System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting a natural language by a user; means for analyzing the natural language input by the user; generative model means for generating a three dimensional space or a three dimensional object based on the analyzed natural language; and means for displaying the generated three dimensional space or three dimensional object to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] When users create 3D spaces and objects in the Metaverse, conventional methods require advanced technology and specialized knowledge, making it difficult for them to intuitively express themselves. This makes it difficult for beginners and users without technical skills to create creative 3D expressions. Another issue is the lack of a way to instantly visualize the created 3D spaces and objects, making it impossible for users to check the results in real time. [Means for solving the problem]

[0005] To solve the above-mentioned problems, the present invention provides the following means: a system including: means for a user to input natural language; means for analyzing the natural language input by the user; generative model means for generating a three-dimensional space or a three-dimensional object based on the analyzed natural language; and means for displaying the generated three-dimensional space or three-dimensional object to the user. Furthermore, by further including means for generating music or a game based on the analyzed natural language, or by using a machine learning algorithm in the natural language analysis means, the system allows users to intuitively and easily create creative three-dimensional expressions. This allows users to freely generate three-dimensional spaces and objects and visualize them in real time without requiring the advanced technology or specialized knowledge of the past.

[0006] A "user" is a human user who utilizes the system to input natural language and give instructions.

[0007] "Natural language" refers to a language that humans use on a daily basis, and in this context refers to words and sentences entered by a user.

[0008] "Analysis" refers to the process by which a system processes the natural language input by a user using machine learning algorithms and other methods to understand the meaning and intent.

[0009] "Three-dimensional space" refers to a space expressed in three dimensions: length, width, and height, and refers to a virtual environment that is generated based on user instructions.

[0010] A "three-dimensional object" refers to an individual object or structure placed in three-dimensional space, and is generated based on the user's instructions.

[0011] A "generative model" is a collection of algorithms and programs for generating three-dimensional spaces and three-dimensional objects based on analyzed natural language.

[0012] "Visualization" refers to the act of displaying the generated three-dimensional space or three-dimensional object so that the user can visually confirm it.

[0013] A "machine learning algorithm" is a computational method for building models based on data and making predictions, classifications, etc., and is used here to analyze natural language. [Brief explanation of the drawings]

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

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

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

[0017] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

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

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

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

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

[0024] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0031] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

[0035] The present invention relates to a system that enables users to intuitively generate three-dimensional spaces and three-dimensional objects using natural language. This system analyzes the natural language input by the user, and a generative model generates three-dimensional spaces and three-dimensional objects based on that, and presents them visually to the user.

[0036] The user inputs instructions in natural language through the system interface. For example, they might input, "I want to create a space with a wizard and where people can go on a treasure hunt." This input is received by the terminal and immediately sent to the server.

[0037] The server uses a natural language processing (NLP) library to parse the received natural language instructions. This parsing step extracts keywords and context to understand the details of the 3D space or 3D object requested by the user. The parsed data is passed to a generative modeling facility.

[0038] A generative model generates specified 3D spaces and objects based on analyzed data using machine learning algorithms. This generation process automatically arranges multiple objects and environments according to user instructions to create a realistic 3D space. For example, if the instructions include "wizard" and "treasure hunt," the generative model generates an explorable environment containing a wizard character and a treasure chest.

[0039] The generated 3D space and objects are sent to the device by the server. The device uses a 3D rendering engine based on the received data to visually display the generated 3D space and objects to the user. This allows the user to see in real time the creative results generated based on their instructions.

[0040] As a concrete example, consider the case where a user inputs, "I want to create a scene with a small waterfall in the forest." The server receives this instruction, extracts keywords such as "forest," "small waterfall," and "scenery," and passes them to the generative model. Based on these keywords, the generative model generates a scene with forest trees, plants, and a small waterfall. The generated three-dimensional space is sent to the device and visualized directly on the user's screen. This allows the user to immediately visually confirm their imagination.

[0041] This invention makes it possible for anyone to easily generate and display three-dimensional spaces and objects without the need for conventional advanced technology or specialized knowledge, significantly lowering the barrier to creative expression in the metaverse.

[0042] The processing flow will be explained below.

[0043] Step 1:

[0044] The user inputs instructions in natural language through the terminal interface, for example, "I want to create a space where there is a wizard and where you can go on a treasure hunt."

[0045] Step 2:

[0046] The device receives the user's instructions and sends them to the server. Specifically, the instructions are sent to the server using an API.

[0047] Step 3:

[0048] The server receives the instruction sent from the terminal, and the received natural language instruction is stored for natural language analysis in the next step.

[0049] Step 4:

[0050] The server uses a natural language processing (NLP) library to analyze the user's instructions. During this analysis process, meanings and keywords are extracted from the user's instructions. For example, keywords such as "wizard," "treasure hunt," and "space" are extracted.

[0051] Step 5:

[0052] The server issues instructions to the generative model based on the analyzed data, and the generative model prepares to generate 3D space and objects according to the user's instructions.

[0053] Step 6:

[0054] Generative models use machine learning algorithms to generate 3D spaces and objects based on the analyzed data, such as a "wizard" character or an environment containing a treasure chest for a "treasure hunt."

[0055] Step 7:

[0056] The generated 3D space and objects are sent to the terminal by the server, where the generated data is formatted so that it can be displayed correctly on the terminal.

[0057] Step 8:

[0058] The device receives the generated data sent from the server, which is then passed to the 3D rendering engine.

[0059] Step 9:

[0060] The device uses a 3D rendering engine to visually display the generated three-dimensional space and objects, allowing users to see the creative results generated in real time based on their instructions.

[0061] Step 10:

[0062] The user visually checks the generated 3D space and objects, and inputs additional instructions (corrections, updates, etc.) as needed, enabling further interactive operation.

[0063] Example 1

[0064] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0065] Conventional systems for generating 3D spaces and 3D objects require advanced technology and specialized knowledge, making them difficult for general users to operate intuitively. Furthermore, there are few systems that allow users to quickly generate 3D spaces by directly instructing them in natural language, and there has been a demand for an improved user experience.

[0066] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0067] In this invention, the server includes means for a user to input natural language, means for analyzing the natural language input by the user, and generative model means for generating a three-dimensional space or a three-dimensional object based on the analyzed natural language. This enables a user to intuitively and efficiently generate a three-dimensional space or an object simply by issuing instructions using natural language, without requiring specialized knowledge.

[0068] "User" refers to a person or organization that uses the system to generate three-dimensional spaces or three-dimensional objects.

[0069] "Natural language" refers to languages ​​that humans use in their daily lives, such as Japanese and English, and is different from programming languages.

[0070] An "input means" is a device or software that provides an interface for a user to input natural language, such as a text box or a voice recognition system.

[0071] "Means for analyzing" refers to a device or software that provides the technology to understand the natural language entered by the user through syntactic and contextual analysis and to enable the system to identify the requested content.

[0072] "Generative model means" refers to an algorithm or program for automatically generating three-dimensional spaces or three-dimensional objects based on parsed natural language instructions.

[0073] "Display means" refers to a device or software that visually presents the generated three-dimensional space or three-dimensional object to a user. For example, a display or a VR headset.

[0074] "Transmitting means" refers to a device or software that provides a technique for transmitting data of a three-dimensional space or three-dimensional object generated by a server to a terminal.

[0075] "Rendering means" refers to a device or software that generates high-quality images in real time based on the three-dimensional space or three-dimensional object data received by the terminal and visually displays them to the user.

[0076] A "machine learning algorithm" is a computational method or model that allows a system to learn from data and improve the accuracy of analysis and generation.

[0077] This invention relates to a system that allows users to intuitively generate three-dimensional spaces and three-dimensional objects using natural language. This system analyzes the natural language input by the user, and a generative AI model generates three-dimensional spaces and three-dimensional objects based on that, and presents them visually to the user.

[0078] Configuration and Operation

[0079] User Input

[0080] The user inputs instructions in natural language through the device interface. For example, they can input a prompt such as, "I want to create a space with a wizard and a treasure hunt." This prompt can be entered in a text box or recorded by voice input.

[0081] Sending data

[0082] The device receives the user's natural language input and immediately sends it to the server, using communication protocols such as web sockets and HTTP requests.

[0083] Natural Language Analysis

[0084] The server uses natural language processing (NLP) libraries, such as Python's NLTK or spaCy, to parse the received natural language instructions. It then extracts keywords and context to understand the details of the 3D space or object the user is requesting. For example, elements such as "wizard," "treasure hunt," and "space" are analyzed.

[0085] Processing the generative model

[0086] The analyzed data is passed to a generative AI model. The server uses machine learning algorithms such as GPT-3 and DALL-E to generate 3D spaces and objects. During this generation process, multiple objects and environments are automatically placed based on the user's instructions to create a realistic 3D space. For example, based on keywords such as "wizard" and "treasure hunt," the generative model creates an exploration space that includes a wizard character and a treasure chest.

[0087] Receiving and displaying data

[0088] The server sends the generated 3D space and object data to the device, which receives the data and uses Unity's 3D rendering engine to visually display the 3D space and objects. This allows users to see the creative results generated based on their instructions in real time.

[0089] Specific examples

[0090] As a concrete example, consider the case where a user inputs, "I want to create a scene with a small waterfall in the forest." The server receives this instruction, extracts keywords such as "forest," "small waterfall," and "scenery," and passes them to the generative model. Based on these keywords, the generative model generates a scene with forest trees, plants, and a small waterfall. The generated three-dimensional space is sent to the device and visualized directly on the user's screen. This allows the user to immediately visually confirm their imagination.

[0091] This invention makes it possible for anyone to easily generate and display three-dimensional spaces and objects without the need for conventional advanced technology or specialized knowledge, significantly lowering the barrier to creative expression in the metaverse.

[0092] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0093] Step 1:

[0094] The user inputs instructions in natural language through the terminal interface. A possible prompt might be, "I want to create a space where wizards can go on treasure hunts." This input natural language is acquired from the terminal's text box or voice input system. The input in this step is the user's natural language, and the output is raw text data. The terminal acquires this text data and prepares it to be passed to the next processing step.

[0095] Step 2:

[0096] The device receives the natural language instructions entered by the user and immediately sends them to the server. Data is communicated using WebSockets or HTTP requests. The input in this step is the text data obtained in step 1, and the output is that text data being sent to the server. The device correctly formats the user's input data and sends it to the server.

[0097] Step 3:

[0098] The server analyzes the received natural language instructions. This analysis uses a natural language processing (NLP) library such as Python's NLTK or spaCy. The input is the text data of the user's natural language instructions sent in step 2. The server analyzes this text data and extracts keywords and context. For example, elements such as "wizard," "treasure hunt," and "space" are extracted. The output is the analyzed keywords and context information.

[0099] Step 4:

[0100] The server prepares data for a generative AI model based on the analyzed keywords and contextual information. Specifically, it uses a generative model such as GPT-3 or DALL-E to generate 3D spaces and objects. The input in this step is the keywords and contextual information extracted in step 3, and the output is data on the 3D spaces and objects generated by the generative AI model. The server converts the data into a format suitable for the generative model and inputs it into the model.

[0101] Step 5:

[0102] The generative AI model generates the specified 3D space and objects based on the data. In this generation process, multiple objects and environments are automatically placed according to the user's instructions. For example, based on the keywords "wizard" and "treasure hunt," an exploration space including a wizard character and a treasure chest is constructed. The input in this step is the data given to the generative model in step 4, and the output is the generated 3D space and object data.

[0103] Step 6:

[0104] The server sends the generated 3D space and object data to the device. Data communication is performed using Web sockets and HTTP requests. The input in this step is the data generated in step 5, and the output is that data being sent to the device. The server sends the generated data to the device in the appropriate format and confirms that communication is complete.

[0105] Step 7:

[0106] The device visually displays the 3D space and objects generated using Unity, a 3D rendering engine, based on the received data. The input is the generated data sent in step 6, and the output is the 3D space and objects displayed on the user's screen. The device renders the received data and executes the operations to display it to the user in real time.

[0107] (Application example 1)

[0108] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0109] Conventional virtual store and three-dimensional space customization methods require advanced technology and specialized knowledge, making them difficult for general users to operate. Furthermore, they lack an intuitive interface for quickly changing the layout and displays within the store. As a result, virtual store operations and marketing activities are restricted. This invention aims to solve these problems by providing a means for users without special skills to easily customize the layout and displays of a virtual store using natural language.

[0110] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0111] In this invention, the server includes means for a user to input natural language, means for analyzing the natural language input by the user, generative model means for generating a three-dimensional space or a three-dimensional object based on the analyzed natural language, means for displaying the generated three-dimensional space or the three-dimensional object to the user, and means for customizing the layout or display of a virtual store based on the analyzed natural language, thereby enabling the user to intuitively customize the layout or display of a virtual store using natural language.

[0112] A "means for a user to input natural language" is something that provides an interface for a user to input instructions to a system using human language.

[0113] The "means for analyzing the natural language input by the user" refers to a process or technology for understanding the natural language input by the user and deciphering its meaning and intent.

[0114] "Generative modeling means for generating 3D spaces or 3D objects based on parsed natural language" refers to algorithms or technologies that create or construct 3D environments or objects based on the results of parsing natural language.

[0115] "Means for displaying the generated three-dimensional space or three-dimensional objects to the user" refers to display or rendering technologies that provide a visual representation of the generated three-dimensional environment or objects to the user.

[0116] "Means for customizing the layout or display of a virtual store based on analyzed natural language" refers to techniques and methods for changing and optimizing the layout and display content within a virtual store based on instructions in the user's natural language.

[0117] This invention relates to a system that allows users to intuitively create and customize three-dimensional spaces and objects using natural language, allowing them to easily change the layout and displays of virtual stores.

[0118] 1. System Configuration

[0119] Hardware and Software

[0120] Hardware used:

[0121] Smartphone

[0122] Head-mounted display (HMD)

[0123] server

[0124] Software used:

[0125] OpenAI API

[0126] Three.js

[0127] 2. System Operation

[0128] 1. User natural language input

[0129] Users use a smartphone or HMD to input instructions in natural language into the system's input interface, such as prompts like "I want to create a new spring display" or "I want to place a flower arch at the entrance."

[0130] 2. Natural Language Analysis

[0131] The server uses OpenAI's Natural Language Processing (NLP) library to parse natural language input from the user, which is used to understand the user's request and obtain the parsed results.

[0132] 3. Generation of 3D space

[0133] The server uses Three.js to generate three-dimensional space and objects based on the analyzed data. The generated objects and layout are then applied to the virtual store.

[0134] 4. Displaying the results

[0135] The generated 3D space and objects are displayed in real time on the user's smartphone or HMD, allowing the user to instantly check the layout and displays of the virtual store based on the instructions they input.

[0136] 3. Specific Examples

[0137] Here are some examples of specific prompts:

[0138] "I want to create a new spring display."

[0139] "I want to put a flower arch at the entrance."

[0140] "Please change the interior of the store to a nautical theme."

[0141] This system allows store owners and marketers to freely change the layout and displays of virtual stores using natural language, without requiring any special skills or expertise, enabling users to easily and efficiently build interactive and creative virtual stores.

[0142] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0143] Step 1:

[0144] The user uses a smartphone or head-mounted display to input instructions in natural language into the system's input interface. These instructions are specific prompt sentences. For example, a user might input an instruction such as "I want to create a new spring display" through the "means for user natural language input."

[0145] Step 2:

[0146] The terminal sends the input natural language to the server. This is done by the "means for analyzing the natural language input by the user." The input data is sent to the server in the form of text, which is the user's instructions.

[0147] Step 3:

[0148] The server uses OpenAI's natural language processing (NLP) library to parse the received natural language instructions. The input is the text of the user's instructions, and the output is structured data of the parsed instructions. This structured data includes keywords for the specified objects and layout. The meaning of the instructions is understood by "a generative modeling means for generating 3D spaces or 3D objects based on the parsed natural language."

[0149] Step 4:

[0150] The server uses Three.js to generate three-dimensional spaces and objects based on the parsed data. The input is the parsed data, and the output is model data for the three-dimensional objects and spaces. This generation process determines how the specified layout and objects will be specifically arranged. The three-dimensional space and objects are generated from the model through "means of displaying the generated three-dimensional space or three-dimensional objects to the user."

[0151] Step 5:

[0152] The server sends the generated model data of the 3D space and objects to the terminal. The input is the 3D model data, and the output is the data sent to the user terminal. Once the terminal receives this data, it proceeds to the next step.

[0153] Step 6:

[0154] The device uses Three.js to render 3D spaces and objects based on the received model data and displays them to the user. The input is model data, and the output is 3D spaces and objects that are visually displayed to the user. This allows users to see the creative results generated based on their instructions in real time.

[0155] Step 7:

[0156] The user checks the layout and display of the virtual store and, if necessary, inputs further correction instructions in natural language. By repeating this cycle, the user can easily customize the design of the virtual store. The input is new correction instructions, and the output is an updated 3D space or object.

[0157] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0158] This invention relates to a system that allows users to intuitively generate three-dimensional spaces and three-dimensional objects using natural language, and further provides more personalized generated results by combining it with an emotion engine that recognizes the user's emotions. This system analyzes the natural language input by the user, and a generative model generates three-dimensional spaces and three-dimensional objects based on that, and visually presents them to the user.

[0159] The user inputs instructions in natural language through the system's interface. For example, say, "I want to create a space with a wizard and where people can go treasure hunting." This input is received by the device, and at the same time, an emotion engine that recognizes the user's emotions is activated.

[0160] The device receives the user's instructions and sends them along with emotional data to the server. The server then analyzes the received natural language instructions and emotional data using a natural language processing (NLP) library and an emotion analysis algorithm, respectively. The analysis step extracts keywords and context to understand the user's desired 3D space and 3D object, as well as the user's emotional state.

[0161] The analyzed natural language data and emotion data are passed to a generative modeling means. The generative model uses a machine learning algorithm to generate a specified three-dimensional space and objects based on the analyzed data. For example, if instructions containing "wizard" and "treasure hunt" are detected and the user's emotional state is excited, the generative model will generate an environment that emphasizes a sense of adventure.

[0162] The generated 3D space and objects are sent to the device by the server. The device uses a 3D rendering engine based on the received data to visually display the generated 3D space and objects to the user. This allows the user to see in real time the creative results generated based on their own instructions and emotions.

[0163] As a concrete example, consider the case where a user inputs "I want to create a scene with a small waterfall in the forest" and indicates an emotional state of "relaxed." The server receives this instruction, analyzes keywords such as "forest," "small waterfall," and "scenery," and the emotional state of "relaxed," and passes them to the generative model. Based on this data, the generative model generates a relaxing environment with a forest, the sound of a waterfall, and a lush green scene with sunlight streaming in. The generated three-dimensional space is sent to the device and visualized directly on the user's screen. This allows the user to immediately visually confirm the scene generated based on their imagination and emotions.

[0164] This invention enables anyone to easily generate and display three-dimensional spaces and objects without requiring advanced skills or specialized knowledge, and also enables personalized creative expression that reflects the user's emotional state.

[0165] The processing flow will be explained below.

[0166] Step 1:

[0167] The user inputs instructions in natural language through the terminal interface, for example, "I want to create a space where there is a wizard and where you can go on a treasure hunt."

[0168] Step 2:

[0169] As soon as the device receives the user's instructions, it uses an emotion engine to collect emotional data from the user's voice, facial expressions, etc., and sends the results to the server.

[0170] Step 3:

[0171] The server receives the natural language instructions and emotion data sent from the terminal, which includes the natural language data and emotion data such as "excited" or "relaxed."

[0172] Step 4:

[0173] The server analyzes the user's instructions using a natural language processing (NLP) library. During this analysis process, the keywords "wizard," "treasure hunt," and "space" are extracted.

[0174] Step 5:

[0175] The server uses an emotion engine to analyze the emotion data and understand the user's emotional state, for example, determining whether they are "excited" or "relaxed."

[0176] Step 6:

[0177] The server passes the analyzed natural language data and emotion data to the generative model, which then generates corresponding 3D spaces and objects based on this data.

[0178] Step 7:

[0179] The generative model uses machine learning algorithms to generate 3D spaces and objects based on the analyzed data. For example, if instructions including "wizard" and "treasure hunt" and an "excited" emotional state are detected, an environment filled with adventure elements will be generated. If the emotional state is "relaxed," an environment emphasizing tranquil scenery will be generated.

[0180] Step 8:

[0181] The generated three-dimensional space and objects are sent to the device by the server, with the generated data adjusted according to the emotion.

[0182] Step 9:

[0183] The device receives the generated data sent from the server, which is then passed to the 3D rendering engine and prepared for display.

[0184] Step 10:

[0185] The device uses a 3D rendering engine to visually display the generated three-dimensional space and objects, allowing users to see the creative results generated in real time based on their instructions and emotions.

[0186] Example 2

[0187] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0188] Conventional systems for generating 3D spaces and 3D objects have limited ways for users to intuitively input instructions, requiring specialized knowledge and skills. Furthermore, it is difficult to provide personalized generated results that take user emotions into account. Furthermore, real-time visualization is difficult, limiting the user experience.

[0189] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0190] In this invention, the server includes means for a user to input natural language, means for analyzing the natural language and emotion input by the user, and generative model means for generating a three-dimensional space or a three-dimensional object based on the analyzed natural language and emotion data. This enables a user to generate a personalized three-dimensional space or object based on natural language and emotion without requiring specialized knowledge.

[0191] "Natural language" refers to a language used by humans as a means of input to a computer by a user.

[0192] "User" refers to a person who uses this system and inputs instructions in natural language.

[0193] "Emotion" refers to data that indicates the user's psychological state and mood, and the system analyzes this data to reflect it in the generated results.

[0194] "Three-dimensional space" refers to spatial data expressed in three-dimensional coordinates, and refers to the visual environment generated by the generative model means.

[0195] A "three-dimensional object" is individual object data that is placed in three-dimensional space and is generated based on user input.

[0196] "Means for analyzing" refers to a method or device for processing natural language and emotion data entered by a user and converting it into understandable information.

[0197] A "generative model means" is a method or device for generating three-dimensional spaces or three-dimensional objects based on data analyzed using a machine learning algorithm.

[0198] The "display means" refers to a method or device for visually presenting the generated three-dimensional space or three-dimensional object to the user.

[0199] This invention relates to a system that allows users to intuitively generate three-dimensional spaces and three-dimensional objects using natural language. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it provides more personalized generated results.

[0200] This system uses the following hardware and software:

[0201] Hardware:

[0202] Terminal: A device that allows users to input and output information, such as a computer, tablet, or smartphone.

[0203] Server: A device with high-performance computing resources that analyzes data and runs generative models.

[0204] Sensors: Cameras, microphones, and other devices used to analyze user emotions.

[0205] software:

[0206] Natural Language Processing (NLP) libraries: For example, spaCy and BERT are used to analyze natural language input from users.

[0207] Sentiment analysis algorithm: For example, we use Google Cloud Natural Language API to analyze user sentiment.

[0208] Generative AI model: Generates 3D spaces and objects based on analysis results using Generative Adversarial Networks (GANs) and other methods.

[0209] 3D rendering engine: Visualize three-dimensional spaces and objects generated using Unity or Unreal Engine.

[0210] Regarding specific program processing

[0211] The user inputs instructions in natural language through the system's interface. For example, say, "I want to create a space with a wizard and where you can go treasure hunting." This input is received by the device, and at the same time, an emotion engine that recognizes the user's emotions is activated. The device then sends this to the server.

[0212] Server Processing

[0213] The server analyzes the received natural language instructions and emotion data, using natural language processing (NLP) libraries to extract keywords and context from the instructions, and emotion analysis algorithms to analyze the user's emotional state (e.g., "excited").

[0214] Running the generative model

[0215] The analyzed natural language data and emotion data are passed to a generative AI model, which then generates the specified 3D space and objects based on this data. For example, if the keywords "wizard" and "treasure hunt" and the emotion of excitement are detected, the generative model will generate an adventure-like environment.

[0216] Visualization

[0217] The generated 3D space and objects are sent by the server to the device, which uses this data to visually display it to the user using a 3D rendering engine, allowing the user to see the creative results in real time.

[0218] Specific examples

[0219] Consider a case where a user inputs "I want to create a scene with a small waterfall in the forest" and indicates an emotional state of "relaxed." Based on this instruction and emotional data, the server performs analysis and passes the data to the generative model. The generative model generates a relaxing environment, with the sounds of a forest and waterfall and a lush green scene with sunlight streaming in. The generated three-dimensional space is sent to the device and visualized on the user's screen. This allows the user to easily check the scene generated based on their imagination and emotions.

[0220] This invention enables users to easily generate and display three-dimensional spaces and objects without requiring advanced technology or specialized knowledge, and also enables personalized creative expression that reflects the user's emotional state.

[0221] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0222] Step 1:

[0223] User natural language input

[0224] Input: The user enters instructions in natural language through the system's interface.

[0225] Processing: The user enters the prompt sentence "I want to create a space where there is a wizard and where you can go on a treasure hunt" into the input field. This data is entered into the terminal.

[0226] Output: Natural language instructions are generated and stored on the device for processing in the next step.

[0227] Specific operation: The user enters natural language using a keyboard or voice input, and presses the send button to send the data to the device.

[0228] Step 2:

[0229] Terminal reception and emotion recognition

[0230] Input: User-entered natural language instructions

[0231] Processing: The device receives the user's input data, and at the same time, the emotion engine analyzes the user's facial expressions and voice using the camera and microphone. The emotion analysis results are packaged together with the natural language input data as a packet.

[0232] Output: Packets of natural language instructions and sentiment data are generated.

[0233] Specific operation: The device collects video and audio data through the camera or microphone, analyzes it in real time, and formats the emotion analysis results and natural language instruction data into packets.

[0234] Step 3:

[0235] Parsing input data by the server

[0236] Input: Packets of natural language instructions and sentiment data

[0237] Processing: The server disassembles the received packet and parses the instructions using a natural language processing (NLP) library. It also parses the sentiment data using a sentiment analysis algorithm (e.g., Google Cloud Natural Language API).

[0238] Output: Generates analyzed keywords, context, and sentiment data.

[0239] How it works: The server collects access logs and uses NLP libraries (e.g., spaCy, BERT) to extract keywords such as "wizard" and "treasure hunt." It then uses a sentiment analysis algorithm to identify emotions such as "excited."

[0240] Step 4:

[0241] 3D space and object generation using generative models

[0242] Input: Analyzed keywords, context, and sentiment data

[0243] Processing: The server inputs the analysis results into a generative AI model (e.g., GANs) to generate 3D spaces and objects.

[0244] Output: Generated 3D spatial data and object data

[0245] How it works: The generative model uses machine learning algorithms to generate an adventurous environment based on the input keywords "wizard" and "treasure hunt" and the emotional state "excitement." The generated data is saved as a 3D object file (e.g., FBX or OBJ format).

[0246] Step 5:

[0247] Generated data sent from the server to the device

[0248] Input: Generated 3D spatial data and object data

[0249] Processing: The server compresses the generated data and transfers it to the terminal.

[0250] Output: Compressed 3D spatial data and object data is sent to the device.

[0251] Specific operation: The server compresses the generated data in ZIP format or similar and sends it to the terminal via HTTP or WebSocket.

[0252] Step 6:

[0253] Terminal visualization

[0254] Input: Compressed data sent from the server

[0255] Processing: The device decompresses the compressed data received and visualizes it using a 3D rendering engine (e.g. Unity, Unreal Engine).

[0256] Output: The generated 3D space and objects are displayed on the user's display.

[0257] What it does: The device decodes the compressed data it receives and inputs it into the 3D rendering engine, which then renders it in real time, displaying an adventurous 3D world on the user's screen.

[0258] This allows users to intuitively generate and display three-dimensional spaces and objects based on natural language and emotions.

[0259] (Application example 2)

[0260] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0261] This invention relates to a system that allows users to intuitively generate three-dimensional spaces and three-dimensional objects and visually confirm them. However, existing systems have a problem in that it is difficult to provide personalized generated results that reflect the user's emotional state. Furthermore, interactive design that takes user emotions into consideration is particularly required in the design of virtual stores, but there is a lack of technology that can achieve this.

[0262] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to input natural language, means for analyzing the natural language input by the user, generative model means for generating a three-dimensional space or a three-dimensional object based on the analyzed natural language and the user's emotion data, and means for displaying the generated three-dimensional space or three-dimensional object to the user. This allows users to easily generate and visualize personalized three-dimensional spaces and three-dimensional objects that reflect their own emotions. Furthermore, an interactive environment that takes user emotions into consideration can be provided in the design of virtual stores.

[0263] A "user" is a person who uses the system to input natural language and instructs the generation of three-dimensional spaces and three-dimensional objects.

[0264] "Natural language" refers to the human language used by people on a daily basis to input instructions and information into a system.

[0265] "Means of analysis" refers to algorithms or programs that analyze input natural language and emotional data and extract the necessary information.

[0266] "Emotion data" is data that represents the user's current emotional state, and adjusts the system's behavior based on this data.

[0267] A "generative model means" is a machine learning algorithm or AI model for generating three-dimensional spaces or three-dimensional objects based on analyzed natural language or emotion data.

[0268] A "three-dimensional space" is a stereoscopic environment or location that is generated based on user instructions.

[0269] A "three-dimensional object" is a solid object or character placed in a three-dimensional space.

[0270] The "display means" refers to a display device or rendering engine for visually presenting the generated three-dimensional space or three-dimensional object to the user.

[0271] A "virtual store" is a virtual store space that users can visit online.

[0272] The present invention relates to a system that allows users to intuitively create three-dimensional spaces and objects using natural language. The system provides personalized results based on the user's input and emotional data. The present invention is particularly useful for designing virtual stores.

[0273] System configuration

[0274] The system consists of the following main components:

[0275] User input:

[0276] A device (e.g., smartphone, tablet, PC) that allows a user to input natural language.

[0277] Natural language analysis means:

[0278] Software for analyzing natural language input (e.g., spaCy, BERT).

[0279] Emotion analysis means:

[0280] Software for analyzing user emotion data (e.g., TensorFlow, OpenAI GPT-4).

[0281] Generative model means:

[0282] Generative models for generating 3D spaces and objects based on natural language and emotion data (e.g., Unity3D, Blender).

[0283] Display means:

[0284] Software for visually displaying generated 3D spaces and objects (e.g., WebGL, Three.js).

[0285] Program processing

[0286] The server analyzes the data received through the user's natural language input. It uses natural language processing libraries such as spaCy and BERT as its natural language analysis method. The analyzed keywords and context are passed to a sentiment analysis method, which uses TensorFlow and OpenAI GPT-4 to determine the user's emotional state.

[0287] The analyzed natural language and emotion data are passed to a generative modeling means, which uses a generative AI model such as Unity3D or Blender to generate 3D spaces and objects according to the user's instructions. For example, if a user inputs "I want to create a relaxing cafe" and the emotion data indicates a relaxed state, the generative model will automatically design a cafe with lush plants, wooden furniture, and calming music.

[0288] The generated 3D spaces and objects are displayed in real time on the user's device using WebGL and Three.js, allowing users to instantly see personalized creative results based on their instructions and emotions.

[0289] Specific examples

[0290] For example, if a user types, "I want a virtual cafe with lots of greenery, nature, and relaxation," and indicates the emotion of "relaxation," the app will automatically generate and visually display a virtual cafe with lush plants, wooden furniture, and calming music.

[0291] Example prompt sentence:

[0292] "Please generate a virtual cafe. The user requested a relaxing space with lots of greenery."

[0293] "The spacious layout creates an airy space."

[0294] As described above, the present invention intuitively generates and displays advanced three-dimensional spaces and objects based on the user's natural language input and emotional data, providing excellent personalization functions, particularly in the design of virtual stores.

[0295] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0296] Step 1:

[0297] Users input their virtual store design preferences in natural language using a device (e.g., smartphone, tablet, or PC). This input includes specific requests, images, and emotions. The input data is captured in text format on the device.

[0298] Step 2:

[0299] The device sends the received natural language input to the server. The server receives this input data and analyzes the text using natural language analysis tools (e.g., spaCy, BERT). As a result of the analysis, keywords and contextual information are extracted. The input is natural language text, and the output is analyzed keywords and contextual information.

[0300] Step 3:

[0301] The server uses emotion analysis tools (e.g., TensorFlow, OpenAI GPT-4) to acquire and analyze the user's emotion data when natural language input is used. The input is natural language text and user information, and the output is emotion data that indicates the user's emotional state.

[0302] Step 4:

[0303] The analyzed natural language data and emotion data are input into a generative model. The server uses a generative modeling tool (e.g., Unity3D, Blender) to generate a 3D space or a 3D object based on this data. The input is keywords, contextual information, and emotion data, and the output is design data for the 3D space or object.

[0304] Step 5:

[0305] The server sends the generated design data to the terminal. The terminal uses a display method (e.g., WebGL, Three.js) to visually display the generated three-dimensional space and objects to the user in real time. The input is the design data, and the output is the visually displayed three-dimensional space and objects.

[0306] Step 6:

[0307] The user can check the 3D space and objects generated in real time and make corrections as needed. By repeating the process from step 1, the optimal virtual store that meets the user's requirements is completed.

[0308] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0309] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0310] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0311] [Second embodiment]

[0312] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0313] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0314] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0315] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0316] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0317] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0318] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0319] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0320] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0321] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0322] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0323] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0324] The present invention relates to a system that enables users to intuitively generate three-dimensional spaces and three-dimensional objects using natural language. This system analyzes the natural language input by the user, and a generative model generates three-dimensional spaces and three-dimensional objects based on that, and presents them visually to the user.

[0325] The user inputs instructions in natural language through the system interface. For example, they might input, "I want to create a space with a wizard and where people can go on a treasure hunt." This input is received by the terminal and immediately sent to the server.

[0326] The server uses a natural language processing (NLP) library to parse the received natural language instructions. This parsing step extracts keywords and context to understand the details of the 3D space or 3D object requested by the user. The parsed data is passed to a generative modeling facility.

[0327] A generative model generates specified 3D spaces and objects based on analyzed data using machine learning algorithms. This generation process automatically arranges multiple objects and environments according to user instructions to create a realistic 3D space. For example, if the instructions include "wizard" and "treasure hunt," the generative model generates an explorable environment containing a wizard character and a treasure chest.

[0328] The generated 3D space and objects are sent to the device by the server. The device uses a 3D rendering engine based on the received data to visually display the generated 3D space and objects to the user. This allows the user to see in real time the creative results generated based on their instructions.

[0329] As a concrete example, consider the case where a user inputs, "I want to create a scene with a small waterfall in the forest." The server receives this instruction, extracts keywords such as "forest," "small waterfall," and "scenery," and passes them to the generative model. Based on these keywords, the generative model generates a scene with forest trees, plants, and a small waterfall. The generated three-dimensional space is sent to the device and visualized directly on the user's screen. This allows the user to immediately visually confirm their imagination.

[0330] This invention makes it possible for anyone to easily generate and display three-dimensional spaces and objects without the need for conventional advanced technology or specialized knowledge, significantly lowering the barrier to creative expression in the metaverse.

[0331] The processing flow will be explained below.

[0332] Step 1:

[0333] The user inputs instructions in natural language through the terminal interface, for example, "I want to create a space where there is a wizard and where you can go on a treasure hunt."

[0334] Step 2:

[0335] The device receives the user's instructions and sends them to the server. Specifically, the instructions are sent to the server using an API.

[0336] Step 3:

[0337] The server receives the instruction sent from the terminal, and the received natural language instruction is stored for natural language analysis in the next step.

[0338] Step 4:

[0339] The server uses a natural language processing (NLP) library to analyze the user's instructions. During this analysis process, meanings and keywords are extracted from the user's instructions. For example, keywords such as "wizard," "treasure hunt," and "space" are extracted.

[0340] Step 5:

[0341] The server issues instructions to the generative model based on the analyzed data, and the generative model prepares to generate 3D spaces and objects according to the user's instructions.

[0342] Step 6:

[0343] A generative model uses machine learning algorithms to generate 3D spaces and objects based on the analyzed data, such as a "wizard" character or an environment containing a treasure chest for a "treasure hunt."

[0344] Step 7:

[0345] The generated 3D space and objects are sent to the terminal by the server, where the generated data is formatted so that it can be displayed correctly on the terminal.

[0346] Step 8:

[0347] The device receives the generated data sent from the server, which is then passed to the 3D rendering engine.

[0348] Step 9:

[0349] The device uses a 3D rendering engine to visually display the generated three-dimensional space and objects, allowing users to see the creative results generated in real time based on their instructions.

[0350] Step 10:

[0351] The user can visually check the generated 3D space and objects and input additional instructions (corrections, updates, etc.) as needed, enabling further interactive operation.

[0352] Example 1

[0353] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0354] Conventional systems for generating 3D spaces and 3D objects require advanced technology and specialized knowledge, making them difficult for general users to operate intuitively. Furthermore, there are few systems that allow users to quickly generate 3D spaces by directly instructing them in natural language, and there has been a demand for an improved user experience.

[0355] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0356] In this invention, the server includes means for a user to input natural language, means for analyzing the natural language input by the user, and generative model means for generating a three-dimensional space or a three-dimensional object based on the analyzed natural language. This enables a user to intuitively and efficiently generate a three-dimensional space or an object simply by issuing instructions using natural language, without requiring specialized knowledge.

[0357] "User" refers to a person or organization that uses the system to generate three-dimensional spaces or three-dimensional objects.

[0358] "Natural language" refers to languages ​​that humans use in their daily lives, such as Japanese and English, and is different from programming languages.

[0359] An "input means" is a device or software that provides an interface for a user to input natural language, such as a text box or a voice recognition system.

[0360] "Means for analyzing" refers to a device or software that provides the technology to understand the natural language entered by the user through syntactic and contextual analysis and to enable the system to identify the requested content.

[0361] "Generative model means" refers to an algorithm or program for automatically generating three-dimensional spaces or three-dimensional objects based on parsed natural language instructions.

[0362] "Displaying means" refers to a device or software that visually presents the generated three-dimensional space or three-dimensional object to a user. For example, a display or a VR headset.

[0363] "Transmitting means" refers to a device or software that provides a technique for transmitting data of a three-dimensional space or three-dimensional object generated by a server to a terminal.

[0364] "Rendering means" refers to a device or software that generates high-quality images in real time based on the three-dimensional space or three-dimensional object data received by the terminal and visually displays them to the user.

[0365] A "machine learning algorithm" is a computational method or model that allows a system to learn from data and improve the accuracy of analysis and generation.

[0366] This invention relates to a system that allows users to intuitively generate three-dimensional spaces and three-dimensional objects using natural language. This system analyzes the natural language input by the user, and a generative AI model generates three-dimensional spaces and three-dimensional objects based on that, and presents them visually to the user.

[0367] Configuration and Operation

[0368] User Input

[0369] The user inputs instructions in natural language through the device interface. For example, they can input a prompt such as, "I want to create a space with a wizard and a treasure hunt." This prompt can be entered in a text box or recorded by voice input.

[0370] Sending data

[0371] The device receives the user's natural language input and immediately sends it to the server, using communication protocols such as web sockets and HTTP requests.

[0372] Natural Language Analysis

[0373] The server uses natural language processing (NLP) libraries, such as Python's NLTK or spaCy, to parse the received natural language instructions. It then extracts keywords and context to understand the details of the 3D space or object the user is requesting. For example, elements such as "wizard," "treasure hunt," and "space" are analyzed.

[0374] Processing the generative model

[0375] The analyzed data is passed to a generative AI model. The server uses machine learning algorithms such as GPT-3 and DALL-E to generate 3D spaces and objects. During this generation process, multiple objects and environments are automatically placed based on the user's instructions to create a realistic 3D space. For example, based on keywords such as "wizard" and "treasure hunt," the generative model creates an exploration space that includes a wizard character and a treasure chest.

[0376] Receiving and displaying data

[0377] The server sends the generated 3D space and object data to the device, which receives the data and uses Unity's 3D rendering engine to visually display the 3D space and objects. This allows users to see the creative results generated based on their instructions in real time.

[0378] Specific examples

[0379] As a concrete example, consider the case where a user inputs, "I want to create a scene with a small waterfall in the forest." The server receives this instruction, extracts keywords such as "forest," "small waterfall," and "scenery," and passes them to the generative model. Based on these keywords, the generative model generates a scene with forest trees, plants, and a small waterfall. The generated three-dimensional space is sent to the device and visualized directly on the user's screen. This allows the user to immediately visually confirm their imagination.

[0380] This invention makes it possible for anyone to easily generate and display three-dimensional spaces and objects without the need for conventional advanced technology or specialized knowledge, significantly lowering the barrier to creative expression in the metaverse.

[0381] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0382] Step 1:

[0383] The user inputs instructions in natural language through the terminal interface. A possible prompt might be, "I want to create a space where wizards can go on treasure hunts." This input natural language is acquired from the terminal's text box or voice input system. The input in this step is the user's natural language, and the output is raw text data. The terminal acquires this text data and prepares it to be passed to the next processing step.

[0384] Step 2:

[0385] The device receives the natural language instructions entered by the user and immediately sends them to the server. Data is communicated using WebSockets or HTTP requests. The input in this step is the text data obtained in step 1, and the output is that text data being sent to the server. The device correctly formats the user's input data and sends it to the server.

[0386] Step 3:

[0387] The server analyzes the received natural language instructions. This analysis uses a natural language processing (NLP) library such as Python's NLTK or spaCy. The input is the text data of the user's natural language instructions sent in step 2. The server analyzes this text data and extracts keywords and context. For example, elements such as "wizard," "treasure hunt," and "space" are extracted. The output is the analyzed keywords and context information.

[0388] Step 4:

[0389] The server prepares data for a generative AI model based on the analyzed keywords and contextual information. Specifically, it uses a generative model such as GPT-3 or DALL-E to generate 3D spaces and objects. The input in this step is the keywords and contextual information extracted in step 3, and the output is data on the 3D spaces and objects generated by the generative AI model. The server converts the data into a format suitable for the generative model and inputs it into the model.

[0390] Step 5:

[0391] The generative AI model generates the specified 3D space and objects based on the data. In this generation process, multiple objects and environments are automatically placed according to the user's instructions. For example, based on the keywords "wizard" and "treasure hunt," an exploration space including a wizard character and a treasure chest is constructed. The input in this step is the data given to the generative model in step 4, and the output is the generated 3D space and object data.

[0392] Step 6:

[0393] The server sends the generated 3D space and object data to the device. Data communication is performed using Web sockets and HTTP requests. The input in this step is the data generated in step 5, and the output is that data being sent to the device. The server sends the generated data to the device in the appropriate format and confirms that communication is complete.

[0394] Step 7:

[0395] The device visually displays the 3D space and objects generated using Unity, a 3D rendering engine, based on the received data. The input is the generated data sent in step 6, and the output is the 3D space and objects displayed on the user's screen. The device renders the received data and executes the operations to display it to the user in real time.

[0396] (Application example 1)

[0397] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0398] Conventional virtual store and three-dimensional space customization methods require advanced technology and specialized knowledge, making them difficult for general users to operate. Furthermore, they lack an intuitive interface for quickly changing the layout and displays within the store. As a result, virtual store operations and marketing activities are restricted. This invention aims to solve these problems by providing a means for users without special skills to easily customize the layout and displays of a virtual store using natural language.

[0399] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0400] In this invention, the server includes means for a user to input natural language, means for analyzing the natural language input by the user, generative model means for generating a three-dimensional space or a three-dimensional object based on the analyzed natural language, means for displaying the generated three-dimensional space or the three-dimensional object to the user, and means for customizing the layout or display of a virtual store based on the analyzed natural language, thereby enabling the user to intuitively customize the layout or display of a virtual store using natural language.

[0401] A "means for a user to input natural language" is something that provides an interface for a user to input instructions to a system using human language.

[0402] The "means for analyzing the natural language input by the user" refers to a process or technology for understanding the natural language input by the user and deciphering its meaning and intent.

[0403] "Generative modeling means for generating 3D spaces or 3D objects based on parsed natural language" refers to algorithms or techniques that create or construct 3D environments or objects based on the results of parsing natural language.

[0404] "Means for displaying the generated three-dimensional space or three-dimensional objects to the user" refers to display or rendering technologies that provide a visual representation of the generated three-dimensional environment or objects to the user.

[0405] "Means for customizing the layout or display of a virtual store based on analyzed natural language" refers to techniques and methods for changing and optimizing the layout and display content within a virtual store based on instructions in the user's natural language.

[0406] This invention relates to a system that allows users to intuitively create and customize three-dimensional spaces and objects using natural language, allowing them to easily change the layout and displays of virtual stores.

[0407] 1. System Configuration

[0408] Hardware and Software

[0409] Hardware used:

[0410] Smartphone

[0411] Head-mounted display (HMD)

[0412] server

[0413] Software used:

[0414] OpenAI API

[0415] Three.js

[0416] 2. System Operation

[0417] 1. User natural language input

[0418] Users use a smartphone or HMD to input instructions in natural language into the system's input interface, such as prompts like "I want to create a new spring display" or "I want to place a flower arch at the entrance."

[0419] 2. Natural Language Analysis

[0420] The server uses OpenAI's Natural Language Processing (NLP) library to parse natural language input from the user, which is used to understand the user's request and obtain the parsed results.

[0421] 3. Generation of 3D space

[0422] The server uses Three.js to generate three-dimensional space and objects based on the analyzed data. The generated objects and layout are then applied to the virtual store.

[0423] 4. Displaying the results

[0424] The generated 3D space and objects are displayed in real time on the user's smartphone or HMD, allowing the user to instantly check the layout and displays of the virtual store based on the instructions they input.

[0425] 3. Specific Examples

[0426] Here are some examples of specific prompts:

[0427] "I want to create a new spring display."

[0428] "I want to put a flower arch at the entrance."

[0429] "Please change the interior of the store to a nautical theme."

[0430] This system allows store owners and marketers to freely change the layout and displays of virtual stores using natural language, without requiring any special skills or expertise, enabling users to easily and efficiently build interactive and creative virtual stores.

[0431] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0432] Step 1:

[0433] The user uses a smartphone or head-mounted display to input instructions in natural language into the system's input interface. These instructions are specific prompt sentences. For example, a user might input an instruction such as "I want to create a new spring display" through the "means for user natural language input."

[0434] Step 2:

[0435] The terminal sends the input natural language to the server. This is done by the "means for analyzing the natural language input by the user." The input data is sent to the server in the form of text, which is the user's instructions.

[0436] Step 3:

[0437] The server uses OpenAI's natural language processing (NLP) library to parse the received natural language instructions. The input is the text of the user's instructions, and the output is structured data of the parsed instructions. This structured data includes keywords for the specified objects and layout. The meaning of the instructions is understood by "a generative modeling means for generating 3D spaces or 3D objects based on the parsed natural language."

[0438] Step 4:

[0439] The server uses Three.js to generate three-dimensional spaces and objects based on the parsed data. The input is the parsed data, and the output is model data for the three-dimensional objects and spaces. This generation process determines how the specified layout and objects will be specifically arranged. The three-dimensional space and objects are generated from the model through "means of displaying the generated three-dimensional space or three-dimensional objects to the user."

[0440] Step 5:

[0441] The server sends the generated model data of the 3D space and objects to the terminal. The input is the 3D model data, and the output is the data sent to the user terminal. Once the terminal receives this data, it proceeds to the next step.

[0442] Step 6:

[0443] The device uses Three.js to render 3D spaces and objects based on the received model data and displays them to the user. The input is model data, and the output is 3D spaces and objects that are visually displayed to the user. This allows users to see the creative results generated based on their instructions in real time.

[0444] Step 7:

[0445] The user checks the layout and display of the virtual store and, if necessary, inputs further correction instructions in natural language. By repeating this cycle, the user can easily customize the design of the virtual store. The input is new correction instructions, and the output is an updated 3D space or object.

[0446] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0447] This invention relates to a system that allows users to intuitively generate three-dimensional spaces and three-dimensional objects using natural language, and further provides more personalized generated results by combining it with an emotion engine that recognizes the user's emotions. This system analyzes the natural language input by the user, and a generative model generates three-dimensional spaces and three-dimensional objects based on that, and visually presents them to the user.

[0448] The user inputs instructions in natural language through the system's interface. For example, say, "I want to create a space with a wizard and where people can go treasure hunting." This input is received by the device, and at the same time, an emotion engine that recognizes the user's emotions is activated.

[0449] The device receives the user's instructions and sends them along with emotional data to the server. The server then analyzes the received natural language instructions and emotional data using a natural language processing (NLP) library and an emotion analysis algorithm, respectively. The analysis step extracts keywords and context to understand the user's desired 3D space and 3D object, as well as the user's emotional state.

[0450] The analyzed natural language data and emotion data are passed to a generative modeling means. The generative model uses a machine learning algorithm to generate a specified three-dimensional space and objects based on the analyzed data. For example, if instructions containing "wizard" and "treasure hunt" are detected and the user's emotional state is excited, the generative model will generate an environment that emphasizes a sense of adventure.

[0451] The generated 3D space and objects are sent to the device by the server. The device uses a 3D rendering engine based on the received data to visually display the generated 3D space and objects to the user. This allows the user to see in real time the creative results generated based on their own instructions and emotions.

[0452] As a concrete example, consider the case where a user inputs "I want to create a scene with a small waterfall in the forest" and indicates an emotional state of "relaxed." The server receives this instruction, analyzes keywords such as "forest," "small waterfall," and "scenery," and the emotional state of "relaxed," and passes them to the generative model. Based on this data, the generative model generates a relaxing environment with a forest, the sound of a waterfall, and a lush green scene with sunlight streaming in. The generated three-dimensional space is sent to the device and visualized directly on the user's screen. This allows the user to immediately visually confirm the scene generated based on their imagination and emotions.

[0453] This invention enables anyone to easily generate and display three-dimensional spaces and objects without requiring advanced skills or specialized knowledge, and also enables personalized creative expression that reflects the user's emotional state.

[0454] The processing flow will be explained below.

[0455] Step 1:

[0456] The user inputs instructions in natural language through the terminal interface, for example, "I want to create a space where there is a wizard and where you can go on a treasure hunt."

[0457] Step 2:

[0458] As soon as the device receives the user's instructions, it uses an emotion engine to collect emotional data from the user's voice, facial expressions, etc., and sends the results to the server.

[0459] Step 3:

[0460] The server receives the natural language instructions and emotion data sent from the terminal, which includes the natural language data and emotion data such as "excited" or "relaxed."

[0461] Step 4:

[0462] The server analyzes the user's instructions using a natural language processing (NLP) library. During this analysis process, the keywords "wizard," "treasure hunt," and "space" are extracted.

[0463] Step 5:

[0464] The server uses an emotion engine to analyze the emotion data and understand the user's emotional state, for example, determining whether they are "excited" or "relaxed."

[0465] Step 6:

[0466] The server passes the analyzed natural language data and emotion data to a generative model, which then generates corresponding 3D spaces and objects based on the data.

[0467] Step 7:

[0468] The generative model uses machine learning algorithms to generate 3D spaces and objects based on the analyzed data. For example, if instructions including "wizard" and "treasure hunt" and an "excited" emotional state are detected, an environment filled with adventure elements will be generated. If the emotional state is "relaxed," an environment emphasizing tranquil scenery will be generated.

[0469] Step 8:

[0470] The generated three-dimensional space and objects are sent to the device by the server, with the generated data adjusted according to the emotion.

[0471] Step 9:

[0472] The device receives the generated data sent from the server, which is then passed to the 3D rendering engine and prepared for display.

[0473] Step 10:

[0474] The device uses a 3D rendering engine to visually display the generated three-dimensional space and objects, allowing users to see the creative results generated in real time based on their instructions and emotions.

[0475] Example 2

[0476] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0477] Conventional systems for generating 3D spaces and 3D objects have limited ways for users to intuitively input instructions, requiring specialized knowledge and skills. Furthermore, it is difficult to provide personalized generated results that take user emotions into account. Furthermore, real-time visualization is difficult, limiting the user experience.

[0478] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0479] In this invention, the server includes means for a user to input natural language, means for analyzing the natural language and emotion input by the user, and generative model means for generating a three-dimensional space or a three-dimensional object based on the analyzed natural language and emotion data. This enables a user to generate a personalized three-dimensional space or object based on natural language and emotion without requiring specialized knowledge.

[0480] "Natural language" refers to a language used by humans as a means of input to a computer by a user.

[0481] "User" refers to a person who uses this system and inputs instructions in natural language.

[0482] "Emotion" refers to data that indicates the user's psychological state and mood, and the system analyzes this data to reflect it in the generated results.

[0483] "Three-dimensional space" refers to spatial data expressed in three-dimensional coordinates, and refers to the visual environment generated by the generative model means.

[0484] A "three-dimensional object" is individual object data that is placed in three-dimensional space and is generated based on user input.

[0485] "Means for analyzing" refers to a method or device for processing natural language and emotion data entered by a user and converting it into understandable information.

[0486] A "generative model means" is a method or device for generating three-dimensional spaces or three-dimensional objects based on data analyzed using a machine learning algorithm.

[0487] The "display means" refers to a method or device for visually presenting the generated three-dimensional space or three-dimensional object to the user.

[0488] This invention relates to a system that allows users to intuitively generate three-dimensional spaces and three-dimensional objects using natural language. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it provides more personalized generated results.

[0489] This system uses the following hardware and software:

[0490] Hardware:

[0491] Terminal: A device that allows users to input and output information, such as a computer, tablet, or smartphone.

[0492] Server: A device with high-performance computing resources that analyzes data and runs generative models.

[0493] Sensors: Cameras, microphones, and other devices used to analyze user emotions.

[0494] software:

[0495] Natural Language Processing (NLP) libraries: For example, spaCy and BERT are used to analyze natural language input from users.

[0496] Sentiment analysis algorithm: For example, we use Google Cloud Natural Language API to analyze user sentiment.

[0497] Generative AI model: Generates 3D spaces and objects based on analysis results using Generative Adversarial Networks (GANs) and other methods.

[0498] 3D rendering engine: Visualize three-dimensional spaces and objects generated using Unity or Unreal Engine.

[0499] Regarding specific program processing

[0500] The user inputs instructions in natural language through the system's interface. For example, say, "I want to create a space with a wizard and where you can go treasure hunting." This input is received by the device, and at the same time, an emotion engine that recognizes the user's emotions is activated. The device then sends this to the server.

[0501] Server Processing

[0502] The server analyzes the received natural language instructions and emotion data, using natural language processing (NLP) libraries to extract keywords and context from the instructions, and emotion analysis algorithms to analyze the user's emotional state (e.g., "excited").

[0503] Running the generative model

[0504] The analyzed natural language data and emotion data are passed to a generative AI model, which then generates the specified 3D space and objects based on this data. For example, if the keywords "wizard" and "treasure hunt" and the emotion of excitement are detected, the generative model will generate an adventure-like environment.

[0505] Visualization

[0506] The generated 3D space and objects are sent by the server to the device, which uses this data to visually display it to the user using a 3D rendering engine, allowing the user to see the creative results in real time.

[0507] Specific examples

[0508] Consider a case where a user inputs "I want to create a scene with a small waterfall in the forest" and indicates an emotional state of "relaxed." Based on this instruction and emotional data, the server performs analysis and passes the data to the generative model. The generative model generates a relaxing environment, with the sounds of a forest and waterfall and a lush green scene with sunlight streaming in. The generated three-dimensional space is sent to the device and visualized on the user's screen. This allows the user to easily check the scene generated based on their imagination and emotions.

[0509] This invention enables users to easily generate and display three-dimensional spaces and objects without requiring advanced technology or specialized knowledge, and also enables personalized creative expression that reflects the user's emotional state.

[0510] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0511] Step 1:

[0512] User natural language input

[0513] Input: The user enters instructions in natural language through the system's interface.

[0514] Processing: The user enters the prompt sentence "I want to create a space where there is a wizard and where you can go on a treasure hunt" into the input field. This data is entered into the terminal.

[0515] Output: Natural language instructions are generated and stored on the device for processing in the next step.

[0516] Specific operation: The user enters natural language using a keyboard or voice input, and presses the send button to send the data to the device.

[0517] Step 2:

[0518] Terminal reception and emotion recognition

[0519] Input: User-entered natural language instructions

[0520] Processing: The device receives the user's input data, and at the same time, the emotion engine analyzes the user's facial expressions and voice using the camera and microphone. The emotion analysis results are packaged together with the natural language input data as a packet.

[0521] Output: Packets of natural language instructions and sentiment data are generated.

[0522] Specific operation: The device collects video and audio data through the camera or microphone, analyzes it in real time, and formats the emotion analysis results and natural language instruction data into packets.

[0523] Step 3:

[0524] Parsing input data by the server

[0525] Input: Packets of natural language instructions and sentiment data

[0526] Processing: The server disassembles the received packet and parses the instructions using a natural language processing (NLP) library. It also parses the sentiment data using a sentiment analysis algorithm (e.g., Google Cloud Natural Language API).

[0527] Output: Generates analyzed keywords, context, and sentiment data.

[0528] How it works: The server collects access logs and uses NLP libraries (e.g., spaCy, BERT) to extract keywords such as "wizard" and "treasure hunt." It then uses a sentiment analysis algorithm to identify emotions such as "excited."

[0529] Step 4:

[0530] 3D space and object generation using generative models

[0531] Input: Analyzed keywords, context, and sentiment data

[0532] Processing: The server inputs the analysis results into a generative AI model (e.g., GANs) to generate 3D spaces and objects.

[0533] Output: Generated 3D spatial data and object data

[0534] How it works: The generative model uses machine learning algorithms to generate an adventurous environment based on the input keywords "wizard" and "treasure hunt" and the emotional state "excitement." The generated data is saved as a 3D object file (e.g., FBX or OBJ format).

[0535] Step 5:

[0536] Generated data sent from the server to the device

[0537] Input: Generated 3D spatial data and object data

[0538] Processing: The server compresses the generated data and transfers it to the terminal.

[0539] Output: Compressed 3D spatial data and object data is sent to the device.

[0540] Specific operation: The server compresses the generated data in ZIP format or similar and sends it to the terminal via HTTP or WebSocket.

[0541] Step 6:

[0542] Terminal visualization

[0543] Input: Compressed data sent from the server

[0544] Processing: The device decompresses the compressed data received and visualizes it using a 3D rendering engine (e.g. Unity, Unreal Engine).

[0545] Output: The generated 3D space and objects are displayed on the user's display.

[0546] What it does: The device decodes the compressed data it receives and inputs it into the 3D rendering engine, which then renders it in real time, displaying an adventurous 3D world on the user's screen.

[0547] This allows users to intuitively generate and display three-dimensional spaces and objects based on natural language and emotions.

[0548] (Application example 2)

[0549] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0550] This invention relates to a system that allows users to intuitively generate three-dimensional spaces and three-dimensional objects and visually confirm them. However, existing systems have a problem in that it is difficult to provide personalized generated results that reflect the user's emotional state. Furthermore, interactive design that takes user emotions into consideration is particularly required in the design of virtual stores, but there is a lack of technology that can achieve this.

[0551] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to input natural language, means for analyzing the natural language input by the user, generative model means for generating a three-dimensional space or a three-dimensional object based on the analyzed natural language and the user's emotion data, and means for displaying the generated three-dimensional space or three-dimensional object to the user. This allows users to easily generate and visualize personalized three-dimensional spaces and three-dimensional objects that reflect their own emotions. Furthermore, an interactive environment that takes user emotions into consideration can be provided in the design of virtual stores.

[0552] A "user" is a person who uses the system to input natural language and instructs the generation of three-dimensional spaces and three-dimensional objects.

[0553] "Natural language" refers to the human language used by people on a daily basis to input instructions and information into a system.

[0554] "Means of analysis" refers to algorithms or programs that analyze input natural language and emotional data and extract the necessary information.

[0555] "Emotion data" is data that represents the user's current emotional state, and adjusts the system's behavior based on this data.

[0556] A "generative model means" is a machine learning algorithm or AI model for generating three-dimensional spaces or three-dimensional objects based on analyzed natural language or emotion data.

[0557] A "three-dimensional space" is a stereoscopic environment or location that is generated based on user instructions.

[0558] A "three-dimensional object" is a solid object or character placed in a three-dimensional space.

[0559] The "display means" refers to a display device or rendering engine for visually presenting the generated three-dimensional space or three-dimensional object to the user.

[0560] A "virtual store" is a virtual store space that users can visit online.

[0561] The present invention relates to a system that allows users to intuitively create three-dimensional spaces and objects using natural language. The system provides personalized results based on the user's input and emotional data. The present invention is particularly useful for designing virtual stores.

[0562] System configuration

[0563] The system consists of the following main components:

[0564] User input:

[0565] A device (e.g., smartphone, tablet, PC) that allows a user to input natural language.

[0566] Natural language analysis means:

[0567] Software for analyzing natural language input (e.g., spaCy, BERT).

[0568] Emotion analysis means:

[0569] Software for analyzing user emotion data (e.g., TensorFlow, OpenAI GPT-4).

[0570] Generative model means:

[0571] Generative models for generating 3D spaces and objects based on natural language and emotion data (e.g., Unity3D, Blender).

[0572] Display means:

[0573] Software for visually displaying generated 3D spaces and objects (e.g., WebGL, Three.js).

[0574] Program processing

[0575] The server analyzes the data received through the user's natural language input. It uses natural language processing libraries such as spaCy and BERT as its natural language analysis method. The analyzed keywords and context are passed to a sentiment analysis method, which uses TensorFlow and OpenAI GPT-4 to determine the user's emotional state.

[0576] The analyzed natural language and emotion data are passed to a generative modeling means, which uses a generative AI model such as Unity3D or Blender to generate 3D spaces and objects according to the user's instructions. For example, if a user inputs "I want to create a relaxing cafe" and the emotion data indicates a relaxed state, the generative model will automatically design a cafe with lush plants, wooden furniture, and calming music.

[0577] The generated 3D spaces and objects are displayed in real time on the user's device using WebGL and Three.js, allowing users to instantly see personalized creative results based on their instructions and emotions.

[0578] Specific examples

[0579] For example, if a user types, "I want a virtual cafe with lots of greenery, nature, and relaxation," and indicates the emotion of "relaxation," the app will automatically generate and visually display a virtual cafe with lush plants, wooden furniture, and calming music.

[0580] Example prompt sentence:

[0581] "Please generate a virtual cafe. The user requested a relaxing space with lots of greenery."

[0582] "The spacious layout creates an airy space."

[0583] As described above, the present invention intuitively generates and displays advanced three-dimensional spaces and objects based on the user's natural language input and emotional data, providing excellent personalization functions, particularly in the design of virtual stores.

[0584] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0585] Step 1:

[0586] Users input their virtual store design preferences in natural language using a device (e.g., smartphone, tablet, or PC). This input includes specific requests, images, and emotions. The input data is captured in text format on the device.

[0587] Step 2:

[0588] The device sends the received natural language input to the server. The server receives this input data and analyzes the text using natural language analysis tools (e.g., spaCy, BERT). As a result of the analysis, keywords and contextual information are extracted. The input is natural language text, and the output is analyzed keywords and contextual information.

[0589] Step 3:

[0590] The server uses emotion analysis tools (e.g., TensorFlow, OpenAI GPT-4) to acquire and analyze the user's emotion data when natural language input is used. The input is natural language text and user information, and the output is emotion data that indicates the user's emotional state.

[0591] Step 4:

[0592] The analyzed natural language data and emotion data are input into a generative model. The server uses a generative modeling tool (e.g., Unity3D, Blender) to generate a 3D space or a 3D object based on this data. The input is keywords, contextual information, and emotion data, and the output is design data for the 3D space or object.

[0593] Step 5:

[0594] The server sends the generated design data to the terminal. The terminal uses a display method (e.g., WebGL, Three.js) to visually display the generated three-dimensional space and objects to the user in real time. The input is the design data, and the output is the visually displayed three-dimensional space and objects.

[0595] Step 6:

[0596] The user can check the 3D space and objects generated in real time and make corrections as necessary. By repeating the process from step 1, the optimal virtual store that meets the user's requirements is completed.

[0597] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0598] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0599] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0600] [Third embodiment]

[0601] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0602] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0603] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0604] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0605] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0606] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0607] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0608] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0609] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0610] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0611] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0612] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0613] The present invention relates to a system that enables users to intuitively generate three-dimensional spaces and three-dimensional objects using natural language. This system analyzes the natural language input by the user, and a generative model generates three-dimensional spaces and three-dimensional objects based on that, and presents them visually to the user.

[0614] The user inputs instructions in natural language through the system interface. For example, they might input, "I want to create a space with a wizard and where people can go on a treasure hunt." This input is received by the terminal and immediately sent to the server.

[0615] The server uses a natural language processing (NLP) library to parse the received natural language instructions. This parsing step extracts keywords and context to understand the details of the 3D space or 3D object requested by the user. The parsed data is passed to a generative modeling facility.

[0616] A generative model generates specified 3D spaces and objects based on analyzed data using machine learning algorithms. This generation process automatically arranges multiple objects and environments according to user instructions to create a realistic 3D space. For example, if the instructions include "wizard" and "treasure hunt," the generative model generates an explorable environment containing a wizard character and a treasure chest.

[0617] The generated 3D space and objects are sent to the device by the server. The device uses a 3D rendering engine based on the received data to visually display the generated 3D space and objects to the user. This allows the user to see in real time the creative results generated based on their instructions.

[0618] As a concrete example, consider the case where a user inputs, "I want to create a scene with a small waterfall in the forest." The server receives this instruction, extracts keywords such as "forest," "small waterfall," and "scenery," and passes them to the generative model. Based on these keywords, the generative model generates a scene with forest trees, plants, and a small waterfall. The generated three-dimensional space is sent to the device and visualized directly on the user's screen. This allows the user to immediately visually confirm their imagination.

[0619] This invention makes it possible for anyone to easily generate and display three-dimensional spaces and objects without the need for conventional advanced technology or specialized knowledge, significantly lowering the barrier to creative expression in the metaverse.

[0620] The processing flow will be explained below.

[0621] Step 1:

[0622] The user inputs instructions in natural language through the terminal interface, for example, "I want to create a space where there is a wizard and where you can go on a treasure hunt."

[0623] Step 2:

[0624] The device receives the user's instructions and sends them to the server. Specifically, the instructions are sent to the server using an API.

[0625] Step 3:

[0626] The server receives the instruction sent from the terminal, and the received natural language instruction is stored for natural language analysis in the next step.

[0627] Step 4:

[0628] The server uses a natural language processing (NLP) library to analyze the user's instructions. During this analysis process, meanings and keywords are extracted from the user's instructions. For example, keywords such as "wizard," "treasure hunt," and "space" are extracted.

[0629] Step 5:

[0630] The server issues instructions to the generative model based on the analyzed data, and the generative model prepares to generate 3D spaces and objects according to the user's instructions.

[0631] Step 6:

[0632] A generative model uses machine learning algorithms to generate 3D spaces and objects based on the analyzed data, such as a "wizard" character or an environment containing a treasure chest for a "treasure hunt."

[0633] Step 7:

[0634] The generated 3D space and objects are sent to the terminal by the server, where the generated data is formatted so that it can be displayed correctly on the terminal.

[0635] Step 8:

[0636] The device receives the generated data sent from the server, which is then passed to the 3D rendering engine.

[0637] Step 9:

[0638] The device uses a 3D rendering engine to visually display the generated three-dimensional space and objects, allowing users to see the creative results generated in real time based on their instructions.

[0639] Step 10:

[0640] The user can visually check the generated 3D space and objects and input additional instructions (corrections, updates, etc.) as needed, enabling further interactive operation.

[0641] Example 1

[0642] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0643] Conventional systems for generating 3D spaces and 3D objects require advanced technology and specialized knowledge, making them difficult for general users to operate intuitively. Furthermore, there are few systems that allow users to quickly generate 3D spaces by directly instructing them in natural language, and there has been a demand for an improved user experience.

[0644] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0645] In this invention, the server includes means for a user to input natural language, means for analyzing the natural language input by the user, and generative model means for generating a three-dimensional space or a three-dimensional object based on the analyzed natural language. This enables a user to intuitively and efficiently generate a three-dimensional space or an object simply by issuing instructions using natural language, without requiring specialized knowledge.

[0646] "User" refers to a person or organization that uses the system to generate three-dimensional spaces or three-dimensional objects.

[0647] "Natural language" refers to languages ​​that humans use in their daily lives, such as Japanese and English, and is different from programming languages.

[0648] An "input means" is a device or software that provides an interface for a user to input natural language, such as a text box or a voice recognition system.

[0649] "Means for analyzing" refers to a device or software that provides the technology to understand the natural language entered by the user through syntactic and contextual analysis and to enable the system to identify the requested content.

[0650] "Generative model means" refers to an algorithm or program for automatically generating three-dimensional spaces or three-dimensional objects based on parsed natural language instructions.

[0651] "Displaying means" refers to a device or software that visually presents the generated three-dimensional space or three-dimensional object to a user. For example, a display or a VR headset.

[0652] "Transmitting means" refers to a device or software that provides a technique for transmitting data of a three-dimensional space or three-dimensional object generated by a server to a terminal.

[0653] "Rendering means" refers to a device or software that generates high-quality images in real time based on the three-dimensional space or three-dimensional object data received by the terminal and visually displays them to the user.

[0654] A "machine learning algorithm" is a computational method or model that allows a system to learn from data and improve the accuracy of analysis and generation.

[0655] This invention relates to a system that allows users to intuitively generate three-dimensional spaces and three-dimensional objects using natural language. This system analyzes the natural language input by the user, and a generative AI model generates three-dimensional spaces and three-dimensional objects based on that, and presents them visually to the user.

[0656] Configuration and Operation

[0657] User Input

[0658] The user inputs instructions in natural language through the device interface. For example, they can input a prompt such as, "I want to create a space with a wizard and a treasure hunt." This prompt can be entered in a text box or recorded by voice input.

[0659] Sending data

[0660] The device receives the user's natural language input and immediately sends it to the server, using communication protocols such as web sockets and HTTP requests.

[0661] Natural Language Analysis

[0662] The server uses natural language processing (NLP) libraries, such as Python's NLTK or spaCy, to parse the received natural language instructions. It then extracts keywords and context to understand the details of the 3D space or object the user is requesting. For example, elements such as "wizard," "treasure hunt," and "space" are analyzed.

[0663] Processing the generative model

[0664] The analyzed data is passed to a generative AI model. The server uses machine learning algorithms such as GPT-3 and DALL-E to generate 3D spaces and objects. During this generation process, multiple objects and environments are automatically placed based on the user's instructions to create a realistic 3D space. For example, based on keywords such as "wizard" and "treasure hunt," the generative model creates an exploration space that includes a wizard character and a treasure chest.

[0665] Receiving and displaying data

[0666] The server sends the generated 3D space and object data to the device, which receives the data and uses Unity's 3D rendering engine to visually display the 3D space and objects. This allows users to see the creative results generated based on their instructions in real time.

[0667] Specific examples

[0668] As a concrete example, consider the case where a user inputs, "I want to create a scene with a small waterfall in the forest." The server receives this instruction, extracts keywords such as "forest," "small waterfall," and "scenery," and passes them to the generative model. Based on these keywords, the generative model generates a scene with forest trees, plants, and a small waterfall. The generated three-dimensional space is sent to the device and visualized directly on the user's screen. This allows the user to immediately visually confirm their imagination.

[0669] This invention makes it possible for anyone to easily generate and display three-dimensional spaces and objects without the need for conventional advanced technology or specialized knowledge, significantly lowering the barrier to creative expression in the metaverse.

[0670] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0671] Step 1:

[0672] The user inputs instructions in natural language through the terminal interface. A possible prompt might be, "I want to create a space where wizards can go on treasure hunts." This input natural language is acquired from the terminal's text box or voice input system. The input in this step is the user's natural language, and the output is raw text data. The terminal acquires this text data and prepares it to be passed to the next processing step.

[0673] Step 2:

[0674] The device receives the natural language instructions entered by the user and immediately sends them to the server. Data is communicated using WebSockets or HTTP requests. The input in this step is the text data obtained in step 1, and the output is that text data being sent to the server. The device correctly formats the user's input data and sends it to the server.

[0675] Step 3:

[0676] The server analyzes the received natural language instructions. This analysis uses a natural language processing (NLP) library such as Python's NLTK or spaCy. The input is the text data of the user's natural language instructions sent in step 2. The server analyzes this text data and extracts keywords and context. For example, elements such as "wizard," "treasure hunt," and "space" are extracted. The output is the analyzed keywords and context information.

[0677] Step 4:

[0678] The server prepares data for a generative AI model based on the analyzed keywords and contextual information. Specifically, it uses a generative model such as GPT-3 or DALL-E to generate 3D spaces and objects. The input in this step is the keywords and contextual information extracted in step 3, and the output is data on the 3D spaces and objects generated by the generative AI model. The server converts the data into a format suitable for the generative model and inputs it into the model.

[0679] Step 5:

[0680] The generative AI model generates the specified 3D space and objects based on the data. In this generation process, multiple objects and environments are automatically placed according to the user's instructions. For example, based on the keywords "wizard" and "treasure hunt," an exploration space including a wizard character and a treasure chest is constructed. The input in this step is the data given to the generative model in step 4, and the output is the generated 3D space and object data.

[0681] Step 6:

[0682] The server sends the generated 3D space and object data to the device. Data communication is performed using Web sockets and HTTP requests. The input in this step is the data generated in step 5, and the output is that data being sent to the device. The server sends the generated data to the device in the appropriate format and confirms that communication is complete.

[0683] Step 7:

[0684] The device visually displays the 3D space and objects generated using Unity, a 3D rendering engine, based on the received data. The input is the generated data sent in step 6, and the output is the 3D space and objects displayed on the user's screen. The device renders the received data and executes the operations to display it to the user in real time.

[0685] (Application example 1)

[0686] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0687] Conventional virtual store and three-dimensional space customization methods require advanced technology and specialized knowledge, making them difficult for general users to operate. Furthermore, they lack an intuitive interface for quickly changing the layout and displays within the store. As a result, virtual store operations and marketing activities are restricted. This invention aims to solve these problems by providing a means for users without special skills to easily customize the layout and displays of a virtual store using natural language.

[0688] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0689] In this invention, the server includes means for a user to input natural language, means for analyzing the natural language input by the user, generative model means for generating a three-dimensional space or a three-dimensional object based on the analyzed natural language, means for displaying the generated three-dimensional space or the three-dimensional object to the user, and means for customizing the layout or display of a virtual store based on the analyzed natural language, thereby enabling the user to intuitively customize the layout or display of a virtual store using natural language.

[0690] A "means for a user to input natural language" is something that provides an interface for a user to input instructions to a system using human language.

[0691] The "means for analyzing the natural language input by the user" refers to a process or technology for understanding the natural language input by the user and deciphering its meaning and intent.

[0692] "Generative modeling means for generating 3D spaces or 3D objects based on parsed natural language" refers to algorithms or techniques that create or construct 3D environments or objects based on the results of parsing natural language.

[0693] "Means for displaying the generated three-dimensional space or three-dimensional objects to the user" refers to display or rendering technologies that provide a visual representation of the generated three-dimensional environment or objects to the user.

[0694] "Means for customizing the layout or display of a virtual store based on analyzed natural language" refers to techniques and methods for changing and optimizing the layout and display content within a virtual store based on instructions in the user's natural language.

[0695] This invention relates to a system that allows users to intuitively create and customize three-dimensional spaces and objects using natural language, allowing them to easily change the layout and displays of virtual stores.

[0696] 1. System Configuration

[0697] Hardware and Software

[0698] Hardware used:

[0699] Smartphone

[0700] Head-mounted display (HMD)

[0701] server

[0702] Software used:

[0703] OpenAI API

[0704] Three.js

[0705] 2. System Operation

[0706] 1. User natural language input

[0707] Users use a smartphone or HMD to input instructions in natural language into the system's input interface, such as prompts like "I want to create a new spring display" or "I want to place a flower arch at the entrance."

[0708] 2. Natural Language Analysis

[0709] The server uses OpenAI's Natural Language Processing (NLP) library to parse natural language input from the user, which is used to understand the user's request and obtain the parsed results.

[0710] 3. Generation of 3D space

[0711] The server uses Three.js to generate three-dimensional space and objects based on the analyzed data. The generated objects and layout are then applied to the virtual store.

[0712] 4. Displaying the results

[0713] The generated 3D space and objects are displayed in real time on the user's smartphone or HMD, allowing the user to instantly check the layout and displays of the virtual store based on the instructions they input.

[0714] 3. Specific Examples

[0715] Here are some examples of specific prompts:

[0716] "I want to create a new spring display."

[0717] "I want to put a flower arch at the entrance."

[0718] "Please change the interior of the store to a nautical theme."

[0719] This system allows store owners and marketers to freely change the layout and displays of virtual stores using natural language, without requiring any special skills or expertise, enabling users to easily and efficiently build interactive and creative virtual stores.

[0720] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0721] Step 1:

[0722] The user uses a smartphone or head-mounted display to input instructions in natural language into the system's input interface. These instructions are specific prompt sentences. For example, a user might input an instruction such as "I want to create a new spring display" through the "means for user natural language input."

[0723] Step 2:

[0724] The terminal sends the input natural language to the server. This is done by the "means for analyzing the natural language input by the user." The input data is sent to the server in the form of text, which is the user's instructions.

[0725] Step 3:

[0726] The server uses OpenAI's natural language processing (NLP) library to parse the received natural language instructions. The input is the text of the user's instructions, and the output is structured data of the parsed instructions. This structured data includes keywords for the specified objects and layout. The meaning of the instructions is understood by "a generative modeling means for generating 3D spaces or 3D objects based on the parsed natural language."

[0727] Step 4:

[0728] The server uses Three.js to generate three-dimensional spaces and objects based on the parsed data. The input is the parsed data, and the output is model data for the three-dimensional objects and spaces. This generation process determines how the specified layout and objects will be specifically arranged. The three-dimensional space and objects are generated from the model through "means of displaying the generated three-dimensional space or three-dimensional objects to the user."

[0729] Step 5:

[0730] The server sends the generated model data of the 3D space and objects to the terminal. The input is the 3D model data, and the output is the data sent to the user terminal. Once the terminal receives this data, it proceeds to the next step.

[0731] Step 6:

[0732] The device uses Three.js to render 3D spaces and objects based on the received model data and displays them to the user. The input is model data, and the output is 3D spaces and objects that are visually displayed to the user. This allows users to see the creative results generated based on their instructions in real time.

[0733] Step 7:

[0734] The user checks the layout and display of the virtual store and, if necessary, inputs further correction instructions in natural language. By repeating this cycle, the user can easily customize the design of the virtual store. The input is new correction instructions, and the output is an updated 3D space or object.

[0735] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0736] This invention relates to a system that allows users to intuitively generate three-dimensional spaces and three-dimensional objects using natural language, and further provides more personalized generated results by combining it with an emotion engine that recognizes the user's emotions. This system analyzes the natural language input by the user, and a generative model generates three-dimensional spaces and three-dimensional objects based on that, and visually presents them to the user.

[0737] The user inputs instructions in natural language through the system's interface. For example, say, "I want to create a space with a wizard and where people can go treasure hunting." This input is received by the device, and at the same time, an emotion engine that recognizes the user's emotions is activated.

[0738] The device receives the user's instructions and sends them along with emotional data to the server. The server then analyzes the received natural language instructions and emotional data using a natural language processing (NLP) library and an emotion analysis algorithm, respectively. The analysis step extracts keywords and context to understand the user's desired 3D space and 3D object, as well as the user's emotional state.

[0739] The analyzed natural language data and emotion data are passed to a generative modeling means. The generative model uses a machine learning algorithm to generate a specified three-dimensional space and objects based on the analyzed data. For example, if instructions containing "wizard" and "treasure hunt" are detected and the user's emotional state is excited, the generative model will generate an environment that emphasizes a sense of adventure.

[0740] The generated 3D space and objects are sent to the device by the server. The device uses a 3D rendering engine based on the received data to visually display the generated 3D space and objects to the user. This allows the user to see in real time the creative results generated based on their own instructions and emotions.

[0741] As a concrete example, consider the case where a user inputs "I want to create a scene with a small waterfall in the forest" and indicates an emotional state of "relaxed." The server receives this instruction, analyzes keywords such as "forest," "small waterfall," and "scenery," and the emotional state of "relaxed," and passes them to the generative model. Based on this data, the generative model generates a relaxing environment with a forest, the sound of a waterfall, and a lush green scene with sunlight streaming in. The generated three-dimensional space is sent to the device and visualized directly on the user's screen. This allows the user to immediately visually confirm the scene generated based on their imagination and emotions.

[0742] This invention enables anyone to easily generate and display three-dimensional spaces and objects without requiring advanced skills or specialized knowledge, and also enables personalized creative expression that reflects the user's emotional state.

[0743] The processing flow will be explained below.

[0744] Step 1:

[0745] The user inputs instructions in natural language through the terminal interface, for example, "I want to create a space where there is a wizard and where you can go on a treasure hunt."

[0746] Step 2:

[0747] As soon as the device receives the user's instructions, it uses an emotion engine to collect emotional data from the user's voice, facial expressions, etc., and sends the results to the server.

[0748] Step 3:

[0749] The server receives the natural language instructions and emotion data sent from the terminal, which includes the natural language data and emotion data such as "excited" or "relaxed."

[0750] Step 4:

[0751] The server analyzes the user's instructions using a natural language processing (NLP) library. During this analysis process, the keywords "wizard," "treasure hunt," and "space" are extracted.

[0752] Step 5:

[0753] The server uses an emotion engine to analyze the emotion data and understand the user's emotional state, for example, determining whether they are "excited" or "relaxed."

[0754] Step 6:

[0755] The server passes the analyzed natural language data and emotion data to the generative model, which then generates corresponding 3D spaces and objects based on this data.

[0756] Step 7:

[0757] The generative model uses machine learning algorithms to generate 3D spaces and objects based on the analyzed data. For example, if instructions including "wizard" and "treasure hunt" and an "excited" emotional state are detected, an environment filled with adventure elements will be generated. If the emotional state is "relaxed," an environment emphasizing tranquil scenery will be generated.

[0758] Step 8:

[0759] The generated three-dimensional space and objects are sent to the device by the server, with the generated data adjusted according to the emotion.

[0760] Step 9:

[0761] The device receives the generated data sent from the server, which is then passed to the 3D rendering engine and prepared for display.

[0762] Step 10:

[0763] The device uses a 3D rendering engine to visually display the generated three-dimensional space and objects, allowing users to see the creative results generated in real time based on their instructions and emotions.

[0764] Example 2

[0765] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0766] Conventional systems for generating 3D spaces and 3D objects have limited ways for users to intuitively input instructions, requiring specialized knowledge and skills. Furthermore, it is difficult to provide personalized generated results that take user emotions into account. Furthermore, real-time visualization is difficult, limiting the user experience.

[0767] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0768] In this invention, the server includes means for a user to input natural language, means for analyzing the natural language and emotion input by the user, and generative model means for generating a three-dimensional space or a three-dimensional object based on the analyzed natural language and emotion data. This enables a user to generate a personalized three-dimensional space or object based on natural language and emotion without requiring specialized knowledge.

[0769] "Natural language" refers to a language used by humans as a means of input to a computer by a user.

[0770] "User" refers to a person who uses this system and inputs instructions in natural language.

[0771] "Emotion" refers to data that indicates the user's psychological state and mood, and the system analyzes this data to reflect it in the generated results.

[0772] "Three-dimensional space" refers to spatial data expressed in three-dimensional coordinates, and refers to the visual environment generated by the generative model means.

[0773] A "three-dimensional object" is individual object data that is placed in three-dimensional space and is generated based on user input.

[0774] "Means for analyzing" refers to a method or device for processing natural language and emotion data entered by a user and converting it into understandable information.

[0775] A "generative model means" is a method or device for generating three-dimensional spaces or three-dimensional objects based on data analyzed using a machine learning algorithm.

[0776] The "display means" refers to a method or device for visually presenting the generated three-dimensional space or three-dimensional object to the user.

[0777] This invention relates to a system that allows users to intuitively generate three-dimensional spaces and three-dimensional objects using natural language. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it provides more personalized generated results.

[0778] This system uses the following hardware and software:

[0779] Hardware:

[0780] Terminal: A device that allows users to input and output information, such as a computer, tablet, or smartphone.

[0781] Server: A device with high-performance computing resources that analyzes data and runs generative models.

[0782] Sensors: Cameras, microphones, and other devices used to analyze user emotions.

[0783] software:

[0784] Natural Language Processing (NLP) libraries: For example, spaCy and BERT are used to analyze natural language input from users.

[0785] Sentiment analysis algorithm: For example, we use Google Cloud Natural Language API to analyze user sentiment.

[0786] Generative AI model: Generates 3D spaces and objects based on analysis results using Generative Adversarial Networks (GANs) and other methods.

[0787] 3D rendering engine: Visualize three-dimensional spaces and objects generated using Unity or Unreal Engine.

[0788] Regarding specific program processing

[0789] The user inputs instructions in natural language through the system's interface. For example, say, "I want to create a space with a wizard and where you can go treasure hunting." This input is received by the device, and at the same time, an emotion engine that recognizes the user's emotions is activated. The device then sends this to the server.

[0790] Server Processing

[0791] The server analyzes the received natural language instructions and emotion data, using natural language processing (NLP) libraries to extract keywords and context from the instructions, and emotion analysis algorithms to analyze the user's emotional state (e.g., "excited").

[0792] Running the generative model

[0793] The analyzed natural language data and emotion data are passed to a generative AI model, which then generates the specified 3D space and objects based on this data. For example, if the keywords "wizard" and "treasure hunt" and the emotion of excitement are detected, the generative model will generate an adventure-like environment.

[0794] Visualization

[0795] The generated 3D space and objects are sent by the server to the device, which uses this data to visually display it to the user using a 3D rendering engine, allowing the user to see the creative results in real time.

[0796] Specific examples

[0797] Consider a case where a user inputs "I want to create a scene with a small waterfall in the forest" and indicates an emotional state of "relaxed." Based on this instruction and emotional data, the server performs analysis and passes the data to the generative model. The generative model generates a relaxing environment, with the sounds of a forest and waterfall and a lush green scene with sunlight streaming in. The generated three-dimensional space is sent to the device and visualized on the user's screen. This allows the user to easily check the scene generated based on their imagination and emotions.

[0798] This invention enables users to easily generate and display three-dimensional spaces and objects without requiring advanced technology or specialized knowledge, and also enables personalized creative expression that reflects the user's emotional state.

[0799] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0800] Step 1:

[0801] User natural language input

[0802] Input: The user enters instructions in natural language through the system's interface.

[0803] Processing: The user enters the prompt sentence "I want to create a space where there is a wizard and where you can go on a treasure hunt" into the input field. This data is entered into the terminal.

[0804] Output: Natural language instructions are generated and stored on the device for processing in the next step.

[0805] Specific operation: The user enters natural language using a keyboard or voice input, and presses the send button to send the data to the device.

[0806] Step 2:

[0807] Terminal reception and emotion recognition

[0808] Input: User-entered natural language instructions

[0809] Processing: The device receives the user's input data, and at the same time, the emotion engine analyzes the user's facial expressions and voice using the camera and microphone. The emotion analysis results are packaged together with the natural language input data as a packet.

[0810] Output: Packets of natural language instructions and sentiment data are generated.

[0811] Specific operation: The device collects video and audio data through the camera or microphone, analyzes it in real time, and formats the emotion analysis results and natural language instruction data into packets.

[0812] Step 3:

[0813] Parsing input data by the server

[0814] Input: Packets of natural language instructions and sentiment data

[0815] Processing: The server disassembles the received packet and parses the instructions using a natural language processing (NLP) library. It also parses the sentiment data using a sentiment analysis algorithm (e.g., Google Cloud Natural Language API).

[0816] Output: Generates analyzed keywords, context, and sentiment data.

[0817] How it works: The server collects access logs and uses NLP libraries (e.g., spaCy, BERT) to extract keywords such as "wizard" and "treasure hunt." It also uses a sentiment analysis algorithm to identify emotions such as "excited."

[0818] Step 4:

[0819] 3D space and object generation using generative models

[0820] Input: Analyzed keywords, context, and sentiment data

[0821] Processing: The server inputs the analysis results into a generative AI model (e.g., GANs) to generate 3D spaces and objects.

[0822] Output: Generated 3D spatial data and object data

[0823] How it works: The generative model uses machine learning algorithms to generate an adventurous environment based on the input keywords "wizard" and "treasure hunt" and the emotional state "excitement." The generated data is saved as a 3D object file (e.g., FBX or OBJ format).

[0824] Step 5:

[0825] Generated data sent from the server to the device

[0826] Input: Generated 3D spatial data and object data

[0827] Processing: The server compresses the generated data and transfers it to the terminal.

[0828] Output: Compressed 3D spatial data and object data is sent to the device.

[0829] Specific operation: The server compresses the generated data in ZIP format or similar and sends it to the terminal via HTTP or WebSocket.

[0830] Step 6:

[0831] Terminal visualization

[0832] Input: Compressed data sent from the server

[0833] Processing: The device decompresses the compressed data received and visualizes it using a 3D rendering engine (e.g. Unity, Unreal Engine).

[0834] Output: The generated 3D space and objects are displayed on the user's display.

[0835] What it does: The device decodes the compressed data it receives and inputs it into the 3D rendering engine, which then renders it in real time, displaying an adventurous 3D world on the user's screen.

[0836] This allows users to intuitively generate and display three-dimensional spaces and objects based on natural language and emotions.

[0837] (Application example 2)

[0838] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0839] This invention relates to a system that allows users to intuitively generate three-dimensional spaces and three-dimensional objects and visually confirm them. However, existing systems have a problem in that it is difficult to provide personalized generated results that reflect the user's emotional state. Furthermore, interactive design that takes user emotions into consideration is particularly required in the design of virtual stores, but there is a lack of technology that can achieve this.

[0840] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to input natural language, means for analyzing the natural language input by the user, generative model means for generating a three-dimensional space or a three-dimensional object based on the analyzed natural language and the user's emotion data, and means for displaying the generated three-dimensional space or three-dimensional object to the user. This allows users to easily generate and visualize personalized three-dimensional spaces and three-dimensional objects that reflect their own emotions. Furthermore, an interactive environment that takes user emotions into consideration can be provided in the design of virtual stores.

[0841] A "user" is a person who uses the system to input natural language and instructs the generation of three-dimensional spaces and three-dimensional objects.

[0842] "Natural language" refers to the human language used by people on a daily basis to input instructions and information into a system.

[0843] "Means of analysis" refers to algorithms or programs that analyze input natural language and emotional data and extract the necessary information.

[0844] "Emotion data" is data that represents the user's current emotional state, and adjusts the system's behavior based on this data.

[0845] A "generative model means" is a machine learning algorithm or AI model for generating three-dimensional space or three-dimensional objects based on analyzed natural language or emotion data.

[0846] A "three-dimensional space" is a stereoscopic environment or location that is generated based on user instructions.

[0847] A "three-dimensional object" is a solid object or character placed in a three-dimensional space.

[0848] The "display means" refers to a display device or rendering engine for visually presenting the generated three-dimensional space or three-dimensional object to the user.

[0849] A "virtual store" is a virtual store space that users can visit online.

[0850] The present invention relates to a system that allows users to intuitively create three-dimensional spaces and objects using natural language. The system provides personalized results based on the user's input and emotional data. The present invention is particularly useful for designing virtual stores.

[0851] System configuration

[0852] The system consists of the following main components:

[0853] User input:

[0854] A device (e.g., smartphone, tablet, PC) that allows a user to input natural language.

[0855] Natural language analysis means:

[0856] Software for analyzing natural language input (e.g., spaCy, BERT).

[0857] Emotion analysis means:

[0858] Software for analyzing user emotion data (e.g., TensorFlow, OpenAI GPT-4).

[0859] Generative model means:

[0860] Generative models for generating 3D spaces and objects based on natural language and emotion data (e.g., Unity3D, Blender).

[0861] Display means:

[0862] Software for visually displaying generated 3D spaces and objects (e.g., WebGL, Three.js).

[0863] Program processing

[0864] The server analyzes the data received through the user's natural language input. It uses natural language processing libraries such as spaCy and BERT as its natural language analysis method. The analyzed keywords and context are passed to a sentiment analysis method, which uses TensorFlow and OpenAI GPT-4 to determine the user's emotional state.

[0865] The analyzed natural language and emotion data are passed to a generative modeling means, which uses a generative AI model such as Unity3D or Blender to generate 3D spaces and objects according to the user's instructions. For example, if a user inputs "I want to create a relaxing cafe" and the emotion data indicates a relaxed state, the generative model will automatically design a cafe with lush plants, wooden furniture, and calming music.

[0866] The generated 3D spaces and objects are displayed in real time on the user's device using WebGL and Three.js, allowing users to instantly see personalized creative results based on their instructions and emotions.

[0867] Specific examples

[0868] For example, if a user types, "I want a virtual cafe with lots of greenery, nature, and relaxation," and indicates the emotion "relaxed," the app will automatically generate and visually display a virtual cafe with lush plants, wooden furniture, and calming music.

[0869] Example prompt sentence:

[0870] "Please generate a virtual cafe. The user requested a relaxing space with lots of greenery."

[0871] "The spacious layout creates an airy space."

[0872] As described above, the present invention intuitively generates and displays advanced three-dimensional spaces and objects based on the user's natural language input and emotional data, providing excellent personalization functions, particularly in the design of virtual stores.

[0873] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0874] Step 1:

[0875] Users input their virtual store design preferences in natural language using a device (e.g., smartphone, tablet, or PC). This input includes specific requests, images, and emotions. The input data is captured in text format on the device.

[0876] Step 2:

[0877] The device sends the received natural language input to the server. The server receives this input data and analyzes the text using natural language analysis tools (e.g., spaCy, BERT). As a result of the analysis, keywords and contextual information are extracted. The input is natural language text, and the output is analyzed keywords and contextual information.

[0878] Step 3:

[0879] The server uses emotion analysis tools (e.g., TensorFlow, OpenAI GPT-4) to acquire and analyze the user's emotion data when inputting natural language. The input is natural language text and user information, and the output is emotion data that indicates the user's emotional state.

[0880] Step 4:

[0881] The analyzed natural language data and emotion data are input into a generative model. The server uses a generative modeling tool (e.g., Unity3D, Blender) to generate a 3D space or a 3D object based on this data. The input is keywords, contextual information, and emotion data, and the output is design data for the 3D space or object.

[0882] Step 5:

[0883] The server sends the generated design data to the terminal. The terminal uses a display method (e.g., WebGL, Three.js) to visually display the generated three-dimensional space and objects to the user in real time. The input is the design data, and the output is the visually displayed three-dimensional space and objects.

[0884] Step 6:

[0885] The user can check the 3D space and objects generated in real time and make corrections as necessary. By repeating the process from step 1, the optimal virtual store that meets the user's requirements is completed.

[0886] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0887] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0888] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0889] [Fourth embodiment]

[0890] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0891] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0892] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0893] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0894] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0895] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0896] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0897] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0898] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0899] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0900] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0901] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0902] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0903] The present invention relates to a system that enables users to intuitively generate three-dimensional spaces and three-dimensional objects using natural language. This system analyzes the natural language input by the user, and a generative model generates three-dimensional spaces and three-dimensional objects based on that, and presents them visually to the user.

[0904] The user inputs instructions in natural language through the system interface. For example, they might input, "I want to create a space with a wizard and where people can go on a treasure hunt." This input is received by the terminal and immediately sent to the server.

[0905] The server uses a natural language processing (NLP) library to parse the received natural language instructions. This parsing step extracts keywords and context to understand the details of the 3D space or 3D object requested by the user. The parsed data is passed to a generative modeling facility.

[0906] A generative model generates specified 3D spaces and objects based on analyzed data using machine learning algorithms. This generation process automatically arranges multiple objects and environments according to user instructions to create a realistic 3D space. For example, if the instructions include "wizard" and "treasure hunt," the generative model generates an explorable environment containing a wizard character and a treasure chest.

[0907] The generated 3D space and objects are sent to the device by the server. The device uses a 3D rendering engine based on the received data to visually display the generated 3D space and objects to the user. This allows the user to see in real time the creative results generated based on their instructions.

[0908] As a concrete example, consider the case where a user inputs, "I want to create a scene with a small waterfall in the forest." The server receives this instruction, extracts keywords such as "forest," "small waterfall," and "scenery," and passes them to the generative model. Based on these keywords, the generative model generates a scene with forest trees, plants, and a small waterfall. The generated three-dimensional space is sent to the device and visualized directly on the user's screen. This allows the user to immediately visually confirm their imagination.

[0909] This invention makes it possible for anyone to easily generate and display three-dimensional spaces and objects without the need for conventional advanced technology or specialized knowledge, significantly lowering the barrier to creative expression in the metaverse.

[0910] The processing flow will be explained below.

[0911] Step 1:

[0912] The user inputs instructions in natural language through the terminal interface, for example, "I want to create a space where there is a wizard and where you can go on a treasure hunt."

[0913] Step 2:

[0914] The device receives the user's instructions and sends them to the server. Specifically, the instructions are sent to the server using an API.

[0915] Step 3:

[0916] The server receives the instruction sent from the terminal, and the received natural language instruction is stored for natural language analysis in the next step.

[0917] Step 4:

[0918] The server uses a natural language processing (NLP) library to analyze the user's instructions. During this analysis process, meanings and keywords are extracted from the user's instructions. For example, keywords such as "wizard," "treasure hunt," and "space" are extracted.

[0919] Step 5:

[0920] The server issues instructions to the generative model based on the analyzed data, and the generative model prepares to generate 3D spaces and objects according to the user's instructions.

[0921] Step 6:

[0922] A generative model uses machine learning algorithms to generate 3D spaces and objects based on the analyzed data, such as a "wizard" character or an environment containing a treasure chest for a "treasure hunt."

[0923] Step 7:

[0924] The generated 3D space and objects are sent to the terminal by the server, where the generated data is formatted so that it can be displayed correctly on the terminal.

[0925] Step 8:

[0926] The device receives the generated data sent from the server, which is then passed to the 3D rendering engine.

[0927] Step 9:

[0928] The device uses a 3D rendering engine to visually display the generated three-dimensional space and objects, allowing users to see the creative results generated in real time based on their instructions.

[0929] Step 10:

[0930] The user can visually check the generated 3D space and objects and input additional instructions (corrections, updates, etc.) as needed, enabling further interactive operation.

[0931] Example 1

[0932] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0933] Conventional systems for generating 3D spaces and 3D objects require advanced technology and specialized knowledge, making them difficult for general users to operate intuitively. Furthermore, there are few systems that allow users to quickly generate 3D spaces by directly instructing them in natural language, and there has been a demand for an improved user experience.

[0934] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0935] In this invention, the server includes means for a user to input natural language, means for analyzing the natural language input by the user, and generative model means for generating a three-dimensional space or a three-dimensional object based on the analyzed natural language. This enables a user to intuitively and efficiently generate a three-dimensional space or an object simply by issuing instructions using natural language, without requiring specialized knowledge.

[0936] "User" refers to a person or organization that uses the system to generate three-dimensional spaces or three-dimensional objects.

[0937] "Natural language" refers to languages ​​that humans use in their daily lives, such as Japanese and English, and is different from programming languages.

[0938] An "input means" is a device or software that provides an interface for a user to input natural language, such as a text box or a voice recognition system.

[0939] "Means for analyzing" refers to a device or software that provides the technology to understand the natural language entered by the user through syntactic and contextual analysis and to enable the system to identify the requested content.

[0940] "Generative model means" refers to an algorithm or program for automatically generating three-dimensional spaces or three-dimensional objects based on parsed natural language instructions.

[0941] "Displaying means" refers to a device or software that visually presents the generated three-dimensional space or three-dimensional object to a user. For example, a display or a VR headset.

[0942] "Transmitting means" refers to a device or software that provides a technique for transmitting data of a three-dimensional space or three-dimensional object generated by a server to a terminal.

[0943] "Rendering means" refers to a device or software that generates high-quality images in real time based on the three-dimensional space or three-dimensional object data received by the terminal and visually displays them to the user.

[0944] A "machine learning algorithm" is a computational method or model that allows a system to learn from data and improve the accuracy of analysis and generation.

[0945] This invention relates to a system that allows users to intuitively generate three-dimensional spaces and three-dimensional objects using natural language. This system analyzes the natural language input by the user, and a generative AI model generates three-dimensional spaces and three-dimensional objects based on that, and presents them visually to the user.

[0946] Configuration and Operation

[0947] User Input

[0948] The user inputs instructions in natural language through the device interface. For example, they can input a prompt such as, "I want to create a space with a wizard and a treasure hunt." This prompt can be entered in a text box or recorded by voice input.

[0949] Sending data

[0950] The device receives the user's natural language input and immediately sends it to the server, using communication protocols such as web sockets and HTTP requests.

[0951] Natural Language Analysis

[0952] The server uses natural language processing (NLP) libraries, such as Python's NLTK or spaCy, to parse the received natural language instructions. It then extracts keywords and context to understand the details of the 3D space or object the user is requesting. For example, elements such as "wizard," "treasure hunt," and "space" are analyzed.

[0953] Processing the generative model

[0954] The analyzed data is passed to a generative AI model. The server uses machine learning algorithms such as GPT-3 and DALL-E to generate 3D spaces and objects. During this generation process, multiple objects and environments are automatically placed based on the user's instructions to create a realistic 3D space. For example, based on keywords such as "wizard" and "treasure hunt," the generative model creates an exploration space that includes a wizard character and a treasure chest.

[0955] Receiving and displaying data

[0956] The server sends the generated 3D space and object data to the device, which receives the data and uses Unity's 3D rendering engine to visually display the 3D space and objects. This allows users to see the creative results generated based on their instructions in real time.

[0957] Specific examples

[0958] As a concrete example, consider the case where a user inputs, "I want to create a scene with a small waterfall in the forest." The server receives this instruction, extracts keywords such as "forest," "small waterfall," and "scenery," and passes them to the generative model. Based on these keywords, the generative model generates a scene with forest trees, plants, and a small waterfall. The generated three-dimensional space is sent to the device and visualized directly on the user's screen. This allows the user to immediately visually confirm their imagination.

[0959] This invention makes it possible for anyone to easily generate and display three-dimensional spaces and objects without the need for conventional advanced technology or specialized knowledge, significantly lowering the barrier to creative expression in the metaverse.

[0960] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0961] Step 1:

[0962] The user inputs instructions in natural language through the terminal interface. A possible prompt might be, "I want to create a space where wizards can go on treasure hunts." This input natural language is acquired from the terminal's text box or voice input system. The input in this step is the user's natural language, and the output is raw text data. The terminal acquires this text data and prepares it to be passed to the next processing step.

[0963] Step 2:

[0964] The device receives the natural language instructions entered by the user and immediately sends them to the server. Data is communicated using WebSockets or HTTP requests. The input in this step is the text data obtained in step 1, and the output is that text data being sent to the server. The device correctly formats the user's input data and sends it to the server.

[0965] Step 3:

[0966] The server analyzes the received natural language instructions. This analysis uses a natural language processing (NLP) library such as Python's NLTK or spaCy. The input is the text data of the user's natural language instructions sent in step 2. The server analyzes this text data and extracts keywords and context. For example, elements such as "wizard," "treasure hunt," and "space" are extracted. The output is the analyzed keywords and context information.

[0967] Step 4:

[0968] The server prepares data for a generative AI model based on the analyzed keywords and contextual information. Specifically, it uses a generative model such as GPT-3 or DALL-E to generate 3D spaces and objects. The input in this step is the keywords and contextual information extracted in step 3, and the output is data on the 3D spaces and objects generated by the generative AI model. The server converts the data into a format suitable for the generative model and inputs it into the model.

[0969] Step 5:

[0970] The generative AI model generates the specified 3D space and objects based on the data. In this generation process, multiple objects and environments are automatically placed according to the user's instructions. For example, based on the keywords "wizard" and "treasure hunt," an exploration space including a wizard character and a treasure chest is constructed. The input in this step is the data given to the generative model in step 4, and the output is the generated 3D space and object data.

[0971] Step 6:

[0972] The server sends the generated 3D space and object data to the device. Data communication is performed using Web sockets and HTTP requests. The input in this step is the data generated in step 5, and the output is that data being sent to the device. The server sends the generated data to the device in the appropriate format and confirms that communication is complete.

[0973] Step 7:

[0974] The device visually displays the 3D space and objects generated using Unity, a 3D rendering engine, based on the received data. The input is the generated data sent in step 6, and the output is the 3D space and objects displayed on the user's screen. The device renders the received data and executes the operations to display it to the user in real time.

[0975] (Application example 1)

[0976] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0977] Conventional virtual store and three-dimensional space customization methods require advanced technology and specialized knowledge, making them difficult for general users to operate. Furthermore, they lack an intuitive interface for quickly changing the layout and displays within the store. As a result, virtual store operations and marketing activities are restricted. This invention aims to solve these problems by providing a means for users without special skills to easily customize the layout and displays of a virtual store using natural language.

[0978] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0979] In this invention, the server includes means for a user to input natural language, means for analyzing the natural language input by the user, generative model means for generating a three-dimensional space or a three-dimensional object based on the analyzed natural language, means for displaying the generated three-dimensional space or the three-dimensional object to the user, and means for customizing the layout or display of a virtual store based on the analyzed natural language, thereby enabling the user to intuitively customize the layout or display of a virtual store using natural language.

[0980] A "means for a user to input natural language" is something that provides an interface for a user to input instructions to a system using human language.

[0981] The "means for analyzing the natural language input by the user" refers to a process or technology for understanding the natural language input by the user and deciphering its meaning and intent.

[0982] "Generative modeling means for generating 3D spaces or 3D objects based on parsed natural language" refers to algorithms or techniques that create or construct 3D environments or objects based on the results of parsing natural language.

[0983] "Means for displaying the generated three-dimensional space or three-dimensional objects to the user" refers to display or rendering technologies that provide a visual representation of the generated three-dimensional environment or objects to the user.

[0984] "Means for customizing the layout or display of a virtual store based on analyzed natural language" refers to techniques and methods for changing and optimizing the layout and display content within a virtual store based on instructions in the user's natural language.

[0985] This invention relates to a system that allows users to intuitively create and customize three-dimensional spaces and objects using natural language, allowing them to easily change the layout and displays of virtual stores.

[0986] 1. System Configuration

[0987] Hardware and Software

[0988] Hardware used:

[0989] Smartphone

[0990] Head-mounted display (HMD)

[0991] server

[0992] Software used:

[0993] OpenAI API

[0994] Three.js

[0995] 2. System Operation

[0996] 1. User natural language input

[0997] Users use a smartphone or HMD to input instructions in natural language into the system's input interface, such as prompts like "I want to create a new spring display" or "I want to place a flower arch at the entrance."

[0998] 2. Natural Language Analysis

[0999] The server uses OpenAI's Natural Language Processing (NLP) library to parse natural language input from the user, which is used to understand the user's request and obtain the parsed results.

[1000] 3. Generation of 3D space

[1001] The server uses Three.js to generate three-dimensional space and objects based on the analyzed data. The generated objects and layout are then applied to the virtual store.

[1002] 4. Displaying the results

[1003] The generated 3D space and objects are displayed in real time on the user's smartphone or HMD, allowing the user to instantly check the layout and displays of the virtual store based on the instructions they input.

[1004] 3. Specific Examples

[1005] Here are some examples of specific prompts:

[1006] "I want to create a new spring display."

[1007] "I want to put a flower arch at the entrance."

[1008] "Please change the interior of the store to a nautical theme."

[1009] This system allows store owners and marketers to freely change the layout and displays of virtual stores using natural language, without requiring any special skills or expertise, enabling users to easily and efficiently build interactive and creative virtual stores.

[1010] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1011] Step 1:

[1012] The user uses a smartphone or head-mounted display to input instructions in natural language into the system's input interface. These instructions are specific prompt sentences. For example, a user might input an instruction such as "I want to create a new spring display" through the "means for user natural language input."

[1013] Step 2:

[1014] The terminal sends the input natural language to the server. This is done by the "means for analyzing the natural language input by the user." The input data is sent to the server in the form of text, which is the user's instructions.

[1015] Step 3:

[1016] The server uses OpenAI's natural language processing (NLP) library to parse the received natural language instructions. The input is the text of the user's instructions, and the output is structured data of the parsed instructions. This structured data includes keywords for the specified objects and layout. The meaning of the instructions is understood by "a generative modeling means for generating 3D spaces or 3D objects based on the parsed natural language."

[1017] Step 4:

[1018] The server uses Three.js to generate three-dimensional spaces and objects based on the parsed data. The input is the parsed data, and the output is model data for the three-dimensional objects and spaces. This generation process determines how the specified layout and objects will be specifically arranged. The three-dimensional space and objects are generated from the model through "means of displaying the generated three-dimensional space or three-dimensional objects to the user."

[1019] Step 5:

[1020] The server sends the generated model data of the 3D space and objects to the terminal. The input is the 3D model data, and the output is the data sent to the user terminal. Once the terminal receives this data, it proceeds to the next step.

[1021] Step 6:

[1022] The device uses Three.js to render 3D spaces and objects based on the received model data and displays them to the user. The input is model data, and the output is 3D spaces and objects that are visually displayed to the user. This allows users to see the creative results generated based on their instructions in real time.

[1023] Step 7:

[1024] The user checks the layout and display of the virtual store and, if necessary, inputs further correction instructions in natural language. By repeating this cycle, the user can easily customize the design of the virtual store. The input is new correction instructions, and the output is an updated 3D space or object.

[1025] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1026] This invention relates to a system that allows users to intuitively generate three-dimensional spaces and three-dimensional objects using natural language, and further provides more personalized generated results by combining it with an emotion engine that recognizes the user's emotions. This system analyzes the natural language input by the user, and a generative model generates three-dimensional spaces and three-dimensional objects based on that, and visually presents them to the user.

[1027] The user inputs instructions in natural language through the system's interface. For example, say, "I want to create a space with a wizard and where people can go treasure hunting." This input is received by the device, and at the same time, an emotion engine that recognizes the user's emotions is activated.

[1028] The device receives the user's instructions and sends them along with emotional data to the server. The server then analyzes the received natural language instructions and emotional data using a natural language processing (NLP) library and an emotion analysis algorithm, respectively. The analysis step extracts keywords and context to understand the user's desired 3D space and 3D object, as well as the user's emotional state.

[1029] The analyzed natural language data and emotion data are passed to a generative modeling means. The generative model uses a machine learning algorithm to generate a specified three-dimensional space and objects based on the analyzed data. For example, if instructions containing "wizard" and "treasure hunt" are detected and the user's emotional state is excited, the generative model will generate an environment that emphasizes a sense of adventure.

[1030] The generated 3D space and objects are sent to the device by the server. The device uses a 3D rendering engine based on the received data to visually display the generated 3D space and objects to the user. This allows the user to see in real time the creative results generated based on their own instructions and emotions.

[1031] As a concrete example, consider the case where a user inputs "I want to create a scene with a small waterfall in the forest" and indicates an emotional state of "relaxed." The server receives this instruction, analyzes keywords such as "forest," "small waterfall," and "scenery," and the emotional state of "relaxed," and passes them to the generative model. Based on this data, the generative model generates a relaxing environment with a forest, the sound of a waterfall, and a lush green scene with sunlight streaming in. The generated three-dimensional space is sent to the device and visualized directly on the user's screen. This allows the user to immediately visually confirm the scene generated based on their imagination and emotions.

[1032] This invention enables anyone to easily generate and display three-dimensional spaces and objects without requiring advanced skills or specialized knowledge, and also enables personalized creative expression that reflects the user's emotional state.

[1033] The processing flow will be explained below.

[1034] Step 1:

[1035] The user inputs instructions in natural language through the terminal interface, for example, "I want to create a space where there is a wizard and where you can go on a treasure hunt."

[1036] Step 2:

[1037] As soon as the device receives the user's instructions, it uses an emotion engine to collect emotional data from the user's voice, facial expressions, etc., and sends the results to the server.

[1038] Step 3:

[1039] The server receives the natural language instructions and emotion data sent from the terminal, which includes the natural language data and emotion data such as "excited" or "relaxed."

[1040] Step 4:

[1041] The server analyzes the user's instructions using a natural language processing (NLP) library. During this analysis process, the keywords "wizard," "treasure hunt," and "space" are extracted.

[1042] Step 5:

[1043] The server uses an emotion engine to analyze the emotion data and understand the user's emotional state, for example, determining whether they are "excited" or "relaxed."

[1044] Step 6:

[1045] The server passes the analyzed natural language data and emotion data to the generative model, which then generates corresponding 3D spaces and objects based on this data.

[1046] Step 7:

[1047] The generative model uses machine learning algorithms to generate 3D spaces and objects based on the analyzed data. For example, if instructions including "wizard" and "treasure hunt" and an "excited" emotional state are detected, an environment filled with adventure elements will be generated. If the emotional state is "relaxed," an environment emphasizing tranquil scenery will be generated.

[1048] Step 8:

[1049] The generated three-dimensional space and objects are sent to the device by the server, with the generated data adjusted according to the emotion.

[1050] Step 9:

[1051] The device receives the generated data sent from the server, which is then passed to the 3D rendering engine and prepared for display.

[1052] Step 10:

[1053] The device uses a 3D rendering engine to visually display the generated three-dimensional space and objects, allowing users to see the creative results generated in real time based on their instructions and emotions.

[1054] Example 2

[1055] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1056] Conventional systems for generating 3D spaces and 3D objects have limited ways for users to intuitively input instructions, requiring specialized knowledge and skills. Furthermore, it is difficult to provide personalized generated results that take user emotions into account. Furthermore, real-time visualization is difficult, limiting the user experience.

[1057] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1058] In this invention, the server includes means for a user to input natural language, means for analyzing the natural language and emotion input by the user, and generative model means for generating a three-dimensional space or a three-dimensional object based on the analyzed natural language and emotion data. This enables a user to generate a personalized three-dimensional space or object based on natural language and emotion without requiring specialized knowledge.

[1059] "Natural language" refers to a language used by humans as a means of input to a computer by a user.

[1060] "User" refers to a person who uses this system and inputs instructions in natural language.

[1061] "Emotion" refers to data that indicates the user's psychological state and mood, and the system analyzes this data to reflect it in the generated results.

[1062] "Three-dimensional space" refers to spatial data expressed in three-dimensional coordinates, and refers to the visual environment generated by the generative model means.

[1063] A "three-dimensional object" is individual object data that is placed in three-dimensional space and is generated based on user input.

[1064] "Means for analyzing" refers to a method or device for processing natural language and emotion data entered by a user and converting it into understandable information.

[1065] A "generative model means" is a method or device for generating three-dimensional spaces or three-dimensional objects based on data analyzed using a machine learning algorithm.

[1066] The "display means" refers to a method or device for visually presenting the generated three-dimensional space or three-dimensional object to the user.

[1067] This invention relates to a system that allows users to intuitively generate three-dimensional spaces and three-dimensional objects using natural language. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it provides more personalized generated results.

[1068] This system uses the following hardware and software:

[1069] Hardware:

[1070] Terminal: A device that allows users to input and output information, such as a computer, tablet, or smartphone.

[1071] Server: A device with high-performance computing resources that analyzes data and runs generative models.

[1072] Sensors: Cameras, microphones, and other devices used to analyze user emotions.

[1073] software:

[1074] Natural Language Processing (NLP) libraries: For example, spaCy and BERT are used to analyze natural language input from users.

[1075] Sentiment analysis algorithm: For example, we use Google Cloud Natural Language API to analyze user sentiment.

[1076] Generative AI model: Generates 3D spaces and objects based on analysis results using Generative Adversarial Networks (GANs) and other methods.

[1077] 3D rendering engine: Visualize three-dimensional spaces and objects generated using Unity or Unreal Engine.

[1078] Regarding specific program processing

[1079] The user inputs instructions in natural language through the system's interface. For example, say, "I want to create a space with a wizard and where you can go treasure hunting." This input is received by the device, and at the same time, an emotion engine that recognizes the user's emotions is activated. The device then sends this to the server.

[1080] Server Processing

[1081] The server analyzes the received natural language instructions and emotion data, using natural language processing (NLP) libraries to extract keywords and context from the instructions, and emotion analysis algorithms to analyze the user's emotional state (e.g., "excited").

[1082] Running the generative model

[1083] The analyzed natural language data and emotion data are passed to a generative AI model, which then generates the specified 3D space and objects based on this data. For example, if the keywords "wizard" and "treasure hunt" and the emotion of excitement are detected, the generative model will generate an adventure-like environment.

[1084] Visualization

[1085] The generated 3D space and objects are sent by the server to the device, which uses this data to visually display it to the user using a 3D rendering engine, allowing the user to see the creative results in real time.

[1086] Specific examples

[1087] Consider a case where a user inputs "I want to create a scene with a small waterfall in the forest" and indicates an emotional state of "relaxed." Based on this instruction and emotional data, the server performs analysis and passes the data to the generative model. The generative model generates a relaxing environment, with the sounds of a forest and waterfall and a lush green scene with sunlight streaming in. The generated three-dimensional space is sent to the device and visualized on the user's screen. This allows the user to easily check the scene generated based on their imagination and emotions.

[1088] This invention enables users to easily generate and display three-dimensional spaces and objects without requiring advanced technology or specialized knowledge, and also enables personalized creative expression that reflects the user's emotional state.

[1089] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1090] Step 1:

[1091] User natural language input

[1092] Input: The user enters instructions in natural language through the system's interface.

[1093] Processing: The user enters the prompt sentence "I want to create a space where there is a wizard and where you can go on a treasure hunt" into the input field. This data is entered into the terminal.

[1094] Output: Natural language instructions are generated and stored on the device for processing in the next step.

[1095] Specific operation: The user enters natural language using a keyboard or voice input, and presses the send button to send the data to the device.

[1096] Step 2:

[1097] Terminal reception and emotion recognition

[1098] Input: User-entered natural language instructions

[1099] Processing: The device receives the user's input data, and at the same time, the emotion engine analyzes the user's facial expressions and voice using the camera and microphone. The emotion analysis results are packaged together with the natural language input data as a packet.

[1100] Output: Packets of natural language instructions and sentiment data are generated.

[1101] Specific operation: The device collects video and audio data through the camera or microphone, analyzes it in real time, and formats the emotion analysis results and natural language instruction data into packets.

[1102] Step 3:

[1103] Parsing input data by the server

[1104] Input: Packets of natural language instructions and sentiment data

[1105] Processing: The server disassembles the received packet and parses the instructions using a natural language processing (NLP) library. It also parses the sentiment data using a sentiment analysis algorithm (e.g., Google Cloud Natural Language API).

[1106] Output: Generates analyzed keywords, context, and sentiment data.

[1107] How it works: The server collects access logs and uses NLP libraries (e.g., spaCy, BERT) to extract keywords such as "wizard" and "treasure hunt." It also uses a sentiment analysis algorithm to identify emotions such as "excited."

[1108] Step 4:

[1109] 3D space and object generation using generative models

[1110] Input: Analyzed keywords, context, and sentiment data

[1111] Processing: The server inputs the analysis results into a generative AI model (e.g., GANs) to generate 3D spaces and objects.

[1112] Output: Generated 3D spatial data and object data

[1113] How it works: The generative model uses machine learning algorithms to generate an adventurous environment based on the input keywords "wizard" and "treasure hunt" and the emotional state "excitement." The generated data is saved as a 3D object file (e.g., FBX or OBJ format).

[1114] Step 5:

[1115] Generated data sent from the server to the device

[1116] Input: Generated 3D spatial data and object data

[1117] Processing: The server compresses the generated data and transfers it to the terminal.

[1118] Output: Compressed 3D spatial data and object data is sent to the device.

[1119] Specific operation: The server compresses the generated data in ZIP format or similar and sends it to the terminal via HTTP or WebSocket.

[1120] Step 6:

[1121] Terminal visualization

[1122] Input: Compressed data sent from the server

[1123] Processing: The device decompresses the compressed data received and visualizes it using a 3D rendering engine (e.g. Unity, Unreal Engine).

[1124] Output: The generated 3D space and objects are displayed on the user's display.

[1125] What it does: The device decodes the compressed data it receives and inputs it into the 3D rendering engine, which then renders it in real time, displaying an adventurous 3D world on the user's screen.

[1126] This allows users to intuitively generate and display three-dimensional spaces and objects based on natural language and emotions.

[1127] (Application example 2)

[1128] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1129] This invention relates to a system that allows users to intuitively generate three-dimensional spaces and three-dimensional objects and visually confirm them. However, existing systems have a problem in that it is difficult to provide personalized generated results that reflect the user's emotional state. Furthermore, interactive design that takes user emotions into consideration is particularly required in the design of virtual stores, but there is a lack of technology that can achieve this.

[1130] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to input natural language, means for analyzing the natural language input by the user, generative model means for generating a three-dimensional space or a three-dimensional object based on the analyzed natural language and the user's emotion data, and means for displaying the generated three-dimensional space or three-dimensional object to the user. This allows users to easily generate and visualize personalized three-dimensional spaces and three-dimensional objects that reflect their own emotions. Furthermore, an interactive environment that takes user emotions into consideration can be provided in the design of virtual stores.

[1131] A "user" is a person who uses the system to input natural language and instructs the generation of three-dimensional spaces and three-dimensional objects.

[1132] "Natural language" refers to the human language used by people on a daily basis to input instructions and information into a system.

[1133] "Means of analysis" refers to algorithms or programs that analyze input natural language and emotional data and extract the necessary information.

[1134] "Emotion data" is data that represents the user's current emotional state, and adjusts the system's behavior based on this data.

[1135] A "generative model means" is a machine learning algorithm or AI model for generating three-dimensional space or three-dimensional objects based on analyzed natural language or emotion data.

[1136] A "three-dimensional space" is a stereoscopic environment or location that is generated based on user instructions.

[1137] A "three-dimensional object" is a solid object or character placed in a three-dimensional space.

[1138] The "display means" refers to a display device or rendering engine for visually presenting the generated three-dimensional space or three-dimensional object to the user.

[1139] A "virtual store" is a virtual store space that users can visit online.

[1140] The present invention relates to a system that allows users to intuitively create three-dimensional spaces and objects using natural language. The system provides personalized results based on the user's input and emotional data. The present invention is particularly useful for designing virtual stores.

[1141] System configuration

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

[1143] User input:

[1144] A device (e.g., smartphone, tablet, PC) that allows a user to input natural language.

[1145] Natural language analysis means:

[1146] Software for analyzing natural language input (e.g., spaCy, BERT).

[1147] Emotion analysis means:

[1148] Software for analyzing user emotion data (e.g., TensorFlow, OpenAI GPT-4).

[1149] Generative model means:

[1150] Generative models for generating 3D spaces and objects based on natural language and emotion data (e.g., Unity3D, Blender).

[1151] Display means:

[1152] Software for visually displaying generated 3D spaces and objects (e.g., WebGL, Three.js).

[1153] Program processing

[1154] The server analyzes the data received through the user's natural language input. It uses natural language processing libraries such as spaCy and BERT as its natural language analysis method. The analyzed keywords and context are passed to a sentiment analysis method, which uses TensorFlow and OpenAI GPT-4 to determine the user's emotional state.

[1155] The analyzed natural language and emotion data are passed to a generative modeling means, which uses a generative AI model such as Unity3D or Blender to generate 3D spaces and objects according to the user's instructions. For example, if a user inputs "I want to create a relaxing cafe" and the emotion data indicates a relaxed state, the generative model will automatically design a cafe with lush plants, wooden furniture, and calming music.

[1156] The generated 3D spaces and objects are displayed in real time on the user's device using WebGL and Three.js, allowing users to instantly see personalized creative results based on their instructions and emotions.

[1157] Specific examples

[1158] For example, if a user types, "I want a virtual cafe with lots of greenery, nature, and relaxation," and indicates the emotion "relaxed," the app will automatically generate and visually display a virtual cafe with lush plants, wooden furniture, and calming music.

[1159] Example prompt sentence:

[1160] "Please generate a virtual cafe. The user requested a relaxing space with lots of greenery."

[1161] "The spacious layout creates an airy space."

[1162] As described above, the present invention intuitively generates and displays advanced three-dimensional spaces and objects based on the user's natural language input and emotional data, providing excellent personalization functions, particularly in the design of virtual stores.

[1163] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1164] Step 1:

[1165] Users input their virtual store design preferences in natural language using a device (e.g., smartphone, tablet, or PC). This input includes specific requests, images, and emotions. The input data is captured in text format on the device.

[1166] Step 2:

[1167] The device sends the received natural language input to the server. The server receives this input data and analyzes the text using natural language analysis tools (e.g., spaCy, BERT). As a result of the analysis, keywords and contextual information are extracted. The input is natural language text, and the output is analyzed keywords and contextual information.

[1168] Step 3:

[1169] The server uses emotion analysis tools (e.g., TensorFlow, OpenAI GPT-4) to acquire and analyze the user's emotion data when inputting natural language. The input is natural language text and user information, and the output is emotion data that indicates the user's emotional state.

[1170] Step 4:

[1171] The analyzed natural language data and emotion data are input into a generative model. The server uses a generative modeling tool (e.g., Unity3D, Blender) to generate a 3D space or a 3D object based on this data. The input is keywords, contextual information, and emotion data, and the output is design data for the 3D space or object.

[1172] Step 5:

[1173] The server sends the generated design data to the terminal. The terminal uses a display method (e.g., WebGL, Three.js) to visually display the generated three-dimensional space and objects to the user in real time. The input is the design data, and the output is the visually displayed three-dimensional space and objects.

[1174] Step 6:

[1175] The user can check the 3D space and objects generated in real time and make corrections as necessary. By repeating the process from step 1, the optimal virtual store that meets the user's requirements is completed.

[1176] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1177] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1178] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1179] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1180] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1181] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1182] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1183] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1184] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1185] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1186] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1187] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1188] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1189] 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.

[1190] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1191] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1192] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1193] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1194] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1195] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1196] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1197] The following is further disclosed regarding the above embodiment.

[1198] (Claim 1)

[1199] a means for a user to input natural language;

[1200] means for analyzing natural language input by the user;

[1201] a generative modeling means for generating a three-dimensional space or a three-dimensional object based on the parsed natural language;

[1202] means for displaying the generated three-dimensional space or three-dimensional object to a user;

[1203] A system including:

[1204] (Claim 2)

[1205] 10. The system of claim 1, further comprising means for generating music or a game based on the analyzed natural language.

[1206] (Claim 3)

[1207] 2. The system according to claim 1, wherein the natural language analyzing means analyzes natural language using a machine learning algorithm.

[1208] "Example 1"

[1209] (Claim 1)

[1210] a means for a user to input natural language;

[1211] means for analyzing natural language input by the user;

[1212] a generative modeling means for generating a three-dimensional space or a three-dimensional object based on the parsed natural language;

[1213] means for displaying the generated three-dimensional space or three-dimensional object to a user;

[1214] means for transmitting the generated three-dimensional space or three-dimensional object from a server to a terminal;

[1215] means for rendering a three-dimensional space or a three-dimensional object on said terminal;

[1216] A system including:

[1217] (Claim 2)

[1218] 10. The system of claim 1, further comprising means for generating music or a game based on the analyzed natural language.

[1219] (Claim 3)

[1220] 2. The system according to claim 1, wherein the natural language analyzing means analyzes natural language using a machine learning algorithm.

[1221] "Application Example 1"

[1222] (Claim 1)

[1223] a means for a user to input natural language;

[1224] means for analyzing natural language input by the user;

[1225] a generative modeling means for generating a three-dimensional space or a three-dimensional object based on the parsed natural language;

[1226] means for displaying the generated three-dimensional space or three-dimensional object to a user;

[1227] means for customizing the layout or display of the virtual store based on the analyzed natural language;

[1228] A system including:

[1229] (Claim 2)

[1230] 10. The system of claim 1, further comprising means for generating music or a game based on the analyzed natural language.

[1231] (Claim 3)

[1232] 2. The system according to claim 1, wherein the natural language analyzing means analyzes natural language using a machine learning algorithm.

[1233] "Example 2: Combining Emotion Engines"

[1234] (Claim 1)

[1235] a means for a user to input natural language;

[1236] means for analyzing the natural language and sentiment input by the user;

[1237] a generative modeling means for generating a three-dimensional space or a three-dimensional object based on the analyzed natural language and emotion data;

[1238] means for displaying the generated three-dimensional space or three-dimensional object to a user;

[1239] A system including:

[1240] (Claim 2)

[1241] 10. The system of claim 1, further comprising means for generating music or games based on the analyzed natural language and emotion data.

[1242] (Claim 3)

[1243] 2. The system according to claim 1, wherein the natural language analysis means and the sentiment analysis means use machine learning algorithms to analyze natural language and sentiment.

[1244] "Application example 2 when combining emotion engines"

[1245] (Claim 1)

[1246] a means for a user to input natural language;

[1247] means for analyzing natural language input by the user;

[1248] a generative modeling means for generating a three-dimensional space or a three-dimensional object based on the analyzed natural language and the user's emotion data;

[1249] means for displaying the generated three-dimensional space or three-dimensional object to a user;

[1250] A system including:

[1251] (Claim 2)

[1252] 10. The system of claim 1, further comprising means for generating a three-dimensional space or objects of the virtual store based on the analyzed natural language and emotion data.

[1253] (Claim 3)

[1254] 2. The system according to claim 1, wherein the natural language analyzing means analyzes natural language using a machine learning algorithm. [Explanation of symbols]

[1255] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for a user to input natural language; means for analyzing natural language input by the user; a generative modeling means for generating a three-dimensional space or a three-dimensional object based on the parsed natural language; means for displaying the generated three-dimensional space or three-dimensional object to a user; A system including:

2. 10. The system of claim 1, further comprising means for generating music or games based on the analyzed natural language.

3. 2. The system according to claim 1, wherein the natural language analyzing means analyzes natural language using a machine learning algorithm.

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