and display means for displaying the generated virtual representation on the user's display device and enabling interaction with the user. This makes it possible for the user to interact with a virtual representation of their pet in real time via their own terminal, enriching their experience.

[0448] "User" refers to a person who uses this system to interact with a virtual representation of a pet.

[0449] "Image and audio data" includes still images, videos, and audio data representing the pet's visual and auditory information.

[0450] "Characteristic data" refers to information extracted from image and audio data that describes the appearance, behavior, and vocal characteristics of a pet.

[0451] A "virtual representation" is a digital model that includes a three-dimensional model and animation of a pet, generated based on characteristic data.

[0452] A "display device" refers to a screen or display used to show virtual representations on a user's device.

[0453] "Interaction" refers to a series of processes in which a user provides input to a virtual representation, and the virtual representation responds accordingly.

[0454] A "server" refers to a computer system that performs image and sound data analysis, generates virtual representations, and transmits data to users.

[0455] To implement this invention, cooperation between the user, a terminal, and a server is necessary. The user uses a terminal such as a smartphone or tablet to collect visual and auditory information from their pet. These terminals are equipped with cameras and microphones, allowing them to collect images, videos, and audio data of the pet.

[0456] The device sends captured data to a server via a dedicated application. The application provides a user interface and automatically handles data organization and format conversion. During this process, data is transferred over the internet and encrypted for security purposes.

[0457] The server uses software such as "OpenCV" and "TensorFlow" to analyze the received data. It analyzes the shape, color, and movements of pets using image recognition technology, and analyzes audio data using libraries such as "Librosa" and "PyDub". As a result of this analysis, characteristic data is extracted and stored in a vector database. This characteristic data is used to create prompt statements for the generative AI model.

[0458] As a concrete example, the prompt is input to the generation AI model in the form of "Generate a 3D avatar of the pet based on the extracted features." The generation AI model then creates a three-dimensional virtual representation of the pet based on this prompt. In this process, tools such as "Blender" and "Unity" are used to add animations and sounds, while also considering the details of the shape and color.

[0459] The generated virtual representation is sent from the server to the user's terminal. The user can visualize and interact with this virtual representation on the terminal's display using a dedicated application. Through this system, the user can once again enjoy a sensory interaction with their pet as it was when it was alive.

[0460] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0461] Step 1:

[0462] Users collect images, videos, and audio of their pets using their devices. This involves taking photos of the pet with the camera and recording their sounds with the microphone. The input data consists of the pet's visual and auditory information, which is then incorporated into the device's application.

[0463] Step 2:

[0464] The terminal organizes the collected data for transmission to the server. The data is compressed and encrypted and transferred to the server via an internet connection. The input here is the raw data obtained from the user, and the output is the data converted into a format that the server can receive.

[0465] Step 3:

[0466] The server analyzes the received data. It uses "OpenCV" and "TensorFlow" for image recognition to identify the shape and color of pets. Audio data is analyzed using "Librosa" and "PyDub" to extract vocalization patterns. The input is organized image and audio data, and the output is extracted characteristic data.

[0467] Step 4:

[0468] The server stores the characteristic data in vector format in a database. This storage process prepares the basic information needed for later use in the generated AI model. The input is the analyzed characteristic data, and the output is the stored data as vector data.

[0469] Step 5:

[0470] The server generates a virtual representation of the pet using a generative AI model. It creates a prompt message, "Generate a 3D avatar of the pet based on the extracted features," and inputs it into the model. The generated virtual representation is output as a 3D model or animation.

[0471] Step 6:

[0472] The server uses virtual reality development tools such as Blender and Unity to add animations of movement and sound to the generated avatars. Specifically, pet movements and sounds are added, enabling more realistic representations. The input is the initial virtual representation output from the AI ​​model, and the output is the completed avatar with added movement and sound.

[0473] Step 7:

[0474] The server sends the completed virtual representation to the user's terminal. The virtual representation is displayed in a dedicated application on the terminal, making it interactive for the user. The input is an avatar with added movement and sound, and the output is a display that allows the user to visually interact with it.

[0475] (Application Example 1)

[0476] 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."

[0477] In modern society, many pet lovers are affected by pet loss, and the use of virtual reality technology to alleviate this is highly anticipated. Furthermore, when purchasing pet-related products, users often find it difficult to determine if a product is actually suitable for their pet, which negatively impacts the purchasing experience. Therefore, there is a need to provide a realistic and interactive virtual pet experience through pet avatars to support the assessment of product suitability.

[0478] 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.

[0479] In this invention, the server includes means for acquiring and analyzing images and video information of pets, generation means for generating virtual animals using feature information extracted from the images and video information, and display means for displaying the generated virtual animals on the user's information processing device and enabling two-way communication. As a result, users can alleviate pet loss and make judgments about product suitability through virtual experiences via pet avatars.

[0480] "Pet image and video information" refers to visual and auditory data of animals owned by the user, including still images and videos recorded in digital format.

[0481] A "virtual animal" refers to a digital avatar created on a computer, such as a 3D model or animation that reflects the characteristics of a specific pet.

[0482] A "user information processing device" refers to a device used for interacting with a pet avatar, and this includes smartphones, tablets, personal computers, etc.

[0483] "Two-way communication" refers to a function that allows information to be exchanged in real time between the user and the virtual animal, and in which the behavior and reactions of the virtual animal change according to the user's input.

[0484] "Virtual product testing" refers to the process of using pet avatars to check the usability and suitability of a product in a virtual reality space before purchasing it.

[0485] This invention is implemented by sending images and video information of pets from a user's terminal to a server. The terminal can be an information processing device such as a smartphone, tablet, or personal computer. The server analyzes the received image and video information and extracts characteristic information of the pet using image recognition technology and voice analysis technology. This process uses a programming language such as Python and machine learning frameworks such as TensorFlow or PyTorch.

[0486] Next, the server uses a generative AI model based on the extracted feature information to generate a virtual animal. The virtual animal is realistically rendered using a 3D engine such as Unity or Unreal Engine. This generated virtual animal is displayed on the user's device, allowing for real-time interaction. When the user interacts with the virtual animal through their device, this information is sent to the server, instantly changing the virtual animal's behavior. This real-time, two-way communication is highly interactive because it changes the virtual animal's response based on user input.

[0487] Furthermore, when users virtually try out pet-related products, the system visually displays a virtual animal trying out the product. This allows users to verify whether the product is actually suitable for their pet. All interaction data is stored in a vector-based database and used in subsequent learning processes. User feedback is continuously used to improve the virtual animal and its behavior.

[0488] As a concrete example, before a user purchases a pet bed, they can see how a virtual animal would relax in it. An example of a prompt message in this case would be, "Generate a pet avatar relaxing in this bed." This allows the user to virtually experience whether the product is actually suitable for their pet.

[0489] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0490] Step 1:

[0491] The user uses their device to acquire images and videos of their pet and sends this information to the server. The input consists of still images and video data related to the pet, and the output is data sent to the server. This process is achieved by capturing images and videos using the device's camera function and uploading them to the server via the network.

[0492] Step 2:

[0493] The server analyzes received image and video information to extract pet characteristic information. The input is image and video data sent by the user, and the output is data that describes the pet's characteristics in detail. This process is carried out using an image recognition algorithm and audio analysis technology programmed in Python, and feature extraction using a TensorFlow model.

[0494] Step 3:

[0495] The server uses a generative AI model to generate virtual animals based on extracted feature information. The input is pet feature information, and the output is a 3D virtual animal avatar. This process uses a deep learning model (e.g., GAN) to calculate the shape and movement of the virtual animal, and then uses Unity to render the specific 3D model.

[0496] Step 4:

[0497] The device displays a generated virtual animal to the user, allowing the user to interact with the avatar. The input is a 3D virtual animal avatar, and the output is a visual image displayed on the device screen. Here, UI design using Unity and the device's display technology are used to achieve real-time interactive display.

[0498] Step 5:

[0499] The user initiates a product trial using a virtual animal via a terminal and sends the information to the server. The input consists of information about the product the user wants to try and specific prompts, while the output is a trial scenario featuring the virtual animal and the product. This process is performed by the user interacting with the system using text input or voice commands, which the server then interprets to generate the virtual scenario.

[0500] Step 6:

[0501] The server instantly changes the behavior of the virtual animal based on user input and sends it to the terminal. Input consists of user actions and prompts, while output is the virtual animal with the updated behavior. In this process, the server analyzes user action data, updates the virtual animal's behavior in real time, and reflects it on the display.

[0502] Step 7:

[0503] Users observe how a virtual animal uses a product through their device and send feedback to the server. The input is feedback information, and the output is training data stored on the server. Here, users input their impressions and opinions in text format after their actions, which the server receives and uses to improve future algorithms.

[0504] 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.

[0505] This invention is a system that incorporates an emotion engine that recognizes the user's emotions and uses that information to adjust the behavior of a pet avatar. This system includes the user's terminal and server and is implemented via an application.

[0506] First, the user uploads images and video data of their pet to their device and sends it to the server. The server receives this data and performs analysis. The analysis is performed using image recognition and speech recognition technologies to extract characteristic data of the pet. Based on this, the server uses a generative AI to generate a 3D avatar of the pet and also creates animation data.

[0507] The generated avatar is displayed on the user's device. At this point, an emotion engine runs on the device and recognizes the user's emotional state by analyzing the user's voice, facial expressions, gestures, etc., in real time. The device sends the results of the emotion engine's analysis to the server, which uses that information to adjust the avatar's responses and actions.

[0508] For example, when a user speaks into the device, the emotion engine recognizes that the user is happy based on the tone of their voice and the content of their words. This emotion information is sent to the server, which controls the avatar to respond with actions or sounds that indicate happiness. Conversely, if the emotion engine determines that the user is depressed, the avatar is designed to perform comforting actions.

[0509] Furthermore, the emotion engine collects emotional data and interaction history, which are then analyzed on the server. Based on this analysis, the pet avatar's responses and actions are continuously improved, enabling the provision of appropriate interactions that match the user's emotions. This system allows users to have a more realistic interaction experience with their pets.

[0510] The following describes the processing flow.

[0511] Step 1:

[0512] The user uses a dedicated application to input images and video data of their pet into the device and send it to the server. The device then transfers the data to the server.

[0513] Step 2:

[0514] The server analyzes the received image and video data. This analysis uses image recognition technology to extract the pet's appearance and behavioral characteristics, and utilizes speech recognition technology to analyze the pet's vocalizations.

[0515] Step 3:

[0516] The server vectorizes the extracted feature data and stores it in a vector database. This storage makes the feature data efficiently searchable and available for use.

[0517] Step 4:

[0518] The server uses a generative AI to create a 3D avatar of the pet based on the stored feature data. The server then adds animations of movements and sounds associated with this avatar.

[0519] Step 5:

[0520] The generated avatar is sent from the server to the user's device. The device uses a dedicated application to display the avatar, creating an environment where the user can interact with it.

[0521] Step 6:

[0522] The device activates an emotion engine that analyzes the user's voice, gestures, and facial expressions in real time as they interact with the avatar, detecting the user's emotional state.

[0523] Step 7:

[0524] The analyzed user's emotional state is sent from the device to the server. The server uses this emotional information to control the avatar's responses and actions, setting appropriate reactions according to the user's emotions.

[0525] Step 8:

[0526] As a concrete example, when a user smiles and talks to their avatar, the emotion engine analyzes that emotion as "joy." The server then instructs the avatar to perform playful actions in response to the user's joy, and the avatar responds to the user with actions and sounds that express that joy.

[0527] Step 9:

[0528] The server collects and analyzes user emotion data and interaction history. This allows for continuous improvement of avatar behavior and responses, enabling more natural and emotion-appropriate interactions.

[0529] (Example 2)

[0530] 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."

[0531] To achieve realistic interaction with pets, not only is avatar generation necessary, but dynamic responses based on the user's emotions are also required. However, existing technologies struggle to accurately reflect user emotions in interactions, and in particular, real-time changes in avatar behavior are insufficient. Furthermore, there is a lack of data collection and analysis to individually tune the behavior of pet avatars, limiting the quality of the user experience.

[0532] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0533] In this invention, the server includes means for identifying the user's emotions in real time and analyzing pet image and video data; means for generating a three-dimensional pet model using an artificial intelligence model; and means for displaying the generated model and its movements on the user's device and analyzing and incorporating the user's emotional information. This enables real-time interaction that responds to the user's emotions and highly accurate, individually tuned avatar movements.

[0534] "User" refers to an individual who uses the system and is the entity that interacts with the pet avatar.

[0535] "Emotional recognition" is the process of analyzing input data such as the user's voice and facial expressions to determine the user's emotional state.

[0536] "Pet image and video data" refers to still images and dynamic video data that record the characteristics of the pet, and is the source data used to generate the avatar.

[0537] An "artificial intelligence model" is a computational model that learns specific patterns and features from data and uses those results to perform inference and generation.

[0538] A "three-dimensional model" refers to a digital representation of a pet avatar recreated in three-dimensional space, intended to provide visually realistic images.

[0539] A "device" refers to an electronic device used by the user to interact with and display their pet avatar.

[0540] "Interaction" refers to the dynamic exchange between the user and the pet avatar, and is a two-way communication that includes changes in the avatar's behavior in response to emotions.

[0541] "Real-time" refers to processing that is immediate and has the characteristic of responding instantly to user input.

[0542] To implement this invention, the user must first upload images and videos of their pet to their device and send them to the server. The device is configured to ensure that this data is reliably delivered to the server via technologies such as cloud storage or direct file transfer.

[0543] The server analyzes the received image and video data and extracts feature data. This process uses image recognition libraries (e.g., image processing libraries) and speech recognition technologies (e.g., automatic speech recognition APIs). After the feature data is extracted, the server uses a generative AI model to generate a 3D model of the pet and design the avatar's animation. Deep learning tools and 3D modeling software are utilized for this process.

[0544] The generated 3D model is sent to the user's device and displayed there. An emotion engine built into the device analyzes the user's voice and facial expressions in real time to recognize their emotional state. Specifically, the device's camera and microphone capture user input, which is then processed by a machine learning algorithm.

[0545] User emotion information is sent from the device to the server, which uses this information to adjust the avatar's responses and actions in real time. This enables natural avatar behavior that is appropriate to the user's situation. For example, if a user says "I'm happy today" to the device, the emotion engine recognizes the joy and sends that data to the server. The server then uses this information to make the avatar perform actions that indicate joy.

[0546] This system aims to enhance the virtual interaction experience with pets by facilitating real-time interactions that respond to the user's emotions.

[0547] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0548] Step 1:

[0549] The user selects images and videos of their pet using their device and uploads them to the server via the application. As input, the user specifies images and videos on the file selection screen and presses the upload button. This sends the image and video data to the server. Specifically, the device uses a file transfer protocol to deliver the data to the server.

[0550] Step 2:

[0551] The server acquires the received image and video data and begins analysis. The input data consists of uploaded images and videos of pets. The server uses an image recognition library to extract pet features from images (e.g., eye color, fur texture) and uses speech recognition technology to identify characteristic sounds from videos. The output is the pet feature data after this analysis. As a specific example, the server runs an image processing algorithm to recognize a particular color pattern.

[0552] Step 3:

[0553] The server uses the extracted feature data to generate a three-dimensional pet model using a generative AI model. The feature data is supplied to the AI ​​model as input. The AI ​​model utilizes deep learning techniques to generate a realistic three-dimensional avatar of the pet. The output consists of the generated three-dimensional model and its associated animation data. Specifically, the AI ​​model performs calculations to construct the pet avatar in a virtual space.

[0554] Step 4:

[0555] The server sends the generated 3D model and animation data to the user's terminal. The input data is the 3D data generated within the server. As output, this data is transferred to the user's terminal and loaded into the display system. Specifically, the server uses a high-speed data communication protocol to transfer the data to the terminal.

[0556] Step 5:

[0557] The device activates an emotion engine to analyze the user's voice, facial expressions, and other data in real time. The input data consists of the user's facial expressions and voice. Based on this data, the emotion engine performs an analysis to recognize the user's emotional state. The output is emotional information as a result of the analysis. Specifically, the device's camera and microphone capture the user's movements and send the information to the analysis algorithm.

[0558] Step 6:

[0559] The terminal sends acquired emotional information to the server. Emotional state data is sent to the server as input, and the server receives this data. The output is the avatar's response and actions, which are adjusted based on this emotional information. Specifically, the server analyzes the emotional information and dynamically sets the avatar's actions.

[0560] Step 7:

[0561] The server uses emotional information from the user to modify the responses and actions of the pet avatar in real time and send them back to the user. Emotional data is sent to the server as input. The output is an avatar with movements and sounds that correspond to the emotion. For example, if the user appears happy, the server will make the avatar perform actions that show happiness.

[0562] (Application Example 2)

[0563] 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."

[0564] Current virtual pet systems have limited user interaction and are insufficient in terms of dynamic emotional expression and behavioral adjustments based on emotional state. Furthermore, in storytelling-type content delivery, there is a need for technology that reflects user emotional feedback in real time. Additionally, it is necessary to improve entertainment value by providing more sophisticated personalized experiences through analysis of interaction history based on emotional state.

[0565] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0566] In this invention, the server includes a device for acquiring and analyzing visual and auditory information of a pet; a creation device for generating a virtual representation of the pet using feature information extracted from the visual and auditory information; a display device for displaying the generated virtual representation on the user's information terminal and enabling two-way interaction; and a device for recognizing the user's emotional state using an emotion analysis engine and adjusting the operation of the virtual representation according to the recognition result. This makes it possible to provide a virtual experience that responds flexibly to the user's emotions, improving content immersion and user satisfaction.

[0567] "Visual information" refers to data related to the user's or pet's vision, and is acquired as images or videos.

[0568] "Audio information" refers to data related to the voices of users or pets, and is information acquired as sounds or words.

[0569] "Characteristic information" refers to elements that represent the individuality and characteristics of a pet, extracted from visual and auditory information.

[0570] A "virtual representation" is a digital representation of a pet generated based on its characteristic information, and is used as an avatar or character.

[0571] A "creation device" is a device that generates virtual representations using feature information.

[0572] A "display device" is a device used to display a generated virtual representation on a user's information terminal.

[0573] An "emotion analysis engine" is an analytical technology that recognizes a user's emotional state from their voice, facial expressions, and other data.

[0574] An "information terminal" is a device, such as a smartphone or computer, that a user uses to interact with virtual representations.

[0575] To implement this invention, it is necessary to build a system in which a user's information terminal and a server in the cloud work together. First, the user uses an information terminal such as a smartphone or tablet to acquire visual and auditory information of their pet and sends this information to the server. The server extracts specific feature information based on the received information. This process uses image processing libraries such as OpenCV and speech recognition services such as Google Cloud Speech-to-Text.

[0576] On the server, a generative AI model is used with feature information to generate a virtual representation (avatar) of the pet. This virtual representation includes 3D models and animation data, and is generated by an AI model built with TensorFlow or PyTorch.

[0577] The generated virtual representation is then sent to the user's information terminal and displayed via a dedicated application. This application monitors the user's facial expressions and voice in real time and recognizes the user's emotional state using an emotion analysis engine installed in the terminal. Based on the recognized emotional state, the server dynamically adjusts the behavior and expression of the virtual representation.

[0578] To give a specific example, if the user is feeling down, the virtual representation is programmed to perform comforting actions. Also, if the system determines that the user is enjoying themselves, the virtual representation changes its behavior to express joy.

[0579] Examples of prompts to input into the generating AI model include, "What action should the pet avatar take when the user smiles?" and "How can the pet avatar make an expression that further captures the viewer's heart during the emotional moments of the story?" This makes it possible to further enhance the user experience.

[0580] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0581] Step 1:

[0582] The user's device acquires visual and auditory information from the pet using its camera and microphone. This input data is saved as image and audio files of the pet. This data is then transmitted from the device to the server.

[0583] Step 2:

[0584] The server receives visual and audio information transmitted from the terminal. To analyze the received data, image processing is performed using OpenCV, and speech recognition is performed using Google Cloud Speech-to-Text. Feature information is extracted using these methods and stored on the server.

[0585] Step 3:

[0586] The server generates a virtual representation using a generative AI model (TensorFlow or PyTorch) based on the extracted feature information. This generation process is achieved by inputting feature information into the model and outputting a 3D model and animation data of the virtual pet.

[0587] Step 4:

[0588] The generated virtual representation is sent from the server to the user's terminal. A dedicated application on the terminal receives this virtual representation and displays it on the terminal's display.

[0589] Step 5:

[0590] An emotion analysis engine installed on the user's device is activated, capturing the user's facial expressions and voice data in real time. This data is processed to identify the user's emotional state. The resulting emotional information is sent to the server with a short delay.

[0591] Step 6:

[0592] The server adjusts the movements and expressions of the virtual representation based on emotional information. It determines the action based on this prompt and updates the virtual representation. For example, it processes a command such as, "What action should the pet avatar take when the user smiles?"

[0593] Step 7:

[0594] Users can interact with virtual representations that have been re-tuned on their devices. The virtual representations respond in accordance with the user's emotional state, enhancing the entertainment value.

[0595] 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.

[0596] 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.

[0597] 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.

[0598] [Fourth Embodiment]

[0599] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0600] 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.

[0601] 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).

[0602] 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.

[0603] 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.

[0604] 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).

[0605] 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.

[0606] 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.

[0607] 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.

[0608] 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.

[0609] 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.

[0610] 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.

[0611] 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".

[0612] This invention is implemented by first having the user send images and video data of their pet from their own device to a server. The user inputs visual and auditory information of the pet via an application, which the server receives.

[0613] The server is responsible for analyzing the received data. Image recognition and voice analysis technologies are used for the analysis to clearly extract features such as the pet's appearance and behavior. This generates feature data, which is then stored in a vector database.

[0614] Next, the server uses a generative AI to generate a 3D avatar of the pet based on the stored feature data. The server then adds animations of the pet's movements and sounds to the avatar to enhance its realism.

[0615] The generated avatar is sent from the server to the user's device and displayed on the device using a dedicated application. The user can interact with this avatar through the device. The device detects user input such as instructions and gestures and sends this information to the server. Based on these instructions, the server controls the avatar's responses.

[0616] For example, when a user taps a pet avatar on their device, the device detects the action and sends it to the server. Based on the user's actions, the server controls the avatar to respond by making a specific sound or performing a joyful jump. In this way, the user can enjoy an experience as if they were interacting with their pet again.

[0617] Furthermore, the terminals and servers are equipped with interaction-based learning modules that continuously improve the avatar's movements and behavior by incorporating user feedback. This increases the naturalness and familiarity of interactions with the avatar, making it possible to provide a more human-like experience.

[0618] In this way, the present invention specifically realizes a system for mitigating pet loss and providing a virtual pet experience.

[0619] The following describes the processing flow.

[0620] Step 1:

[0621] Users upload images and videos of their pets to their device using a dedicated application. The device then sends the uploaded data to the server.

[0622] Step 2:

[0623] The server analyzes the received image and video data. This analysis uses image recognition technology to identify the pet's visual features and speech recognition technology to extract the pet's vocalizations.

[0624] Step 3:

[0625] The server vectorizes the feature data obtained as a result of the analysis and stores it in a vector database. Vectorization allows pet features to be stored digitally efficiently and in a searchable format.

[0626] Step 4:

[0627] The server uses the stored feature data to begin generating avatars using a generative AI. Based on the features, the server creates a 3D avatar of the pet and also generates animation data with movement and sound.

[0628] Step 5:

[0629] The server sends the generated avatar to the user's device. The device uses a dedicated application to display the avatar and enable interaction with the user.

[0630] Step 6:

[0631] The user interacts with the displayed avatar by inputting gestures and voice commands into the terminal. The terminal detects these inputs and sends them to the server in real time.

[0632] Step 7:

[0633] The server controls the avatar's movements based on user input. The server adjusts the avatar's animations so that it responds appropriately to user instructions, and displays them on the terminal.

[0634] Step 8:

[0635] The server collects user feedback and interaction history, and updates the machine learning model to improve the accuracy of the avatar's movements and responses. This continuous learning process makes the avatar's responses more natural.

[0636] (Example 1)

[0637] 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".

[0638] The challenge is to provide a system that intuitively recreates the appearance and movements of a pet as it was in life, offering comfort to users experiencing pet loss. Furthermore, it is necessary to enable users to interact naturally with the virtual representation through their own devices, and to allow for continuous technological improvement.

[0639] 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.

[0640] In this invention, the server includes means for acquiring and analyzing image and sound data captured by the user, generation means for generating a virtual representation using characteristic data extracted from the image and sound data, and display means for displaying the generated virtual representation on the user's display device and enabling interaction with the user. This makes it possible for the user to interact with a virtual representation of their pet in real time via their own terminal, enriching their experience.

[0641] "User" refers to a person who uses this system to interact with a virtual representation of a pet.

[0642] "Image and audio data" includes still images, videos, and audio data representing the pet's visual and auditory information.

[0643] "Characteristic data" refers to information extracted from image and audio data that describes the appearance, behavior, and vocal characteristics of a pet.

[0644] A "virtual representation" is a digital model that includes a three-dimensional model and animation of a pet, generated based on characteristic data.

[0645] A "display device" refers to a screen or display used to show virtual representations on a user's device.

[0646] "Interaction" refers to a series of processes in which a user provides input to a virtual representation, and the virtual representation responds accordingly.

[0647] A "server" refers to a computer system that performs image and sound data analysis, generates virtual representations, and transmits data to users.

[0648] To implement this invention, cooperation between the user, a terminal, and a server is necessary. The user uses a terminal such as a smartphone or tablet to collect visual and auditory information from their pet. These terminals are equipped with cameras and microphones, allowing them to collect images, videos, and audio data of the pet.

[0649] The device sends captured data to a server via a dedicated application. The application provides a user interface and automatically handles data organization and format conversion. During this process, data is transferred over the internet and encrypted for security purposes.

[0650] The server uses software such as "OpenCV" and "TensorFlow" to analyze the received data. It analyzes the shape, color, and movements of pets using image recognition technology, and analyzes audio data using libraries such as "Librosa" and "PyDub". As a result of this analysis, characteristic data is extracted and stored in a vector database. This characteristic data is used to create prompt statements for the generative AI model.

[0651] As a concrete example, the prompt is input to the generation AI model in the form of "Generate a 3D avatar of the pet based on the extracted features." The generation AI model then creates a three-dimensional virtual representation of the pet based on this prompt. In this process, tools such as "Blender" and "Unity" are used to add animations and sounds, while also considering the details of the shape and color.

[0652] The generated virtual representation is sent from the server to the user's terminal. The user can visualize and interact with this virtual representation on the terminal's display using a dedicated application. Through this system, the user can once again enjoy a sensory interaction with their pet as it was when it was alive.

[0653] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0654] Step 1:

[0655] Users collect images, videos, and audio of their pets using their devices. This involves taking photos of the pet with the camera and recording their sounds with the microphone. The input data consists of the pet's visual and auditory information, which is then incorporated into the device's application.

[0656] Step 2:

[0657] The terminal organizes the collected data for transmission to the server. The data is compressed and encrypted and transferred to the server via an internet connection. The input here is the raw data obtained from the user, and the output is the data converted into a format that the server can receive.

[0658] Step 3:

[0659] The server analyzes the received data. It uses "OpenCV" and "TensorFlow" for image recognition to identify the shape and color of pets. Audio data is analyzed using "Librosa" and "PyDub" to extract vocalization patterns. The input is organized image and audio data, and the output is extracted characteristic data.

[0660] Step 4:

[0661] The server stores the characteristic data in vector format in a database. This storage process prepares the basic information needed for later use in the generated AI model. The input is the analyzed characteristic data, and the output is the stored data as vector data.

[0662] Step 5:

[0663] The server generates a virtual representation of the pet using a generative AI model. It creates a prompt message, "Generate a 3D avatar of the pet based on the extracted features," and inputs it into the model. The generated virtual representation is output as a 3D model or animation.

[0664] Step 6:

[0665] The server uses virtual reality development tools such as Blender and Unity to add animations of movement and sound to the generated avatars. Specifically, pet movements and sounds are added, enabling more realistic representations. The input is the initial virtual representation output from the AI ​​model, and the output is the completed avatar with added movement and sound.

[0666] Step 7:

[0667] The server sends the completed virtual representation to the user's terminal. The virtual representation is displayed in a dedicated application on the terminal, making it interactive for the user. The input is an avatar with added movement and sound, and the output is a display that allows the user to visually interact with it.

[0668] (Application Example 1)

[0669] 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".

[0670] In modern society, many pet lovers are affected by pet loss, and the use of virtual reality technology to alleviate this is highly anticipated. Furthermore, when purchasing pet-related products, users often find it difficult to determine if a product is actually suitable for their pet, which negatively impacts the purchasing experience. Therefore, there is a need to provide a realistic and interactive virtual pet experience through pet avatars to support the assessment of product suitability.

[0671] 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.

[0672] In this invention, the server includes means for acquiring and analyzing images and video information of pets, generation means for generating virtual animals using feature information extracted from the images and video information, and display means for displaying the generated virtual animals on the user's information processing device and enabling two-way communication. As a result, users can alleviate pet loss and make judgments about product suitability through virtual experiences via pet avatars.

[0673] "Pet image and video information" refers to visual and auditory data of animals owned by the user, including still images and videos recorded in digital format.

[0674] A "virtual animal" refers to a digital avatar created on a computer, such as a 3D model or animation that reflects the characteristics of a specific pet.

[0675] A "user information processing device" refers to a device used for interacting with a pet avatar, and this includes smartphones, tablets, personal computers, etc.

[0676] "Two-way communication" refers to a function that allows information to be exchanged in real time between the user and the virtual animal, and in which the behavior and reactions of the virtual animal change according to the user's input.

[0677] "Virtual product testing" refers to the process of using pet avatars to check the usability and suitability of a product in a virtual reality space before purchasing it.

[0678] This invention is implemented by sending images and video information of pets from a user's terminal to a server. The terminal can be an information processing device such as a smartphone, tablet, or personal computer. The server analyzes the received image and video information and extracts characteristic information of the pet using image recognition technology and voice analysis technology. This process uses a programming language such as Python and machine learning frameworks such as TensorFlow or PyTorch.

[0679] Next, the server uses a generative AI model based on the extracted feature information to generate a virtual animal. The virtual animal is realistically rendered using a 3D engine such as Unity or Unreal Engine. This generated virtual animal is displayed on the user's device, allowing for real-time interaction. When the user interacts with the virtual animal through their device, this information is sent to the server, instantly changing the virtual animal's behavior. This real-time, two-way communication is highly interactive because it changes the virtual animal's response based on user input.

[0680] Furthermore, when users virtually try out pet-related products, the system visually displays a virtual animal trying out the product. This allows users to verify whether the product is actually suitable for their pet. All interaction data is stored in a vector-based database and used in subsequent learning processes. User feedback is continuously used to improve the virtual animal and its behavior.

[0681] As a concrete example, before a user purchases a pet bed, they can see how a virtual animal would relax in it. An example of a prompt message in this case would be, "Generate a pet avatar relaxing in this bed." This allows the user to virtually experience whether the product is actually suitable for their pet.

[0682] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0683] Step 1:

[0684] The user uses their device to acquire images and videos of their pet and sends this information to the server. The input consists of still images and video data related to the pet, and the output is data sent to the server. This process is achieved by capturing images and videos using the device's camera function and uploading them to the server via the network.

[0685] Step 2:

[0686] The server analyzes received image and video information to extract pet characteristic information. The input is image and video data sent by the user, and the output is data that describes the pet's characteristics in detail. This process is carried out using an image recognition algorithm and audio analysis technology programmed in Python, and feature extraction using a TensorFlow model.

[0687] Step 3:

[0688] The server uses a generative AI model to generate virtual animals based on extracted feature information. The input is pet feature information, and the output is a 3D virtual animal avatar. This process uses a deep learning model (e.g., GAN) to calculate the shape and movement of the virtual animal, and then uses Unity to render the specific 3D model.

[0689] Step 4:

[0690] The device displays a generated virtual animal to the user, allowing the user to interact with the avatar. The input is a 3D virtual animal avatar, and the output is a visual image displayed on the device screen. Here, UI design using Unity and the device's display technology are used to achieve real-time interactive display.

[0691] Step 5:

[0692] The user initiates a product trial using a virtual animal via a terminal and sends the information to the server. The input consists of information about the product the user wants to try and specific prompts, while the output is a trial scenario featuring the virtual animal and the product. This process is performed by the user interacting with the system using text input or voice commands, which the server then interprets to generate the virtual scenario.

[0693] Step 6:

[0694] The server instantly changes the behavior of the virtual animal based on user input and sends it to the terminal. Input consists of user actions and prompts, while output is the virtual animal with the updated behavior. In this process, the server analyzes user action data, updates the virtual animal's behavior in real time, and reflects it on the display.

[0695] Step 7:

[0696] Users observe how a virtual animal uses a product through their device and send feedback to the server. The input is feedback information, and the output is training data stored on the server. Here, users input their impressions and opinions in text format after their actions, which the server receives and uses to improve future algorithms.

[0697] 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.

[0698] This invention is a system that incorporates an emotion engine that recognizes the user's emotions and uses that information to adjust the behavior of a pet avatar. This system includes the user's terminal and server and is implemented via an application.

[0699] First, the user uploads images and video data of their pet to their device and sends it to the server. The server receives this data and performs analysis. The analysis is performed using image recognition and speech recognition technologies to extract characteristic data of the pet. Based on this, the server uses a generative AI to generate a 3D avatar of the pet and also creates animation data.

[0700] The generated avatar is displayed on the user's device. At this point, an emotion engine runs on the device and recognizes the user's emotional state by analyzing the user's voice, facial expressions, gestures, etc., in real time. The device sends the results of the emotion engine's analysis to the server, which uses that information to adjust the avatar's responses and actions.

[0701] For example, when a user speaks into the device, the emotion engine recognizes that the user is happy based on the tone of their voice and the content of their words. This emotion information is sent to the server, which controls the avatar to respond with actions or sounds that indicate happiness. Conversely, if the emotion engine determines that the user is depressed, the avatar is designed to perform comforting actions.

[0702] Furthermore, the emotion engine collects emotional data and interaction history, which are then analyzed on the server. Based on this analysis, the pet avatar's responses and actions are continuously improved, enabling the provision of appropriate interactions that match the user's emotions. This system allows users to have a more realistic interaction experience with their pets.

[0703] The following describes the processing flow.

[0704] Step 1:

[0705] The user uses a dedicated application to input images and video data of their pet into the device and send it to the server. The device then transfers the data to the server.

[0706] Step 2:

[0707] The server analyzes the received image and video data. This analysis uses image recognition technology to extract the pet's appearance and behavioral characteristics, and utilizes speech recognition technology to analyze the pet's vocalizations.

[0708] Step 3:

[0709] The server vectorizes the extracted feature data and stores it in a vector database. This storage makes the feature data efficiently searchable and available for use.

[0710] Step 4:

[0711] The server uses a generative AI to create a 3D avatar of the pet based on the stored feature data. The server then adds animations of movements and sounds associated with this avatar.

[0712] Step 5:

[0713] The generated avatar is sent from the server to the user's device. The device uses a dedicated application to display the avatar, creating an environment where the user can interact with it.

[0714] Step 6:

[0715] The device activates an emotion engine that analyzes the user's voice, gestures, and facial expressions in real time as they interact with the avatar, detecting the user's emotional state.

[0716] Step 7:

[0717] The analyzed user's emotional state is sent from the device to the server. The server uses this emotional information to control the avatar's responses and actions, setting appropriate reactions according to the user's emotions.

[0718] Step 8:

[0719] As a concrete example, when a user smiles and talks to their avatar, the emotion engine analyzes that emotion as "joy." The server then instructs the avatar to perform playful actions in response to the user's joy, and the avatar responds to the user with actions and sounds that express that joy.

[0720] Step 9:

[0721] The server collects and analyzes user emotion data and interaction history. This allows for continuous improvement of avatar behavior and responses, enabling more natural and emotion-appropriate interactions.

[0722] (Example 2)

[0723] 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".

[0724] To achieve realistic interaction with pets, not only is avatar generation necessary, but dynamic responses based on the user's emotions are also required. However, existing technologies struggle to accurately reflect user emotions in interactions, and in particular, real-time changes in avatar behavior are insufficient. Furthermore, there is a lack of data collection and analysis to individually tune the behavior of pet avatars, limiting the quality of the user experience.

[0725] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0726] In this invention, the server includes means for identifying the user's emotions in real time and analyzing pet image and video data; means for generating a three-dimensional pet model using an artificial intelligence model; and means for displaying the generated model and its movements on the user's device and analyzing and incorporating the user's emotional information. This enables real-time interaction that responds to the user's emotions and highly accurate, individually tuned avatar movements.

[0727] "User" refers to an individual who uses the system and is the entity that interacts with the pet avatar.

[0728] "Emotional recognition" is the process of analyzing input data such as the user's voice and facial expressions to determine the user's emotional state.

[0729] "Pet image and video data" refers to still images and dynamic video data that record the characteristics of the pet, and is the source data used to generate the avatar.

[0730] An "artificial intelligence model" is a computational model that learns specific patterns and features from data and uses those results to perform inference and generation.

[0731] A "three-dimensional model" refers to a digital representation of a pet avatar recreated in three-dimensional space, intended to provide visually realistic images.

[0732] A "device" refers to an electronic device used by the user to interact with and display their pet avatar.

[0733] "Interaction" refers to the dynamic exchange between the user and the pet avatar, and is a two-way communication that includes changes in the avatar's behavior in response to emotions.

[0734] "Real-time" refers to processing that is immediate and has the characteristic of responding instantly to user input.

[0735] To implement this invention, the user must first upload images and videos of their pet to their device and send them to the server. The device is configured to ensure that this data is reliably delivered to the server via technologies such as cloud storage or direct file transfer.

[0736] The server analyzes the received image and video data and extracts feature data. This process uses image recognition libraries (e.g., image processing libraries) and speech recognition technologies (e.g., automatic speech recognition APIs). After the feature data is extracted, the server uses a generative AI model to generate a 3D model of the pet and design the avatar's animation. Deep learning tools and 3D modeling software are utilized for this process.

[0737] The generated 3D model is sent to the user's device and displayed there. An emotion engine built into the device analyzes the user's voice and facial expressions in real time to recognize their emotional state. Specifically, the device's camera and microphone capture user input, which is then processed by a machine learning algorithm.

[0738] User emotion information is sent from the device to the server, which uses this information to adjust the avatar's responses and actions in real time. This enables natural avatar behavior that is appropriate to the user's situation. For example, if a user says "I'm happy today" to the device, the emotion engine recognizes the joy and sends that data to the server. The server then uses this information to make the avatar perform actions that indicate joy.

[0739] This system aims to enhance the virtual interaction experience with pets by facilitating real-time interactions that respond to the user's emotions.

[0740] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0741] Step 1:

[0742] The user selects images and videos of their pet using their device and uploads them to the server via the application. As input, the user specifies images and videos on the file selection screen and presses the upload button. This sends the image and video data to the server. Specifically, the device uses a file transfer protocol to deliver the data to the server.

[0743] Step 2:

[0744] The server acquires the received image and video data and begins analysis. The input data consists of uploaded images and videos of pets. The server uses an image recognition library to extract pet features from images (e.g., eye color, fur texture) and uses speech recognition technology to identify characteristic sounds from videos. The output is the pet feature data after this analysis. As a specific example, the server runs an image processing algorithm to recognize a particular color pattern.

[0745] Step 3:

[0746] The server uses the extracted feature data to generate a three-dimensional pet model using a generative AI model. The feature data is supplied to the AI ​​model as input. The AI ​​model utilizes deep learning techniques to generate a realistic three-dimensional avatar of the pet. The output consists of the generated three-dimensional model and its associated animation data. Specifically, the AI ​​model performs calculations to construct the pet avatar in a virtual space.

[0747] Step 4:

[0748] The server sends the generated 3D model and animation data to the user's terminal. The input data is the 3D data generated within the server. As output, this data is transferred to the user's terminal and loaded into the display system. Specifically, the server uses a high-speed data communication protocol to transfer the data to the terminal.

[0749] Step 5:

[0750] The device activates an emotion engine to analyze the user's voice, facial expressions, and other data in real time. The input data consists of the user's facial expressions and voice. Based on this data, the emotion engine performs an analysis to recognize the user's emotional state. The output is emotional information as a result of the analysis. Specifically, the device's camera and microphone capture the user's movements and send the information to the analysis algorithm.

[0751] Step 6:

[0752] The terminal sends acquired emotional information to the server. Emotional state data is sent to the server as input, and the server receives this data. The output is the avatar's response and actions, which are adjusted based on this emotional information. Specifically, the server analyzes the emotional information and dynamically sets the avatar's actions.

[0753] Step 7:

[0754] The server uses emotional information from the user to modify the responses and actions of the pet avatar in real time and send them back to the user. Emotional data is sent to the server as input. The output is an avatar with movements and sounds that correspond to the emotion. For example, if the user appears happy, the server will make the avatar perform actions that show happiness.

[0755] (Application Example 2)

[0756] 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".

[0757] Current virtual pet systems have limited user interaction and are insufficient in terms of dynamic emotional expression and behavioral adjustments based on emotional state. Furthermore, in storytelling-type content delivery, there is a need for technology that reflects user emotional feedback in real time. Additionally, it is necessary to improve entertainment value by providing more sophisticated personalized experiences through analysis of interaction history based on emotional state.

[0758] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0759] In this invention, the server includes a device for acquiring and analyzing visual and auditory information of a pet; a creation device for generating a virtual representation of the pet using feature information extracted from the visual and auditory information; a display device for displaying the generated virtual representation on the user's information terminal and enabling two-way interaction; and a device for recognizing the user's emotional state using an emotion analysis engine and adjusting the operation of the virtual representation according to the recognition result. This makes it possible to provide a virtual experience that responds flexibly to the user's emotions, improving content immersion and user satisfaction.

[0760] "Visual information" refers to data related to the user's or pet's vision, and is acquired as images or videos.

[0761] "Audio information" refers to data related to the voices of users or pets, and is information acquired as sounds or words.

[0762] "Characteristic information" refers to elements that represent the individuality and characteristics of a pet, extracted from visual and auditory information.

[0763] A "virtual representation" is a digital representation of a pet generated based on its characteristic information, and is used as an avatar or character.

[0764] A "creation device" is a device that generates virtual representations using feature information.

[0765] A "display device" is a device used to display a generated virtual representation on a user's information terminal.

[0766] An "emotion analysis engine" is an analytical technology that recognizes a user's emotional state from their voice, facial expressions, and other data.

[0767] An "information terminal" is a device, such as a smartphone or computer, that a user uses to interact with virtual representations.

[0768] To implement this invention, it is necessary to build a system in which a user's information terminal and a server in the cloud work together. First, the user uses an information terminal such as a smartphone or tablet to acquire visual and auditory information of their pet and sends this information to the server. The server extracts specific feature information based on the received information. This process uses image processing libraries such as OpenCV and speech recognition services such as Google Cloud Speech-to-Text.

[0769] On the server, a generative AI model is used with feature information to generate a virtual representation (avatar) of the pet. This virtual representation includes 3D models and animation data, and is generated by an AI model built with TensorFlow or PyTorch.

[0770] The generated virtual representation is then sent to the user's information terminal and displayed via a dedicated application. This application monitors the user's facial expressions and voice in real time and recognizes the user's emotional state using an emotion analysis engine installed in the terminal. Based on the recognized emotional state, the server dynamically adjusts the behavior and expression of the virtual representation.

[0771] To give a specific example, if the user is feeling down, the virtual representation is programmed to perform comforting actions. Also, if the system determines that the user is enjoying themselves, the virtual representation changes its behavior to express joy.

[0772] Examples of prompts to input into the generating AI model include, "What action should the pet avatar take when the user smiles?" and "How can the pet avatar make an expression that further captures the viewer's heart during the emotional moments of the story?" This makes it possible to further enhance the user experience.

[0773] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0774] Step 1:

[0775] The user's device acquires visual and auditory information from the pet using its camera and microphone. This input data is saved as image and audio files of the pet. This data is then transmitted from the device to the server.

[0776] Step 2:

[0777] The server receives visual and audio information transmitted from the terminal. To analyze the received data, image processing is performed using OpenCV, and speech recognition is performed using Google Cloud Speech-to-Text. Feature information is extracted using these methods and stored on the server.

[0778] Step 3:

[0779] The server generates a virtual representation using a generative AI model (TensorFlow or PyTorch) based on the extracted feature information. This generation process is achieved by inputting feature information into the model and outputting a 3D model and animation data of the virtual pet.

[0780] Step 4:

[0781] The generated virtual representation is sent from the server to the user's terminal. A dedicated application on the terminal receives this virtual representation and displays it on the terminal's display.

[0782] Step 5:

[0783] An emotion analysis engine installed on the user's device is activated, capturing the user's facial expressions and voice data in real time. This data is processed to identify the user's emotional state. The resulting emotional information is sent to the server with a short delay.

[0784] Step 6:

[0785] The server adjusts the movements and expressions of the virtual representation based on emotional information. It determines the action based on this prompt and updates the virtual representation. For example, it processes a command such as, "What action should the pet avatar take when the user smiles?"

[0786] Step 7:

[0787] Users can interact with virtual representations that have been re-tuned on their devices. The virtual representations respond in accordance with the user's emotional state, enhancing the entertainment value.

[0788] 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.

[0789] 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.

[0790] 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 robot 414.

[0791] 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.

[0792] 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. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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."

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] The following is further disclosed regarding the embodiments described above.

[0810] (Claim 1)

[0811] A means for acquiring and analyzing images and video data of pets,

[0812] A generation means for generating a pet avatar using feature data extracted from the aforementioned image and video data,

[0813] A display means that displays the generated avatar on the user's device and enables interaction,

[0814] A control means that changes the behavior of an avatar in real time based on user input,

[0815] A system that includes this.

[0816] (Claim 2)

[0817] The system according to claim 1, comprising means for storing analyzed feature data in a vector database and for using it to reproduce the movements and sounds of a pet avatar.

[0818] (Claim 3)

[0819] The system according to claim 1, comprising an avatar generated based on user feedback and a learning means for improving the avatar's behavior.

[0820] "Example 1"

[0821] (Claim 1)

[0822] A means for acquiring and analyzing image and sound data captured by the user,

[0823] A generation means for generating a virtual representation using characteristic data extracted from the aforementioned image and sound data,

[0824] A display means that displays the generated virtual representation on the user's display device and enables interaction with the user,

[0825] A control means that changes the behavior of a virtual representation in real time based on user input,

[0826] Means for storing feature data in a memory device and for reproducing the behavior and sound of a virtual representation,

[0827] A virtual representation generated based on user evaluations, and a learning method for improving its behavior,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, comprising means for storing characteristic data and for reproducing the behavior and sound of a virtual representation.

[0831] (Claim 3)

[0832] The system according to claim 1, comprising a learning means for improving virtual representations and their behavior based on user feedback.

[0833] "Application Example 1"

[0834] (Claim 1)

[0835] A means for acquiring and analyzing images and video information of pets,

[0836] A generation means for generating a virtual animal using feature information extracted from the aforementioned image and video information,

[0837] A display means that displays the generated virtual animal on the user's information processing device and enables two-way communication,

[0838] A control means that instantly changes the behavior of a virtual animal based on user input,

[0839] A means of virtually trying out pet-related products,

[0840] A system that includes this.

[0841] (Claim 2)

[0842] The system according to claim 1, comprising means for storing analyzed feature information in a vector-format database and for using it to reproduce the behavior and sounds of a virtual animal.

[0843] (Claim 3)

[0844] The system according to claim 1, comprising a virtual animal generated based on user feedback and a learning means for improving the behavior of the virtual animal.

[0845] "Example 2 of combining an emotion engine"

[0846] (Claim 1)

[0847] A means for recognizing the user's emotions and acquiring and analyzing images and video data of pets,

[0848] A generation means that uses feature data extracted from the aforementioned image and video data to generate a three-dimensional model and motion data of the pet using an artificial intelligence model,

[0849] A display and analysis means that displays the generated three-dimensional model on the user's device and enables interaction based on emotional data from the user,

[0850] A control means that modifies the behavior of a three-dimensional model in real time based on user actions and emotional changes,

[0851] A system that includes this.

[0852] (Claim 2)

[0853] The system according to claim 1, comprising means for storing analyzed feature data and emotion data in an information database and using them to reproduce the movements and sounds of a three-dimensional model of a pet.

[0854] (Claim 3)

[0855] The system according to claim 1, comprising a three-dimensional model generated based on the user's emotional data and past interaction history, and a learning means for improving the model's behavior.

[0856] "Application example 2 of combining emotional engines"

[0857] (Claim 1)

[0858] A device for acquiring and analyzing visual and auditory information of pets,

[0859] A creation device that generates a virtual representation of a pet using feature information extracted from the aforementioned visual and audio information,

[0860] A display device that displays the generated virtual representation on the user's information terminal and enables two-way communication,

[0861] A control device that changes the behavior of a virtual representation in real time based on user input information,

[0862] A device that recognizes the user's emotional state using an emotion analysis engine and adjusts the operation of a virtual representation according to the recognition results,

[0863] A system that includes this.

[0864] (Claim 2)

[0865] The system according to claim 1, comprising a device used to record analyzed feature information and sentiment data in a database and to reproduce and optimize the behavior and representation methods of a virtual representation.

[0866] (Claim 3)

[0867] The system according to claim 1, comprising a virtual representation generated based on the emotional state of a user and a learning device for improving the behavior of the virtual representation. [Explanation of Symbols]

[0868] 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 acquiring and analyzing images and video data of pets, A generation means for generating a pet avatar using feature data extracted from the aforementioned image and video data, A display means that displays the generated avatar on the user's device and enables interaction, A control means that changes the behavior of an avatar in real time based on user input, A system that includes this.

2. The system according to claim 1, comprising means for storing analyzed feature data in a vector database and using it to reproduce the movements and sounds of a pet avatar.

3. The system according to claim 1, comprising an avatar generated based on user feedback and a learning means for improving the avatar's movements.

Images