system

Abstract

We provide the system. [Solution] A means of entering project requirements, A computation means using generative artificial intelligence to analyze the aforementioned project requirements, A means for generating and visualizing a virtual environment based on the aforementioned analyzed project requirements, A means of evaluating the feasibility of a project using a visualized virtual environment, A means for presenting the analysis results based on the aforementioned evaluation, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the initial stage of a project, when the requirement definition is ambiguous or the decision-making is delayed, delays or failures of the entire project often occur. Therefore, it is necessary to accurately visualize the requirements of the project and share information in a form that is easy for all stakeholders to understand. However, to achieve this, specialized knowledge and resources are required, and there is a problem that it takes a lot of time and cost.

Means for Solving the Problems

[0005] The present invention solves the above problem by providing a system having means for inputting project requirements, means for calculating requirements using generative artificial intelligence, means for generating and visualizing a virtual environment based on the analysis results, means for evaluating feasibility, and means for presenting the results. This enables the rapid extraction of problems and visualization of requirements in the early stages of a project, thereby supporting effective decision-making.

[0006] "Project requirements" are documents or information that clearly define the specifications, functions, and constraints necessary to carry out a project.

[0007] "Generative artificial intelligence" is a type of artificial intelligence that has the ability to learn from large amounts of data and produce appropriate responses or generation in response to given input.

[0008] "Analysis means" refers to methods and techniques for analyzing provided data and information and extracting meaning and patterns.

[0009] A "virtual environment" refers to an artificial environment simulated on a computer, including models and systems that users can interact with.

[0010] "Means of visualization" refers to technologies and methods that display data and analysis results as visual information, making them easier for users to understand.

[0011] "Feasibility" is a criterion used to evaluate whether a proposed project or plan is technically and economically feasible.

[0012] "Evaluation methods" refer to techniques and methods for measuring and analyzing an object based on specific criteria and judging the results.

[0013] "Means of presentation" refers to methods and techniques for clearly communicating analysis results and evaluation content to users. [Brief explanation of the drawing]

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

Modes for Carrying Out the Invention

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

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

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

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

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

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

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

[0022] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0035] This invention relates to a system for efficiently visualizing project requirements and evaluating feasibility. The system mainly consists of three main components: a server, a terminal, and a user.

[0036] User input of requirements

[0037] Users access an interface using their devices to input project requirements in text or voice. For example, if a user is requesting the development of a new web application, they can input or record the features and specifications on the device screen.

[0038] Server-based requirements analysis

[0039] When requirements are submitted from a terminal, the server uses generative artificial intelligence to analyze them. The generative AI model used here has been pre-trained on a large dataset and has the ability to understand what kind of project structure the provided requirements represent. Through this analysis, the server prepares the foundational data for building the virtual environment.

[0040] Virtual environment creation and visualization

[0041] Based on the analysis data obtained by the server, the terminal generates a virtual environment. This environment is provided interactively so that the user can visually grasp the completed form of the project. For example, the UI prototype and user flow of the requested web application are visualized.

[0042] Feasibility and challenge analysis

[0043] The server evaluates the feasibility of the project based on the visualized virtual environment. This evaluation includes technical feasibility, resource allocation efficiency, and timeline estimates. It also simultaneously detects potential problems and provides feedback to the user through the terminal.

[0044] Facilitating feedback and input of new requirements

[0045] Based on the feedback provided by the device, the user re-examines the requirements. Additions and modifications are made as needed, and the generating AI then re-analyzes the changes and outputs a new virtual environment, continuously adjusting the requirements.

[0046] This process allows users to clarify ambiguous requirements in the early stages of a project, facilitate smooth agreement among stakeholders, and support rapid decision-making.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] Users enter project requirements on their devices. Input can be done via text or voice, and detailed specifications and desired conditions can be included through the interface.

[0050] Step 2:

[0051] The terminal sends the entered requirement data to the server. The transmitted data is securely transferred to the server using encryption technology.

[0052] Step 3:

[0053] The server analyzes the requirements data received from the terminal. Using a generative artificial intelligence model, the server extracts the structure and related information of the requirements and generates the basic data for the virtual environment.

[0054] Step 4:

[0055] The server builds a virtual environment based on the analysis results. This virtual environment includes a prototype based on the project's final structure and design.

[0056] Step 5:

[0057] The terminal receives virtual environment data from the server and visualizes it for the user. The visualization is in an interactive format, allowing the user to grasp a concrete image of the project.

[0058] Step 6:

[0059] The server evaluates the visualized prototype and analyzes its feasibility. This includes technical feasibility, resource optimization, and risk assessment.

[0060] Step 7:

[0061] The terminal receives analysis results from the server and displays them to the user. These results include potential problems and suggestions for improvement in the project.

[0062] Step 8:

[0063] Based on feedback from the user's device, they re-enter and modify their requirements as needed. The modified requirements are then analyzed again on the server, and a new virtual environment is generated.

[0064] (Example 1)

[0065] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0066] In modern project development, unclear requirements and insufficient feasibility assessments are common challenges that hinder the efficient progress of project planning. In particular, unclear requirements in the early stages of a project lead to wasted time and resources and make consensus building among stakeholders difficult. Therefore, a system is needed to clarify requirements and quickly and accurately assess feasibility.

[0067] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0068] In this invention, the server includes means for a user to input project requirements via a computing device, information processing means using generative artificial intelligence to analyze the project requirements, and means for a computer to generate and display a virtual space based on the analyzed project requirements. This enables the user to clarify requirements in the early stages of a project and supports smooth decision-making among stakeholders.

[0069] A "user" refers to an individual or organization that operates the system and inputs project requirements.

[0070] "Calculation unit" refers to electronic devices such as computers and smartphones, which are used by users to input project requirements.

[0071] "Project requirements" refer to detailed specifications regarding the functions, performance, and design necessary to carry out the project.

[0072] "Generative artificial intelligence" refers to algorithms that learn from large datasets and have the ability to generate and analyze information for specific tasks, much like a human would.

[0073] "Information processing means" refers to a system that uses computer software and hardware to analyze, process, and store data.

[0074] A "virtual space" is a computer-generated simulation environment that allows users to visually check the results of a project.

[0075] "Means of display" refers to methods of providing the generated virtual space or analysis results to the user through a screen or display.

[0076] "Feasibility" is an indicator used to evaluate whether a planned project is technically, economically, and temporally feasible.

[0077] "Assessment" refers to the act of evaluating or estimating, and is used to determine the feasibility of a project.

[0078] This invention is a system for clarifying project requirements and quickly evaluating feasibility. The system mainly consists of three components: user, terminal, and server.

[0079] The user inputs project requirements via a computing device. Input is in text or voice format, and a user-friendly interface is provided. For example, a user might input requirements for a new web application development, such as "We would like to implement user authentication and an administration dashboard." An example of a prompt in this case might be, "Analyze the project requirements and create an appropriate virtual prototype."

[0080] The terminal is responsible for sending the entered requirements data to the server. In this case, the terminal converts the data into the appropriate format and sends it to the server using a communication protocol.

[0081] The server utilizes a generative artificial intelligence model to analyze project requirements submitted by the user. This generative AI model is pre-trained on a large dataset. Based on the analysis results, the server generates foundational data for creating a virtual environment, which is later used by the computing system.

[0082] The computer uses data transmitted from the server to generate a virtual space based on project requirements, allowing users to visually verify the project's results. This virtual space intuitively represents the project's UI prototype and functional flow, presenting the generated content in a way that users can interact with and verify.

[0083] This system's configuration and process enable users to define project requirements in the early stages and support efficient decision-making.

[0084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0085] Step 1:

[0086] The user inputs project requirements via a computing device. Input can be in text or voice format. For example, the user might input, "The new web application requires user authentication functionality." The input data generates text or voice data as the requirement. This data is then prepared for transmission to the terminal.

[0087] Step 2:

[0088] The terminal converts the requirements data entered by the user into a format and sends it to the server. Specifically, it encodes text data and audio data as needed and transfers them to the server using the appropriate communication protocol. The input is the requirements data received from the user, and the output is the formatted data sent to the server.

[0089] Step 3:

[0090] The server uses a generating AI model to analyze the requirements data received from the terminal. For example, if the prompt is "I would like to implement user authentication functionality and a management dashboard," the AI ​​model analyzes the requirements into a project structure and generates a list of necessary functions. The input is formatted requirements data sent from the terminal, and the output is the analyzed project structure information.

[0091] Step 4:

[0092] The server generates the foundational data for the virtual environment based on the analyzed structural information. This includes data that defines the project's UI prototype and functional flow. The input is the project structural information analyzed by the AI, and the output is the foundational data for generating the virtual environment.

[0093] Step 5:

[0094] The terminal uses the underlying data sent from the server to generate and visualize the project's virtual environment. Specifically, it uses a 3D modeling engine to visualize elements and provides the user with an interactive experience. The input is the underlying data of the virtual environment sent from the server, and the output is a user-manipulable visual virtual space.

[0095] Step 6:

[0096] The server evaluates the generated virtual environment and assesses the feasibility of the project. This includes technical feasibility, resource efficiency, and project duration estimates. The input is data from the visualized virtual environment, and the output is the assessment results and analytical information.

[0097] Step 7:

[0098] The user reviews feedback and suggestions for improvement from the server via their terminal and modifies the project requirements. If necessary, they re-enter the requirements and run the analysis and virtualization process again. The input is the feedback from the server, and the output is the revised project requirements.

[0099] This series of steps enables the system to efficiently clarify project requirements and assess feasibility.

[0100] (Application Example 1)

[0101] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0102] In construction projects, unclear requirements and insufficient feasibility assessments during the design phase can lead to plan changes and work delays. Furthermore, the difficulty in intuitively verifying plans on-site and inadequate communication among stakeholders can hinder consensus building, posing a significant challenge.

[0103] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0104] In this invention, the server includes means for inputting project specifications, processing means using generative artificial intelligence to analyze the project specifications, means for constructing a three-dimensional virtual environment based on the analyzed project specifications and presenting it visually, and means for enabling the display and operation of the virtual environment on-site via a mobile terminal. This makes it possible to clarify project specifications at the design stage and to construct a highly feasible plan. Furthermore, it can facilitate rapid plan confirmation on-site and smooth communication among stakeholders.

[0105] "Project specifications" refer to the requirements and conditions related to the design and planning of a construction project, and detailed designs and work plans are formulated based on these specifications.

[0106] "Generative artificial intelligence" is an artificial intelligence technology that has the ability to learn from large amounts of data, analyze input information, and generate appropriate output.

[0107] A "three-dimensional virtual environment" is a three-dimensional space generated on a computer, intended to visually simulate the completed form of a project.

[0108] A "mobile device" is a communication device that a user can carry with them, and includes hardware capable of accessing the internet and running applications.

[0109] The embodiments for carrying out the invention are as follows:

[0110] First, users input specifications for the construction project using mobile devices such as smartphones or tablets. These devices are provided with a user interface for text or voice input. The information entered by the user constitutes the "project specifications," which include the project requirements and conditions.

[0111] Next, the server uses generative artificial intelligence (generative AI model) to analyze the project specifications received from the user. This analysis utilizes a generative AI model trained on large-scale data (e.g., OpenAI® GPT-4®) to understand the structure and requirements of the input information. Based on this analysis, the server generates the foundational data for constructing a three-dimensional virtual environment.

[0112] Subsequently, a three-dimensional virtual environment is constructed on the terminal and visually presented to the user. This allows the user to interactively check the completed form of the project. This virtual environment can be modified and adjusted in real time, allowing for a visual understanding of the design and construction plan of the construction project.

[0113] Furthermore, the server evaluates the project's technical feasibility and potential challenges based on the visualized virtual environment. This evaluation result is fed back to the user's mobile device as an analysis, facilitating the user to review and adjust the project specifications as needed.

[0114] As a concrete example, in a project where a user designs a new school building, they are prompted with the message, "Please enter the design requirements for the new school building. Please include the number of classrooms, facility layout, material selection, etc." and input the design requirements. In the generated 3D virtual environment, they can verify the suitability of the design and modify the requirements as needed. Through this process, the user can develop an appropriate plan while communicating smoothly with stakeholders.

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] The user enters project specifications using a mobile device. This input is done via text or voice. Specifically, the user enters design details into the device's interface in response to a prompt such as, "Please enter the design requirements for the new school building." This input data is sent to the server. The input is the project specification, and the output is the transmission of the specification data.

[0118] Step 2:

[0119] The server analyzes the received specification data using a generative AI model. Here, the generative AI model performs textual analysis of the project specifications and creates a data structure to understand the requirements. Specifically, the generative AI analyzes the input requirements and models the structure of the construction project. The input is the specification data, and the output is the data structure of the analysis result.

[0120] Step 3:

[0121] The server generates data for a three-dimensional virtual environment based on the data structure of the analysis results. Specifically, the server uses a 3D modeling tool to construct a virtual model of the construction project. This model is designed to allow the user to intuitively grasp the overall picture of the project. The input is the data structure of the analysis results, and the output is the virtual environment data.

[0122] Step 4:

[0123] The terminal receives virtual environment data sent from the server and presents it visually to the user. Specifically, a three-dimensional virtual environment is displayed on the terminal's screen, allowing the user to examine each part of the model in detail. The input is virtual environment data, and the output is visual feedback to the user.

[0124] Step 5:

[0125] Users interact with a three-dimensional virtual environment to verify and modify their designs. Specifically, users operate a terminal to click and drag elements within the model, and the results of these operations are displayed on the terminal. The input is the user's actions, and the output is the display of the results.

[0126] Step 6:

[0127] The server evaluates the technical feasibility and potential challenges of a project based on the results of operations from the terminal, and provides feedback to the user. Specifically, the server performs simulations, analyzes the evaluation results, and sends them to the user. The input is operation result data, and the output is an evaluation result report.

[0128] Step 7:

[0129] Based on the evaluation report received, the user reviews the project specifications again as needed and makes necessary corrections. Specifically, the user checks the evaluation report, re-enters the proposed revisions to the project specifications into the terminal, and the new specification data is sent to the server. The input is the specification revision based on the evaluation results, and the output is the refined specification data.

[0130] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0131] This invention is a system that efficiently visualizes project requirements and evaluates feasibility while considering user emotions. This system is mainly composed of a server, terminals, and an emotion engine.

[0132] Input and analysis of project requirements

[0133] The user begins by using their device to input project requirements via text or voice. For example, they can enter detailed development requirements for a new mobile application. The entered data is then sent from the device to the server.

[0134] The server uses generative artificial intelligence to analyze requirements based on user input. During the analysis process, data is abstracted and prepared for a virtual environment. This analysis helps understand the project structure and interface elements.

[0135] Virtual environment creation and visualization

[0136] Based on the analyzed data, the server generates a virtual environment. This includes the project design and a prototype of the operation flow. The terminal receives this and displays it to the user in an interactive format. This allows the user to gain a concrete understanding of the completed project and confirm the proposed specifications.

[0137] Application of the emotion engine

[0138] An emotion engine built into the device recognizes the user's emotions during visualization. This uses data collected through the camera and microphone. For example, the engine analyzes the user's facial expressions and vocal intonation, and records their emotional response to project requirements as feedback.

[0139] The server uses this sentiment data to dynamically adjust the user experience. For example, if a user expresses frustration, the system can improve usability by displaying an interface guide.

[0140] Feasibility assessment and proposal for improvement

[0141] The server evaluates the feasibility of the project based on a visualized virtual environment and sentiment data. This evaluation takes into account technical limitations, user feedback, and potential risks. The terminal presents these evaluation results to the user, clarifying the project's progress and areas for improvement.

[0142] By applying this system, users can gain support for clarifying requirements early in a project, improving the user experience with emotional considerations in mind, and making rapid decisions.

[0143] The following describes the processing flow.

[0144] Step 1:

[0145] The user enters project requirements using a terminal. The user describes detailed specifications and desired features in the interface via text message or voice input.

[0146] Step 2:

[0147] The terminal sends the entered requirement data to the server. This transmission is performed via a secure protocol, ensuring data integrity.

[0148] Step 3:

[0149] The server uses a generative artificial intelligence model to analyze the requirements data it receives. The server analyzes the text data of the requirements and identifies the core elements necessary for generating the virtual environment.

[0150] Step 4:

[0151] Based on the analysis results, the server generates a virtual environment. This environment includes the project's layout and interface design. The virtual environment data is then sent to the terminal.

[0152] Step 5:

[0153] The terminal visualizes and displays the virtual environment to the user. The user can interact with it, review the generated prototype, and evaluate its usability and visuals.

[0154] Step 6:

[0155] An emotion engine built into the device recognizes the user's emotions. Using the camera and microphone, it analyzes the user's facial expressions and voice tone in real time and sends the emotion data to a server.

[0156] Step 7:

[0157] The server evaluates the user experience based on emotional data and the virtual environment. The server analyzes user responses to the feasibility of the project and generates feedback.

[0158] Step 8:

[0159] The terminal displays evaluation results and feedback from the server to the user. Suggested improvements and technical adjustments are shown, allowing the user to modify their requirements based on this information.

[0160] Step 9:

[0161] The user modifies the project requirements based on feedback and re-enters them. The modified requirements are then analyzed again on the server, and a new virtual environment is generated.

[0162] (Example 2)

[0163] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0164] In the early stages of project development, it is crucial to efficiently visualize user requirements and enhance feasibility while considering emotional responses. However, conventional systems fail to adequately improve the user experience, leading to challenges such as misunderstandings of requirements and inefficient prototype creation. Furthermore, there is a lack of mechanisms for systems to dynamically utilize user emotional feedback.

[0165] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0166] In this invention, the server includes a device for inputting project requirements, a computing device using generative artificial intelligence for analyzing the project requirements, and a device for generating and visualizing a virtual environment based on the analyzed project requirements. This makes it possible to quickly generate a virtual prototype based on user requirements and dynamically improve usability by utilizing emotional feedback obtained by an emotion recognition engine.

[0167] "Project requirements" is a concept that refers to the specific requirements regarding the functions and specifications of a project.

[0168] "Device" refers to a mechanical or electronic mechanism designed to perform a specific function.

[0169] "Generative artificial intelligence" refers to an artificial intelligence model that learns on its own based on given data and has the ability to perform necessary analysis and predictions.

[0170] A "computational device" refers to a hardware or software system that receives input data and performs programmed arithmetic operations.

[0171] A "virtual environment" refers to a simulated or modeled environment created using computer technology, providing a domain where users can interact.

[0172] "Visualization" refers to the process of transforming abstract or complex data into a more easily understandable visual representation.

[0173] "Feasibility" refers to a measure of whether a particular plan or project can be carried out physically, technically, or economically.

[0174] "Analysis results" refer to conclusions or insights obtained by processing and analyzing data and information.

[0175] An "emotion recognition engine" refers to a system or software that analyzes voice and facial expression data to determine a user's emotional state.

[0176] "User experience" refers to the overall feeling or impression that a user experiences when using a system.

[0177] This invention is a system that efficiently visualizes project requirements and dynamically evaluates their feasibility while considering user emotions. This system is primarily implemented using a server, terminals, and an emotion recognition engine.

[0178] The user enters project requirements using a terminal. The terminal accepts input via text and voice, providing an interface that allows the user to freely express specific requirements, such as "develop customer management functionality for a new mobile application." The entered data is sent from the terminal to the server.

[0179] The server uses a generative AI model to analyze the received project requirements. This generative AI model uses a predefined prompt, "Analyze project requirements and specify the necessary components," to abstract the input requirements and extract the necessary interface elements. Based on the analyzed information, the server generates a virtual environment and visualizes the project's interface design and operation flow.

[0180] The generated virtual environment is sent back to the terminal and displayed interactively to the user. This allows the user to dynamically review the prototype of the proposed project and evaluate the specifications in detail.

[0181] Furthermore, the emotion recognition engine built into the device collects the user's emotions in real time through the camera and microphone. This analyzes the user's facial expressions and voice intonation, and their emotional response to project requirements is recorded as feedback.

[0182] The server analyzes this collected emotional data and dynamically adjusts the user experience. For example, if a user shows a confused expression, the device can display additional guidance to improve usability. In this way, users can improve the UX by incorporating emotional feedback from the early stages of the project, enabling them to make quick and effective decisions.

[0183] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0184] Step 1:

[0185] The user enters project requirements through their device. The input is done via text or voice, and the device converts this into digital request data and sends it to the server. A specific example of input might be a requirement such as, "A new mobile application needs customer management functionality." The submitted data arrives at the server as information containing the basic project requirements.

[0186] Step 2:

[0187] The server launches a generative AI model to analyze the received requirements data. Here, the prompt "Analyze project requirements and specify the necessary components" is used to instruct the AI ​​to perform the analysis. The AI ​​model abstracts the requirements, computes interface and functional elements, and outputs the results as a series of datasets. These datasets contain the project's structural information.

[0188] Step 3:

[0189] Based on the analyzed data, the server generates a virtual environment. This virtual environment includes the project's design and operational flow. The server sends this information to the terminal, allowing the user to visually verify it. The output sent from the server is a digital model of the prototype.

[0190] Step 4:

[0191] The terminal processes the received virtual environment data and displays it to the user in an interactive format. This display allows the user to observe the concrete visuals of the project and evaluate whether it meets the requirements. The displayed output is a user-operable interface screen.

[0192] Step 5:

[0193] The emotion recognition engine built into the device collects user emotion data through the camera and microphone. The engine analyzes the user's facial expressions and voice tone to output an emotion state, which is then transmitted to a server in real time. This output includes emotional information such as the user's happiness or dissatisfaction.

[0194] Step 6:

[0195] The server uses the received sentiment data to dynamically adjust the user experience when necessary. For example, if the user is confused, the server sends a guide or tutorial screen to the device. This transmission outputs instructions for improving the interface, which are then displayed to the user on the device.

[0196] Step 7:

[0197] Finally, the server comprehensively evaluates the collected technical and emotional data to determine the project's feasibility. The resulting evaluation is sent to the terminal as a report, including the technical evaluation and user feedback, and presented to the user. This allows the user to specifically understand areas for improvement in the project and the next steps.

[0198] (Application Example 2)

[0199] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0200] Traditional project management systems rely heavily on human judgment for visualizing project requirements and evaluating feasibility, and because they fail to consider the user's feelings, improvements in the user experience are limited. Furthermore, they lack support for resolving users' potential anxieties and problems early on, beyond simply meeting requirements.

[0201] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0202] In this invention, the server includes a computation means using generative artificial intelligence to analyze project requirements, a means for generating and visualizing a virtual environment based on the analyzed requirements, and a means for analyzing user emotions and dynamically adjusting the experience. This allows for clarification of requirements through concrete visualization from the early stages of a project, and by adjusting responses based on user emotions, a better user experience can be provided, enabling faster decision-making.

[0203] "Project requirements" refer to the specific conditions and criteria necessary to achieve the project's objectives.

[0204] "Generative artificial intelligence" is an AI technology that can create new information based on input data and perform judgments and analyses.

[0205] A "virtual environment" refers to a space or situational simulation that provides a realistic experience generated on a computer.

[0206] "Visualization" is a technique that makes abstract data and information easier to understand by representing them graphically.

[0207] "Emotional data" refers to information about a user's feelings obtained and analyzed from their facial expressions and voice.

[0208] "Dynamic adjustment of user experience" refers to the immediate optimization of services and interfaces provided in response to user reactions and emotions.

[0209] This invention is a system primarily implemented using a server and a terminal. The server utilizes generative artificial intelligence to analyze project requirements input by the user. This analysis abstracts the project structure and generates the necessary virtual environment. The generated virtual environment includes the design and operation flow, and is visualized by the terminal. The user can interact with this virtual environment through the terminal.

[0210] The device incorporates an emotion engine that acquires user emotion data through the camera and microphone. This emotion data is obtained by analyzing the user's facial expressions and vocal intonation. Based on this emotion data, the server dynamically adjusts the user experience. Specifically, if the user shows signs of anxiety or confusion, the server improves the user experience by providing operational guides and navigation.

[0211] For example, if a user enters the requirements for a "simple cooking project at home," the server generates the project's steps and layout as a virtual environment, which is then visualized on the user's device. If the user experiences any anxiety while progressing through the cooking steps, the system will offer support, such as asking, "Do you have all the ingredients?"

[0212] An example of a prompt to the generating AI model would be: "Analyze the user's project requirements and generate a virtual environment in an easy-to-understand format. Based on sentiment data, suggest improvements to the project and enhance the user experience. The project is about 'simple cooking at home.' Engage in dialogue that responds to the user's emotions."

[0213] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0214] Step 1:

[0215] Users enter project requirements using a terminal. This input can be in text or voice format. The entered information is collected by the terminal and sent to the server. The input data includes specific project goals and conditions.

[0216] Step 2:

[0217] The server analyzes the received project requirements using generative artificial intelligence. This analysis process extracts the structural elements of the project from the input data and further abstracts related data. As a result, foundational data for generating the virtual environment is output.

[0218] Step 3:

[0219] Based on the analyzed project requirements, the server generates a virtual environment. This virtual environment includes the design and operational flow for a visual representation of the project. The generated data is sent to the terminal and displayed to the user, allowing them to concretely visualize the project concept.

[0220] Step 4:

[0221] The device uses a built-in emotion engine to collect user emotion data. This data is collected through the camera and microphone, and the user's facial expressions and voice intonation are analyzed. As a result of this emotion analysis, the user's emotional state is output.

[0222] Step 5:

[0223] The server uses emotional data to dynamically adjust the user experience. For example, if a user shows signs of anxiety, the server will instruct the device to provide operational guidance or suggest interactions. This prompting is done to improve the user experience.

[0224] Step 6:

[0225] The server evaluates the feasibility of the project based on the virtual environment and sentiment data. This evaluation process takes into account technical constraints and potential risks. The evaluation results are presented to the user as an analysis. This allows the user to identify areas for improvement in the project and make adjustments as needed.

[0226] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0227] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0228] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0229] [Second Embodiment]

[0230] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0231] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0232] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0233] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0234] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0235] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0236] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0237] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0238] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0239] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0240] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0241] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0242] This invention relates to a system for efficiently visualizing project requirements and evaluating feasibility. The system mainly consists of three main components: a server, a terminal, and a user.

[0243] User input of requirements

[0244] Users access an interface using their devices to input project requirements in text or voice. For example, if a user is requesting the development of a new web application, they can input or record the features and specifications on the device screen.

[0245] Server-based requirements analysis

[0246] When requirements are submitted from a terminal, the server uses generative artificial intelligence to analyze them. The generative AI model used here has been pre-trained on a large dataset and has the ability to understand what kind of project structure the provided requirements represent. Through this analysis, the server prepares the foundational data for building the virtual environment.

[0247] Virtual environment creation and visualization

[0248] Based on the analysis data obtained by the server, the terminal generates a virtual environment. This environment is provided interactively so that the user can visually grasp the completed form of the project. For example, the UI prototype and user flow of the requested web application are visualized.

[0249] Feasibility and challenge analysis

[0250] The server evaluates the feasibility of the project based on the visualized virtual environment. This evaluation includes technical feasibility, resource allocation efficiency, and timeline estimates. It also simultaneously detects potential problems and provides feedback to the user through the terminal.

[0251] Facilitating feedback and input of new requirements

[0252] Based on the feedback provided by the device, the user re-examines the requirements. Additions and modifications are made as needed, and the generating AI then re-analyzes the changes and outputs a new virtual environment, continuously adjusting the requirements.

[0253] This process allows users to clarify ambiguous requirements in the early stages of a project, facilitate smooth agreement among stakeholders, and support rapid decision-making.

[0254] The following describes the processing flow.

[0255] Step 1:

[0256] Users enter project requirements on their devices. Input can be done via text or voice, and detailed specifications and desired conditions can be included through the interface.

[0257] Step 2:

[0258] The terminal sends the entered requirement data to the server. The transmitted data is securely transferred to the server using encryption technology.

[0259] Step 3:

[0260] The server analyzes the requirements data received from the terminal. Using a generative artificial intelligence model, the server extracts the structure and related information of the requirements and generates the basic data for the virtual environment.

[0261] Step 4:

[0262] The server builds a virtual environment based on the analysis results. This virtual environment includes a prototype based on the project's final structure and design.

[0263] Step 5:

[0264] The terminal receives virtual environment data from the server and visualizes it for the user. The visualization is in an interactive format, allowing the user to grasp a concrete image of the project.

[0265] Step 6:

[0266] The server evaluates the visualized prototype and analyzes its feasibility. This includes technical feasibility, resource optimization, and risk assessment.

[0267] Step 7:

[0268] The terminal receives analysis results from the server and displays them to the user. These results include potential problems and suggestions for improvement in the project.

[0269] Step 8:

[0270] Based on feedback from the user's device, they re-enter and modify their requirements as needed. The modified requirements are then analyzed again on the server, and a new virtual environment is generated.

[0271] (Example 1)

[0272] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0273] In modern project development, unclear requirements and insufficient feasibility assessments are common challenges that hinder the efficient progress of project planning. In particular, unclear requirements in the early stages of a project lead to wasted time and resources and make consensus building among stakeholders difficult. Therefore, a system is needed to clarify requirements and quickly and accurately assess feasibility.

[0274] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0275] In this invention, the server includes means for a user to input project requirements via a computing device, information processing means using generative artificial intelligence to analyze the project requirements, and means for a computer to generate and display a virtual space based on the analyzed project requirements. This enables the user to clarify requirements in the early stages of a project and supports smooth decision-making among stakeholders.

[0276] "User" refers to a person or organization that operates the system and inputs project requirements.

[0277] "Computing device" refers to an electronic device such as a computer or smartphone, which is used by the user to input project requirements.

[0278] "Project requirements" refer to the detailed specifications regarding the functions, performance, and design necessary to carry out a project.

[0279] "Generative artificial intelligence" refers to an algorithm that learns based on a large dataset and has the ability to generate or analyze information like a human for specific tasks.

[0280] "Information processing means" refers to a mechanism for analyzing, processing, and storing data using computer software and hardware.

[0281] "Virtual space" refers to a simulated environment generated by a computer, where the user can visually confirm the results of a project.

[0282] "Display means" refers to a method of providing the generated virtual space and analysis results to the user through a screen or display.

[0283] "Feasibility" refers to an indicator for evaluating whether a planned project is technically, economically, and temporally feasible.

[0284] "Assessment" refers to the act of evaluating or estimating, and is used to determine the feasibility of a project.

[0285] This invention is a system for clarifying project requirements and quickly evaluating feasibility. The system mainly consists of three components: a user, a terminal, and a server.

[0286] The user inputs the requirements of the project through the computing device. The input is in the form of text or voice, and an interface that is easy for the user to use is provided. As a specific example, the user inputs requirements such as "want to implement a user authentication function and a management dashboard" as the development requirements of a new web application. An example of the prompt text at this time is "Please analyze the project requirements and create an appropriate virtual prototype."

[0287] The terminal is responsible for sending the input requirement data to the server. In this case, the terminal converts the data into an appropriate format and sends it to the server using a communication protocol.

[0288] The server utilizes a generative artificial intelligence model to analyze the project requirements sent by the user. The generative AI model used here has been pre-trained with a large-scale dataset. Based on the analysis results, the server generates the basic data for generating a virtual environment, and this data is later used by the computing device.

[0289] The computing device uses the data sent from the server to generate a virtual space based on the project requirements, enabling the user to visually confirm the results of the project. This virtual space intuitively shows the UI prototype and function flow of the project, and presents the generated content in a form that allows the user to operate and confirm.

[0290] With the configuration and process of this system, the user can materialize the project requirements at the initial stage and it becomes possible to support efficient decision-making.

[0291] The flow of the specific process in Example 1 will be described using FIG. 11.

[0292] Step 1:

[0293] The user inputs project requirements via a computing device. Input can be in text or voice format. For example, the user might input, "The new web application requires user authentication functionality." The input data generates text or voice data as the requirement. This data is then prepared for transmission to the terminal.

[0294] Step 2:

[0295] The terminal converts the requirements data entered by the user into a format and sends it to the server. Specifically, it encodes text and audio data as needed and transfers them to the server using the appropriate communication protocol. The input is the requirements data received from the user, and the output is the formatted data sent to the server.

[0296] Step 3:

[0297] The server uses a generating AI model to analyze the requirements data received from the terminal. For example, if the prompt is "I would like to implement user authentication functionality and a management dashboard," the AI ​​model analyzes the requirements into a project structure and generates a list of necessary functions. The input is formatted requirements data sent from the terminal, and the output is the analyzed project structure information.

[0298] Step 4:

[0299] The server generates the foundational data for the virtual environment based on the analyzed structural information. This includes data that defines the project's UI prototype and functional flow. The input is the project structural information analyzed by the AI, and the output is the foundational data for generating the virtual environment.

[0300] Step 5:

[0301] The terminal uses the basic data sent from the server to generate and visualize the virtual environment of the project. As a specific operation, it uses a 3D modeling engine to visualize elements and provides an interactive experience for the user. The input is the basic data of the virtual environment sent from the server, and the output is a visual virtual space that the user can operate.

[0302] Step 6:

[0303] The server evaluates the generated virtual environment and assesses the feasibility of the project. This includes technical feasibility, resource efficiency, and an estimate of the project duration. The input is the data of the visualized virtual environment, and the output is the assessment result and analysis information.

[0304] Step 7:

[0305] The user checks the feedback and improvement points from the server through the terminal and modifies the project requirements. Re-enter the requirements as needed and re-run the analysis and virtualization process. The input is the feedback from the server, and the output is the revised project requirements.

[0306] This series of steps enables the system to clarify project requirements and efficiently evaluate feasibility.

[0307] (Application Example 1)

[0308] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0309] In a construction project, due to unclear requirements and insufficient assessment of feasibility in the design stage, there are problems such as plan changes and work delays. Also, it is difficult to intuitively confirm the plan on-site, and communication among stakeholders is insufficient, so the issue is that consensus formation is delayed.

[0310] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0311] In this invention, the server includes means for inputting project specifications, processing means using generative artificial intelligence to analyze the project specifications, means for constructing a three-dimensional virtual environment based on the analyzed project specifications and presenting it visually, and means for enabling the display and operation of the virtual environment on-site via a mobile terminal. This makes it possible to clarify project specifications at the design stage and to construct a highly feasible plan. Furthermore, it can facilitate rapid plan confirmation on-site and smooth communication among stakeholders.

[0312] "Project specifications" refer to the requirements and conditions related to the design and planning of a construction project, and detailed designs and work plans are formulated based on these specifications.

[0313] "Generative artificial intelligence" is an artificial intelligence technology that has the ability to learn from large amounts of data, analyze input information, and generate appropriate output.

[0314] A "three-dimensional virtual environment" is a three-dimensional space generated on a computer, used to visually simulate the completed form of a project.

[0315] A "mobile device" is a communication device that a user can carry with them, and includes hardware capable of accessing the internet and running applications.

[0316] The embodiments for carrying out the invention are as follows:

[0317] First, users input specifications for the construction project using mobile devices such as smartphones or tablets. These devices are provided with a user interface for text or voice input. The information entered by the user constitutes the "project specifications," which include the project requirements and conditions.

[0318] Next, the server uses generative artificial intelligence (generative AI model) to analyze the project specifications received from the user. The analysis utilizes a generative AI model trained on large-scale data (e.g., OpenAI GPT-4) to understand the structure and requirements of the input information. Based on these analysis results, the server generates the foundational data for constructing a three-dimensional virtual environment.

[0319] Subsequently, a three-dimensional virtual environment is constructed on the terminal and visually presented to the user. This allows the user to interactively check the completed form of the project. This virtual environment can be modified and adjusted in real time, allowing for a visual understanding of the design and construction plan of the construction project.

[0320] Furthermore, the server evaluates the project's technical feasibility and potential challenges based on the visualized virtual environment. This evaluation result is fed back to the user's mobile device as an analysis, facilitating the user to review and adjust the project specifications as needed.

[0321] As a concrete example, in a project where a user designs a new school building, they are prompted with the message, "Please enter the design requirements for the new school building. Please include the number of classrooms, facility layout, material selection, etc." and input the design requirements. In the generated 3D virtual environment, they can verify the suitability of the design and modify the requirements as needed. Through this process, the user can develop an appropriate plan while communicating smoothly with stakeholders.

[0322] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0323] Step 1:

[0324] The user enters project specifications using a mobile device. This input is done via text or voice. Specifically, the user enters design details into the device's interface in response to a prompt such as, "Please enter the design requirements for the new school building." This input data is sent to the server. The input is the project specification, and the output is the transmission of the specification data.

[0325] Step 2:

[0326] The server analyzes the received specification data using a generative AI model. Here, the generative AI model performs textual analysis of the project specifications and creates a data structure to understand the requirements. Specifically, the generative AI analyzes the input requirements and models the structure of the construction project. The input is the specification data, and the output is the data structure of the analysis result.

[0327] Step 3:

[0328] The server generates data for a three-dimensional virtual environment based on the data structure of the analysis results. Specifically, the server uses a 3D modeling tool to construct a virtual model of the construction project. This model is designed to allow the user to intuitively grasp the overall picture of the project. The input is the data structure of the analysis results, and the output is the virtual environment data.

[0329] Step 4:

[0330] The terminal receives virtual environment data sent from the server and presents it visually to the user. Specifically, a three-dimensional virtual environment is displayed on the terminal's screen, allowing the user to examine each part of the model in detail. The input is virtual environment data, and the output is visual feedback to the user.

[0331] Step 5:

[0332] Users interact with a three-dimensional virtual environment to verify and modify their designs. Specifically, users operate a terminal to click and drag elements within the model, and the results of these operations are displayed on the terminal. The input is the user's actions, and the output is the display of those results.

[0333] Step 6:

[0334] The server evaluates the technical feasibility and potential challenges of a project based on the results of operations from the terminal, and provides feedback to the user. Specifically, the server performs simulations, analyzes the evaluation results, and sends them to the user. The input is operation result data, and the output is an evaluation result report.

[0335] Step 7:

[0336] Based on the evaluation report received, the user reviews the project specifications again as needed and makes necessary corrections. Specifically, the user checks the evaluation report, re-enters the proposed revisions to the project specifications into the terminal, and the new specification data is sent to the server. The input is the specification revision based on the evaluation results, and the output is the refined specification data.

[0337] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0338] This invention is a system that efficiently visualizes project requirements and evaluates feasibility while considering user emotions. This system is mainly composed of a server, terminals, and an emotion engine.

[0339] Input and analysis of project requirements

[0340] The user begins by using their device to input project requirements via text or voice. For example, they can enter detailed development requirements for a new mobile application. The entered data is then sent from the device to the server.

[0341] The server uses generative artificial intelligence to analyze requirements based on user input. During the analysis process, data is abstracted and prepared for a virtual environment. This analysis helps understand the project structure and interface elements.

[0342] Virtual environment creation and visualization

[0343] Based on the analyzed data, the server generates a virtual environment. This includes the project design and a prototype of the operation flow. The terminal receives this and displays it to the user in an interactive format. This allows the user to gain a concrete understanding of the completed project and confirm the proposed specifications.

[0344] Application of the emotion engine

[0345] An emotion engine built into the device recognizes the user's emotions during visualization. This uses data collected through the camera and microphone. For example, the engine analyzes the user's facial expressions and vocal intonation, and records their emotional response to project requirements as feedback.

[0346] The server uses this sentiment data to dynamically adjust the user experience. For example, if a user expresses frustration, the system can improve usability by displaying an interface guide.

[0347] Feasibility assessment and proposal for improvement

[0348] The server evaluates the feasibility of the project based on a visualized virtual environment and sentiment data. This evaluation takes into account technical limitations, user feedback, and potential risks. The terminal presents these evaluation results to the user, clarifying the project's progress and areas for improvement.

[0349] By applying this system, users can gain support for clarifying requirements early in a project, improving the user experience with emotional considerations in mind, and making rapid decisions.

[0350] The following describes the processing flow.

[0351] Step 1:

[0352] The user enters project requirements using a terminal. The user describes detailed specifications and desired features in the interface via text message or voice input.

[0353] Step 2:

[0354] The terminal sends the entered requirement data to the server. This transmission is performed via a secure protocol, ensuring data integrity.

[0355] Step 3:

[0356] The server uses a generative artificial intelligence model to analyze the requirements data it receives. The server analyzes the text data of the requirements and identifies the core elements necessary for generating the virtual environment.

[0357] Step 4:

[0358] Based on the analysis results, the server generates a virtual environment. This environment includes the project's layout and interface design. The virtual environment data is then sent to the terminal.

[0359] Step 5:

[0360] The terminal visualizes and displays the virtual environment to the user. The user can interact with it, review the generated prototype, and evaluate its usability and visuals.

[0361] Step 6:

[0362] An emotion engine built into the device recognizes the user's emotions. Using the camera and microphone, it analyzes the user's facial expressions and voice tone in real time and sends the emotion data to a server.

[0363] Step 7:

[0364] The server evaluates the user experience based on emotional data and the virtual environment. The server analyzes the user's response to the project's feasibility and generates feedback.

[0365] Step 8:

[0366] The terminal displays evaluation results and feedback from the server to the user. Suggested improvements and technical adjustments are shown, allowing the user to modify their requirements based on this information.

[0367] Step 9:

[0368] The user modifies the project requirements based on feedback and re-enters them. The modified requirements are then analyzed again on the server, and a new virtual environment is generated.

[0369] (Example 2)

[0370] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0371] In the early stages of project development, it is crucial to efficiently visualize user requirements and enhance feasibility while considering emotional responses. However, conventional systems fail to adequately improve the user experience, leading to challenges such as misunderstandings of requirements and inefficient prototype creation. Furthermore, there is a lack of mechanisms for systems to dynamically utilize user emotional feedback.

[0372] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0373] In this invention, the server includes a device for inputting project requirements, a computing device using generative artificial intelligence for analyzing the project requirements, and a device for generating and visualizing a virtual environment based on the analyzed project requirements. This makes it possible to quickly generate a virtual prototype based on user requirements and dynamically improve usability by utilizing emotional feedback obtained by an emotion recognition engine.

[0374] "Project requirements" is a concept that refers to the specific requirements regarding the functions and specifications of a project.

[0375] "Device" refers to a mechanical or electronic mechanism designed to perform a specific function.

[0376] "Generative artificial intelligence" refers to an artificial intelligence model that learns on its own based on given data and has the ability to perform necessary analysis and predictions.

[0377] A "computational device" refers to a hardware or software system that receives input data and performs programmed arithmetic operations.

[0378] A "virtual environment" refers to a simulated or modeled environment created using computer technology, providing a domain where users can interact.

[0379] "Visualization" refers to the process of transforming abstract or complex data into a more easily understandable visual representation.

[0380] "Feasibility" refers to a measure of whether a particular plan or project can be carried out physically, technically, or economically.

[0381] "Analysis results" refer to conclusions or insights obtained by processing and analyzing data and information.

[0382] An "emotion recognition engine" refers to a system or software that analyzes voice and facial expression data to determine a user's emotional state.

[0383] "User experience" refers to the overall feeling or impression that a user experiences when using a system.

[0384] This invention is a system that efficiently visualizes project requirements and dynamically evaluates their feasibility while considering user emotions. This system is primarily implemented using a server, terminals, and an emotion recognition engine.

[0385] The user enters project requirements using a terminal. The terminal accepts input via text and voice, providing an interface that allows the user to freely express specific requirements, such as "develop customer management functionality for a new mobile application." The entered data is sent from the terminal to the server.

[0386] The server uses a generative AI model to analyze the received project requirements. This generative AI model uses a predefined prompt, "Analyze project requirements and specify the necessary components," to abstract the input requirements and extract the necessary interface elements. Based on the analyzed information, the server generates a virtual environment and visualizes the project's interface design and operation flow.

[0387] The generated virtual environment is sent back to the terminal and displayed interactively to the user. This allows the user to dynamically review the prototype of the proposed project and evaluate the specifications in detail.

[0388] Furthermore, the emotion recognition engine built into the device collects the user's emotions in real time through the camera and microphone. This analyzes the user's facial expressions and vocal intonation, and their emotional response to project requirements is recorded as feedback.

[0389] The server analyzes this collected emotional data and dynamically adjusts the user experience. For example, if a user shows a confused expression, the device can display additional guidance to improve usability. In this way, users can improve the UX by incorporating emotional feedback from the early stages of the project, enabling them to make quick and effective decisions.

[0390] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0391] Step 1:

[0392] The user enters project requirements through their device. The input is done via text or voice, and the device converts this into digital request data and sends it to the server. A specific example of input might be a requirement such as, "A new mobile application needs customer management functionality." The submitted data arrives at the server as information containing the basic project requirements.

[0393] Step 2:

[0394] The server launches a generative AI model to analyze the received requirements data. Here, the prompt "Analyze project requirements and specify the necessary components" is used to instruct the AI ​​to perform the analysis. The AI ​​model abstracts the requirements, computes interface and functional elements, and outputs the results as a series of datasets. These datasets contain the project's structural information.

[0395] Step 3:

[0396] Based on the analyzed data, the server generates a virtual environment. This virtual environment includes the project's design and operational flow. The server sends this information to the terminal, allowing the user to visually verify it. The output sent from the server is a digital model of the prototype.

[0397] Step 4:

[0398] The terminal processes the received virtual environment data and displays it to the user in an interactive format. This display allows the user to observe the concrete visuals of the project and evaluate whether it meets the requirements. The displayed output is a user-operable interface screen.

[0399] Step 5:

[0400] The emotion recognition engine built into the device collects user emotion data through the camera and microphone. The engine analyzes the user's facial expressions and voice tone to output an emotion state, which is then transmitted to a server in real time. This output includes emotional information such as the user's happiness or dissatisfaction.

[0401] Step 6:

[0402] The server uses the received sentiment data to dynamically adjust the user experience when necessary. For example, if the user is confused, the server sends a guide or tutorial screen to the device. This transmission outputs instructions for improving the interface, which are then displayed to the user on the device.

[0403] Step 7:

[0404] Finally, the server comprehensively evaluates the collected technical and emotional data to determine the project's feasibility. The resulting evaluation is sent to the terminal as a report, including the technical evaluation and user feedback, and presented to the user. This allows the user to specifically understand areas for improvement in the project and the next steps.

[0405] (Application Example 2)

[0406] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0407] Traditional project management systems rely heavily on human judgment for visualizing project requirements and evaluating feasibility, and because they fail to consider the user's feelings, improvements in the user experience are limited. Furthermore, they lack support for resolving users' potential anxieties and problems early on, beyond simply meeting requirements.

[0408] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0409] In this invention, the server includes a computation means using generative artificial intelligence to analyze project requirements, a means for generating and visualizing a virtual environment based on the analyzed requirements, and a means for analyzing user emotions and dynamically adjusting the experience. This allows for clarification of requirements through concrete visualization from the early stages of a project, and by adjusting responses based on user emotions, a better user experience can be provided, enabling faster decision-making.

[0410] "Project requirements" refer to the specific conditions and criteria necessary to achieve the project's objectives.

[0411] "Generative artificial intelligence" is an AI technology that can create new information based on input data and perform judgments and analyses.

[0412] A "virtual environment" refers to a space or situational simulation that provides a realistic experience generated on a computer.

[0413] "Visualization" is a technique that makes abstract data and information easier to understand by representing them graphically.

[0414] "Emotional data" refers to information about a user's feelings obtained and analyzed from their facial expressions and voice.

[0415] "Dynamic adjustment of user experience" refers to the immediate optimization of services and interfaces provided in response to user reactions and emotions.

[0416] This invention is a system primarily implemented using a server and a terminal. The server utilizes generative artificial intelligence to analyze project requirements input by the user. This analysis abstracts the project structure and generates the necessary virtual environment. The generated virtual environment includes the design and operation flow, and is visualized by the terminal. The user can interact with this virtual environment through the terminal.

[0417] The device incorporates an emotion engine that acquires user emotion data through the camera and microphone. This emotion data is obtained by analyzing the user's facial expressions and vocal intonation. Based on this emotion data, the server dynamically adjusts the user experience. Specifically, if the user shows signs of anxiety or confusion, the server improves the user experience by providing operational guides and navigation.

[0418] For example, if a user enters the requirements for a "simple cooking project at home," the server generates the project's steps and layout as a virtual environment, which is then visualized on the user's device. If the user experiences any anxiety while progressing through the cooking steps, the system will offer support, such as asking, "Do you have all the ingredients?"

[0419] An example of a prompt to the generating AI model would be: "Analyze the user's project requirements and generate a virtual environment in an easy-to-understand format. Based on sentiment data, suggest improvements to the project and enhance the user experience. The project is about 'simple cooking at home.' Engage in dialogue that responds to the user's emotions."

[0420] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0421] Step 1:

[0422] Users enter project requirements using a terminal. This input can be in text or voice format. The entered information is collected by the terminal and sent to the server. The input data includes specific project goals and conditions.

[0423] Step 2:

[0424] The server analyzes the received project requirements using generative artificial intelligence. This analysis process extracts the structural elements of the project from the input data and further abstracts related data. As a result, foundational data for generating the virtual environment is output.

[0425] Step 3:

[0426] Based on the analyzed project requirements, the server generates a virtual environment. This virtual environment includes the design and operational flow for a visual representation of the project. The generated data is sent to the terminal and displayed to the user, allowing them to concretely visualize the project concept.

[0427] Step 4:

[0428] The device uses a built-in emotion engine to collect user emotion data. This data is collected through the camera and microphone, and the user's facial expressions and voice intonation are analyzed. As a result of this emotion analysis, the user's emotional state is output.

[0429] Step 5:

[0430] The server uses emotional data to dynamically adjust the user experience. For example, if a user shows signs of anxiety, the server will instruct the device to provide operational guidance or suggest interactions. This prompting is done to improve the user experience.

[0431] Step 6:

[0432] The server evaluates the feasibility of the project based on the virtual environment and sentiment data. This evaluation process takes into account technical constraints and potential risks. The evaluation results are presented to the user as an analysis. This allows the user to identify areas for improvement in the project and make adjustments as needed.

[0433] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0434] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0435] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0436] [Third Embodiment]

[0437] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0438] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0439] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0440] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0441] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0442] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0443] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0444] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0445] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0446] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0447] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0448] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0449] This invention relates to a system for efficiently visualizing project requirements and evaluating feasibility. The system mainly consists of three main components: a server, a terminal, and a user.

[0450] User input of requirements

[0451] Users access an interface using their devices to input project requirements in text or voice. For example, if a user is requesting the development of a new web application, they can input or record the features and specifications on the device screen.

[0452] Server-based requirements analysis

[0453] When requirements are submitted from a terminal, the server uses generative artificial intelligence to analyze them. The generative AI model used here has been pre-trained on a large dataset and has the ability to understand what kind of project structure the provided requirements represent. Through this analysis, the server prepares the foundational data for building the virtual environment.

[0454] Virtual environment creation and visualization

[0455] Based on the analysis data obtained by the server, the terminal generates a virtual environment. This environment is provided interactively so that the user can visually grasp the completed form of the project. For example, the UI prototype and user flow of the requested web application are visualized.

[0456] Feasibility and challenge analysis

[0457] The server evaluates the feasibility of the project based on the visualized virtual environment. This evaluation includes technical feasibility, resource allocation efficiency, and timeline estimates. It also simultaneously detects potential problems and provides feedback to the user through the terminal.

[0458] Facilitating feedback and input of new requirements

[0459] Based on the feedback provided by the device, the user re-examines the requirements. Additions and modifications are made as needed, and the generating AI then re-analyzes the changes and outputs a new virtual environment, continuously adjusting the requirements.

[0460] This process allows users to clarify ambiguous requirements in the early stages of a project, facilitate smooth agreement among stakeholders, and support rapid decision-making.

[0461] The following describes the processing flow.

[0462] Step 1:

[0463] Users enter project requirements on their devices. Input can be done via text or voice, and detailed specifications and desired conditions can be included through the interface.

[0464] Step 2:

[0465] The terminal sends the entered requirement data to the server. The transmitted data is securely transferred to the server using encryption technology.

[0466] Step 3:

[0467] The server analyzes the requirements data received from the terminal. Using a generative artificial intelligence model, the server extracts the structure and related information of the requirements and generates the basic data for the virtual environment.

[0468] Step 4:

[0469] The server builds a virtual environment based on the analysis results. This virtual environment includes a prototype based on the project's final structure and design.

[0470] Step 5:

[0471] The terminal receives virtual environment data from the server and visualizes it for the user. The visualization is in an interactive format, allowing the user to grasp a concrete image of the project.

[0472] Step 6:

[0473] The server evaluates the visualized prototype and analyzes its feasibility. This includes technical feasibility, resource optimization, and risk assessment.

[0474] Step 7:

[0475] The terminal receives analysis results from the server and displays them to the user. These results include potential problems and suggestions for improvement in the project.

[0476] Step 8:

[0477] Based on feedback from the user's device, they re-enter and modify their requirements as needed. The modified requirements are then analyzed again on the server, and a new virtual environment is generated.

[0478] (Example 1)

[0479] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0480] In modern project development, unclear requirements and insufficient feasibility assessments are common challenges that hinder the efficient progress of project planning. In particular, unclear requirements in the early stages of a project lead to wasted time and resources and make consensus building among stakeholders difficult. Therefore, a system is needed to clarify requirements and quickly and accurately assess feasibility.

[0481] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0482] In this invention, the server includes means for a user to input project requirements via a computing device, information processing means using generative artificial intelligence to analyze the project requirements, and means for a computer to generate and display a virtual space based on the analyzed project requirements. This enables the user to clarify requirements in the early stages of a project and supports smooth decision-making among stakeholders.

[0483] A "user" refers to an individual or organization that operates the system and inputs project requirements.

[0484] "Calculation unit" refers to electronic devices such as computers and smartphones, which are used by users to input project requirements.

[0485] "Project requirements" refer to detailed specifications regarding the functions, performance, and design necessary to carry out the project.

[0486] "Generative artificial intelligence" refers to algorithms that learn from large datasets and have the ability to generate and analyze information for specific tasks, much like a human would.

[0487] "Information processing means" refers to a system that uses computer software and hardware to analyze, process, and store data.

[0488] A "virtual space" is a computer-generated simulation environment that allows users to visually check the results of a project.

[0489] "Means of display" refers to methods of providing the generated virtual space or analysis results to the user through a screen or display.

[0490] "Feasibility" is an indicator used to evaluate whether a planned project is technically, economically, and temporally feasible.

[0491] "Assessment" refers to the act of evaluating or estimating, and is used to determine the feasibility of a project.

[0492] This invention is a system for clarifying project requirements and quickly evaluating feasibility. The system mainly consists of three components: user, terminal, and server.

[0493] The user inputs project requirements via a computing device. Input is in text or voice format, and a user-friendly interface is provided. For example, a user might input requirements for a new web application development, such as "We would like to implement user authentication and an administration dashboard." An example of a prompt in this case might be, "Analyze the project requirements and create an appropriate virtual prototype."

[0494] The terminal is responsible for sending the entered requirements data to the server. In this case, the terminal converts the data into the appropriate format and sends it to the server using a communication protocol.

[0495] The server utilizes a generative artificial intelligence model to analyze project requirements submitted by the user. This generative AI model is pre-trained on a large dataset. Based on the analysis results, the server generates foundational data for creating a virtual environment, which is later used by the computing system.

[0496] The computer uses data transmitted from the server to generate a virtual space based on project requirements, allowing users to visually verify the project's results. This virtual space intuitively represents the project's UI prototype and functional flow, presenting the generated content in a way that users can interact with and verify.

[0497] This system's configuration and process enable users to define project requirements in the early stages and support efficient decision-making.

[0498] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0499] Step 1:

[0500] The user inputs project requirements via a computing device. Input can be in text or voice format. For example, the user might input, "The new web application requires user authentication functionality." The input data generates text or voice data as the requirement. This data is then prepared for transmission to the terminal.

[0501] Step 2:

[0502] The terminal converts the requirements data entered by the user into a format and sends it to the server. Specifically, it encodes text and audio data as needed and transfers them to the server using the appropriate communication protocol. The input is the requirements data received from the user, and the output is the formatted data sent to the server.

[0503] Step 3:

[0504] The server uses a generating AI model to analyze the requirements data received from the terminal. For example, if the prompt is "I would like to implement user authentication functionality and a management dashboard," the AI ​​model analyzes the requirements into a project structure and generates a list of necessary functions. The input is formatted requirements data sent from the terminal, and the output is the analyzed project structure information.

[0505] Step 4:

[0506] The server generates the foundational data for the virtual environment based on the analyzed structural information. This includes data that defines the project's UI prototype and functional flow. The input is the project structural information analyzed by the AI, and the output is the foundational data for generating the virtual environment.

[0507] Step 5:

[0508] The terminal uses the underlying data sent from the server to generate and visualize the project's virtual environment. Specifically, it uses a 3D modeling engine to visualize elements and provides the user with an interactive experience. The input is the underlying data of the virtual environment sent from the server, and the output is a user-manipulable visual virtual space.

[0509] Step 6:

[0510] The server evaluates the generated virtual environment and assesses the feasibility of the project. This includes technical feasibility, resource efficiency, and project duration estimates. The input is data from the visualized virtual environment, and the output is the assessment results and analytical information.

[0511] Step 7:

[0512] The user reviews feedback and suggestions for improvement from the server via their terminal and modifies the project requirements. If necessary, they re-enter the requirements and run the analysis and virtualization process again. The input is the feedback from the server, and the output is the revised project requirements.

[0513] This series of steps enables the system to efficiently clarify project requirements and assess feasibility.

[0514] (Application Example 1)

[0515] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0516] In construction projects, unclear requirements and insufficient feasibility assessments during the design phase can lead to plan changes and work delays. Furthermore, the difficulty in intuitively verifying plans on-site and inadequate communication among stakeholders can hinder consensus building, posing a significant challenge.

[0517] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0518] In this invention, the server includes means for inputting project specifications, processing means using generative artificial intelligence to analyze the project specifications, means for constructing a three-dimensional virtual environment based on the analyzed project specifications and presenting it visually, and means for enabling the display and operation of the virtual environment on-site via a mobile terminal. This makes it possible to clarify project specifications at the design stage and to construct a highly feasible plan. Furthermore, it can facilitate rapid plan confirmation on-site and smooth communication among stakeholders.

[0519] "Project specifications" refer to the requirements and conditions related to the design and planning of a construction project, and detailed designs and work plans are formulated based on these specifications.

[0520] "Generative artificial intelligence" is an artificial intelligence technology that has the ability to learn from large amounts of data, analyze input information, and generate appropriate output.

[0521] A "three-dimensional virtual environment" is a three-dimensional space generated on a computer, used to visually simulate the completed form of a project.

[0522] A "mobile device" is a communication device that a user can carry with them, and includes hardware capable of accessing the internet and running applications.

[0523] The embodiments for carrying out the invention are as follows:

[0524] First, users input specifications for the construction project using mobile devices such as smartphones or tablets. These devices are provided with a user interface for text or voice input. The information entered by the user constitutes the "project specifications," which include the project requirements and conditions.

[0525] Next, the server uses generative artificial intelligence (generative AI model) to analyze the project specifications received from the user. The analysis utilizes a generative AI model trained on large-scale data (e.g., OpenAI GPT-4) to understand the structure and requirements of the input information. Based on these analysis results, the server generates the foundational data for constructing a three-dimensional virtual environment.

[0526] Subsequently, a three-dimensional virtual environment is constructed on the terminal and visually presented to the user. This allows the user to interactively check the completed form of the project. This virtual environment can be modified and adjusted in real time, allowing for a visual understanding of the design and construction plan of the construction project.

[0527] Furthermore, the server evaluates the project's technical feasibility and potential challenges based on the visualized virtual environment. This evaluation result is fed back to the user's mobile device as an analysis, facilitating the user to review and adjust the project specifications as needed.

[0528] As a concrete example, in a project where a user designs a new school building, they are prompted with the message, "Please enter the design requirements for the new school building. Please include the number of classrooms, facility layout, material selection, etc." and input the design requirements. In the generated 3D virtual environment, they can verify the suitability of the design and modify the requirements as needed. Through this process, the user can develop an appropriate plan while communicating smoothly with stakeholders.

[0529] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0530] Step 1:

[0531] The user enters project specifications using a mobile device. This input is done via text or voice. Specifically, the user enters design details into the device's interface in response to a prompt such as, "Please enter the design requirements for the new school building." This input data is sent to the server. The input is the project specification, and the output is the transmission of the specification data.

[0532] Step 2:

[0533] The server analyzes the received specification data using a generative AI model. Here, the generative AI model performs textual analysis of the project specifications and creates a data structure to understand the requirements. Specifically, the generative AI analyzes the input requirements and models the structure of the construction project. The input is the specification data, and the output is the data structure of the analysis result.

[0534] Step 3:

[0535] The server generates data for a three-dimensional virtual environment based on the data structure of the analysis results. Specifically, the server uses a 3D modeling tool to construct a virtual model of the construction project. This model is designed to allow the user to intuitively grasp the overall picture of the project. The input is the data structure of the analysis results, and the output is the virtual environment data.

[0536] Step 4:

[0537] The terminal receives virtual environment data sent from the server and presents it visually to the user. Specifically, a three-dimensional virtual environment is displayed on the terminal's screen, allowing the user to examine each part of the model in detail. The input is virtual environment data, and the output is visual feedback to the user.

[0538] Step 5:

[0539] Users interact with a three-dimensional virtual environment to verify and modify their designs. Specifically, users operate a terminal to click and drag elements within the model, and the results of these operations are displayed on the terminal. The input is the user's actions, and the output is the display of those results.

[0540] Step 6:

[0541] The server evaluates the technical feasibility and potential challenges of a project based on the results of operations from the terminal, and provides feedback to the user. Specifically, the server performs simulations, analyzes the evaluation results, and sends them to the user. The input is operation result data, and the output is an evaluation result report.

[0542] Step 7:

[0543] Based on the evaluation report received, the user reviews the project specifications again as needed and makes necessary corrections. Specifically, the user checks the evaluation report, re-enters the proposed revisions to the project specifications into the terminal, and the new specification data is sent to the server. The input is the specification revision based on the evaluation results, and the output is the refined specification data.

[0544] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0545] This invention is a system that efficiently visualizes project requirements and evaluates feasibility while considering user emotions. This system is mainly composed of a server, terminals, and an emotion engine.

[0546] Input and analysis of project requirements

[0547] The user begins by using their device to input project requirements via text or voice. For example, they can enter detailed development requirements for a new mobile application. The entered data is then sent from the device to the server.

[0548] The server uses generative artificial intelligence to analyze requirements based on user input. During the analysis process, data is abstracted and prepared for a virtual environment. This analysis helps understand the project structure and interface elements.

[0549] Virtual environment creation and visualization

[0550] Based on the analyzed data, the server generates a virtual environment. This includes the project design and a prototype of the operation flow. The terminal receives this and displays it to the user in an interactive format. This allows the user to gain a concrete understanding of the completed project and confirm the proposed specifications.

[0551] Application of the emotion engine

[0552] An emotion engine built into the device recognizes the user's emotions during visualization. This uses data collected through the camera and microphone. For example, the engine analyzes the user's facial expressions and vocal intonation, and records their emotional response to project requirements as feedback.

[0553] The server uses this sentiment data to dynamically adjust the user experience. For example, if a user expresses frustration, the system can improve usability by displaying an interface guide.

[0554] Feasibility assessment and proposal for improvement

[0555] The server evaluates the feasibility of the project based on a visualized virtual environment and sentiment data. This evaluation takes into account technical limitations, user feedback, and potential risks. The terminal presents these evaluation results to the user, clarifying the project's progress and areas for improvement.

[0556] By applying this system, users can gain support for clarifying requirements early in a project, improving the user experience with emotional considerations in mind, and making rapid decisions.

[0557] The following describes the processing flow.

[0558] Step 1:

[0559] The user enters project requirements using a terminal. The user describes detailed specifications and desired features in the interface via text message or voice input.

[0560] Step 2:

[0561] The terminal sends the entered requirement data to the server. This transmission is performed via a secure protocol, ensuring data integrity.

[0562] Step 3:

[0563] The server uses a generative artificial intelligence model to analyze the requirements data it receives. The server analyzes the text data of the requirements and identifies the core elements necessary for generating the virtual environment.

[0564] Step 4:

[0565] Based on the analysis results, the server generates a virtual environment. This environment includes the project's layout and interface design. The virtual environment data is then sent to the terminal.

[0566] Step 5:

[0567] The terminal visualizes and displays the virtual environment to the user. The user can interact with it, review the generated prototype, and evaluate its usability and visuals.

[0568] Step 6:

[0569] An emotion engine built into the device recognizes the user's emotions. Using the camera and microphone, it analyzes the user's facial expressions and voice tone in real time and sends the emotion data to a server.

[0570] Step 7:

[0571] The server evaluates the user experience based on emotional data and the virtual environment. The server analyzes the user's response to the project's feasibility and generates feedback.

[0572] Step 8:

[0573] The terminal displays evaluation results and feedback from the server to the user. Suggested improvements and technical adjustments are shown, allowing the user to modify their requirements based on this information.

[0574] Step 9:

[0575] The user modifies the project requirements based on feedback and re-enters them. The modified requirements are then analyzed again on the server, and a new virtual environment is generated.

[0576] (Example 2)

[0577] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0578] In the early stages of project development, it is crucial to efficiently visualize user requirements and enhance feasibility while considering emotional responses. However, conventional systems fail to adequately improve the user experience, leading to challenges such as misunderstandings of requirements and inefficient prototype creation. Furthermore, there is a lack of mechanisms for systems to dynamically utilize user emotional feedback.

[0579] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0580] In this invention, the server includes a device for inputting project requirements, a computing device using generative artificial intelligence for analyzing the project requirements, and a device for generating and visualizing a virtual environment based on the analyzed project requirements. This makes it possible to quickly generate a virtual prototype based on user requirements and dynamically improve usability by utilizing emotional feedback obtained by an emotion recognition engine.

[0581] "Project requirements" is a concept that refers to the specific requirements regarding the functions and specifications of a project.

[0582] "Device" refers to a mechanical or electronic mechanism designed to perform a specific function.

[0583] "Generative artificial intelligence" refers to an artificial intelligence model that learns on its own based on given data and has the ability to perform necessary analysis and predictions.

[0584] A "computational device" refers to a hardware or software system that receives input data and performs programmed arithmetic operations.

[0585] A "virtual environment" refers to a simulated or modeled environment created using computer technology, providing a domain where users can interact.

[0586] "Visualization" refers to the process of transforming abstract or complex data into a more easily understandable visual representation.

[0587] "Feasibility" refers to a measure of whether a particular plan or project can be carried out physically, technically, or economically.

[0588] "Analysis results" refer to conclusions or insights obtained by processing and analyzing data and information.

[0589] An "emotion recognition engine" refers to a system or software that analyzes voice and facial expression data to determine a user's emotional state.

[0590] "User experience" refers to the overall feeling or impression that a user experiences when using a system.

[0591] This invention is a system that efficiently visualizes project requirements and dynamically evaluates their feasibility while considering user emotions. This system is primarily implemented using a server, terminals, and an emotion recognition engine.

[0592] The user enters project requirements using a terminal. The terminal accepts input via text and voice, providing an interface that allows the user to freely express specific requirements, such as "develop customer management functionality for a new mobile application." The entered data is sent from the terminal to the server.

[0593] The server uses a generative AI model to analyze the received project requirements. This generative AI model uses a predefined prompt, "Analyze project requirements and specify the necessary components," to abstract the input requirements and extract the necessary interface elements. Based on the analyzed information, the server generates a virtual environment and visualizes the project's interface design and operation flow.

[0594] The generated virtual environment is sent back to the terminal and displayed interactively to the user. This allows the user to dynamically review the prototype of the proposed project and evaluate the specifications in detail.

[0595] Furthermore, the emotion recognition engine built into the device collects the user's emotions in real time through the camera and microphone. This analyzes the user's facial expressions and vocal intonation, and their emotional response to project requirements is recorded as feedback.

[0596] The server analyzes this collected emotional data and dynamically adjusts the user experience. For example, if a user shows a confused expression, the device can display additional guidance to improve usability. In this way, users can improve the UX by incorporating emotional feedback from the early stages of the project, enabling them to make quick and effective decisions.

[0597] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0598] Step 1:

[0599] The user enters project requirements through their device. The input is done via text or voice, and the device converts this into digital request data and sends it to the server. A specific example of input might be a requirement such as, "A new mobile application needs customer management functionality." The submitted data arrives at the server as information containing the basic project requirements.

[0600] Step 2:

[0601] The server launches a generative AI model to analyze the received requirements data. Here, the prompt "Analyze project requirements and specify the necessary components" is used to instruct the AI ​​to perform the analysis. The AI ​​model abstracts the requirements, computes interface and functional elements, and outputs the results as a series of datasets. These datasets contain the project's structural information.

[0602] Step 3:

[0603] Based on the analyzed data, the server generates a virtual environment. This virtual environment includes the project's design and operational flow. The server sends this information to the terminal, allowing the user to visually verify it. The output sent from the server is a digital model of the prototype.

[0604] Step 4:

[0605] The terminal processes the received virtual environment data and displays it to the user in an interactive format. This display allows the user to observe the concrete visuals of the project and evaluate whether it meets the requirements. The displayed output is a user-operable interface screen.

[0606] Step 5:

[0607] The emotion recognition engine built into the device collects user emotion data through the camera and microphone. The engine analyzes the user's facial expressions and voice tone to output an emotion state, which is then transmitted to a server in real time. This output includes emotional information such as the user's happiness or dissatisfaction.

[0608] Step 6:

[0609] The server uses the received sentiment data to dynamically adjust the user experience when necessary. For example, if the user is confused, the server sends a guide or tutorial screen to the device. This transmission outputs instructions for improving the interface, which are then displayed to the user on the device.

[0610] Step 7:

[0611] Finally, the server comprehensively evaluates the collected technical and emotional data to determine the project's feasibility. The resulting evaluation is sent to the terminal as a report, including the technical evaluation and user feedback, and presented to the user. This allows the user to specifically understand areas for improvement in the project and the next steps.

[0612] (Application Example 2)

[0613] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0614] Traditional project management systems rely heavily on human judgment for visualizing project requirements and evaluating feasibility, and because they fail to consider the user's feelings, improvements in the user experience are limited. Furthermore, they lack support for resolving users' potential anxieties and problems early on, beyond simply meeting requirements.

[0615] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0616] In this invention, the server includes a computation means using generative artificial intelligence to analyze project requirements, a means for generating and visualizing a virtual environment based on the analyzed requirements, and a means for analyzing user emotions and dynamically adjusting the experience. This allows for clarification of requirements through concrete visualization from the early stages of a project, and by adjusting responses based on user emotions, a better user experience can be provided, enabling faster decision-making.

[0617] "Project requirements" refer to the specific conditions and criteria necessary to achieve the project's objectives.

[0618] "Generative artificial intelligence" is an AI technology that can create new information based on input data and perform judgments and analyses.

[0619] A "virtual environment" refers to a space or situational simulation that provides a realistic experience generated on a computer.

[0620] "Visualization" is a technique that makes abstract data and information easier to understand by representing them graphically.

[0621] "Emotional data" refers to information about a user's feelings obtained and analyzed from their facial expressions and voice.

[0622] "Dynamic adjustment of user experience" refers to the immediate optimization of services and interfaces provided in response to user reactions and emotions.

[0623] This invention is a system primarily implemented using a server and a terminal. The server utilizes generative artificial intelligence to analyze project requirements input by the user. This analysis abstracts the project structure and generates the necessary virtual environment. The generated virtual environment includes the design and operation flow, and is visualized by the terminal. The user can interact with this virtual environment through the terminal.

[0624] The device incorporates an emotion engine that acquires user emotion data through the camera and microphone. This emotion data is obtained by analyzing the user's facial expressions and vocal intonation. Based on this emotion data, the server dynamically adjusts the user experience. Specifically, if the user shows signs of anxiety or confusion, the server improves the user experience by providing operational guides and navigation.

[0625] For example, if a user enters the requirements for a "simple cooking project at home," the server generates the project's steps and layout as a virtual environment, which is then visualized on the user's device. If the user experiences any anxiety while progressing through the cooking steps, the system will offer support, such as asking, "Do you have all the ingredients?"

[0626] An example of a prompt to the generating AI model would be: "Analyze the user's project requirements and generate a virtual environment in an easy-to-understand format. Based on sentiment data, suggest improvements to the project and enhance the user experience. The project is about 'simple cooking at home.' Engage in dialogue that responds to the user's emotions."

[0627] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0628] Step 1:

[0629] Users enter project requirements using a terminal. This input can be in text or voice format. The entered information is collected by the terminal and sent to the server. The input data includes specific project goals and conditions.

[0630] Step 2:

[0631] The server analyzes the received project requirements using generative artificial intelligence. This analysis process extracts the structural elements of the project from the input data and further abstracts related data. As a result, foundational data for generating the virtual environment is output.

[0632] Step 3:

[0633] Based on the analyzed project requirements, the server generates a virtual environment. This virtual environment includes the design and operational flow for a visual representation of the project. The generated data is sent to the terminal and displayed to the user, allowing them to concretely visualize the project concept.

[0634] Step 4:

[0635] The device uses a built-in emotion engine to collect user emotion data. This data is collected through the camera and microphone, and the user's facial expressions and voice intonation are analyzed. As a result of this emotion analysis, the user's emotional state is output.

[0636] Step 5:

[0637] The server uses emotional data to dynamically adjust the user experience. For example, if a user shows signs of anxiety, the server will instruct the device to provide operational guidance or suggest interactions. This prompting is done to improve the user experience.

[0638] Step 6:

[0639] The server evaluates the feasibility of the project based on the virtual environment and sentiment data. This evaluation process takes into account technical constraints and potential risks. The evaluation results are presented to the user as an analysis. This allows the user to identify areas for improvement in the project and make adjustments as needed.

[0640] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0641] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0642] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0643] [Fourth Embodiment]

[0644] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0645] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0646] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0647] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0648] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0649] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0650] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0651] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0652] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0653] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0654] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0655] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0656] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0657] This invention relates to a system for efficiently visualizing project requirements and evaluating feasibility. The system mainly consists of three main components: a server, a terminal, and a user.

[0658] User input of requirements

[0659] Users access an interface using their devices to input project requirements in text or voice. For example, if a user is requesting the development of a new web application, they can input or record the features and specifications on the device screen.

[0660] Server-based requirements analysis

[0661] When requirements are submitted from a terminal, the server uses generative artificial intelligence to analyze them. The generative AI model used here has been pre-trained on a large dataset and has the ability to understand what kind of project structure the provided requirements represent. Through this analysis, the server prepares the foundational data for building the virtual environment.

[0662] Virtual environment creation and visualization

[0663] Based on the analysis data obtained by the server, the terminal generates a virtual environment. This environment is provided interactively so that the user can visually grasp the completed form of the project. For example, the UI prototype and user flow of the requested web application are visualized.

[0664] Feasibility and challenge analysis

[0665] The server evaluates the feasibility of the project based on the visualized virtual environment. This evaluation includes technical feasibility, resource allocation efficiency, and timeline estimates. It also simultaneously detects potential problems and provides feedback to the user through the terminal.

[0666] Facilitating feedback and input of new requirements

[0667] Based on the feedback provided by the device, the user re-examines the requirements. Additions and modifications are made as needed, and the generating AI then re-analyzes the changes and outputs a new virtual environment, continuously adjusting the requirements.

[0668] This process allows users to clarify ambiguous requirements in the early stages of a project, facilitate smooth agreement among stakeholders, and support rapid decision-making.

[0669] The following describes the processing flow.

[0670] Step 1:

[0671] Users enter project requirements on their devices. Input can be done via text or voice, and detailed specifications and desired conditions can be included through the interface.

[0672] Step 2:

[0673] The terminal sends the entered requirement data to the server. The transmitted data is securely transferred to the server using encryption technology.

[0674] Step 3:

[0675] The server analyzes the requirements data received from the terminal. Using a generative artificial intelligence model, the server extracts the structure and related information of the requirements and generates the basic data for the virtual environment.

[0676] Step 4:

[0677] The server builds a virtual environment based on the analysis results. This virtual environment includes a prototype based on the project's final structure and design.

[0678] Step 5:

[0679] The terminal receives virtual environment data from the server and visualizes it for the user. The visualization is in an interactive format, allowing the user to grasp a concrete image of the project.

[0680] Step 6:

[0681] The server evaluates the visualized prototype and analyzes its feasibility. This includes technical feasibility, resource optimization, and risk assessment.

[0682] Step 7:

[0683] The terminal receives analysis results from the server and displays them to the user. These results include potential problems and suggestions for improvement in the project.

[0684] Step 8:

[0685] Based on feedback from the user's device, they re-enter and modify their requirements as needed. The modified requirements are then analyzed again on the server, and a new virtual environment is generated.

[0686] (Example 1)

[0687] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0688] In modern project development, unclear requirements and insufficient feasibility assessments are common challenges that hinder the efficient progress of project planning. In particular, unclear requirements in the early stages of a project lead to wasted time and resources and make consensus building among stakeholders difficult. Therefore, a system is needed to clarify requirements and quickly and accurately assess feasibility.

[0689] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0690] In this invention, the server includes means for a user to input project requirements via a computing device, information processing means using generative artificial intelligence to analyze the project requirements, and means for a computer to generate and display a virtual space based on the analyzed project requirements. This enables the user to clarify requirements in the early stages of a project and supports smooth decision-making among stakeholders.

[0691] A "user" refers to an individual or organization that operates the system and inputs project requirements.

[0692] "Calculation unit" refers to electronic devices such as computers and smartphones, which are used by users to input project requirements.

[0693] "Project requirements" refer to detailed specifications regarding the functions, performance, and design necessary to carry out the project.

[0694] "Generative artificial intelligence" refers to algorithms that learn from large datasets and have the ability to generate and analyze information for specific tasks, much like a human would.

[0695] "Information processing means" refers to a system that uses computer software and hardware to analyze, process, and store data.

[0696] A "virtual space" is a computer-generated simulation environment that allows users to visually check the results of a project.

[0697] "Means of display" refers to methods of providing the generated virtual space or analysis results to the user through a screen or display.

[0698] "Feasibility" is an indicator used to evaluate whether a planned project is technically, economically, and temporally feasible.

[0699] "Assessment" refers to the act of evaluating or estimating, and is used to determine the feasibility of a project.

[0700] This invention is a system for clarifying project requirements and quickly evaluating feasibility. The system mainly consists of three components: user, terminal, and server.

[0701] The user inputs project requirements via a computing device. Input is in text or voice format, and a user-friendly interface is provided. For example, a user might input requirements for a new web application development, such as "We would like to implement user authentication and an administration dashboard." An example of a prompt in this case might be, "Analyze the project requirements and create an appropriate virtual prototype."

[0702] The terminal is responsible for sending the entered requirements data to the server. In this case, the terminal converts the data into the appropriate format and sends it to the server using a communication protocol.

[0703] The server utilizes a generative artificial intelligence model to analyze project requirements submitted by the user. This generative AI model is pre-trained on a large dataset. Based on the analysis results, the server generates foundational data for creating a virtual environment, which is later used by the computing system.

[0704] The computer uses data transmitted from the server to generate a virtual space based on project requirements, allowing users to visually verify the project's results. This virtual space intuitively represents the project's UI prototype and functional flow, presenting the generated content in a way that users can interact with and verify.

[0705] This system's configuration and process enable users to define project requirements in the early stages and support efficient decision-making.

[0706] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0707] Step 1:

[0708] The user inputs project requirements via a computing device. Input can be in text or voice format. For example, the user might input, "The new web application requires user authentication functionality." The input data generates text or voice data as the requirement. This data is then prepared for transmission to the terminal.

[0709] Step 2:

[0710] The terminal converts the requirements data entered by the user into a format and sends it to the server. Specifically, it encodes text and audio data as needed and transfers them to the server using the appropriate communication protocol. The input is the requirements data received from the user, and the output is the formatted data sent to the server.

[0711] Step 3:

[0712] The server uses a generating AI model to analyze the requirements data received from the terminal. For example, if the prompt is "I would like to implement user authentication functionality and a management dashboard," the AI ​​model analyzes the requirements into a project structure and generates a list of necessary functions. The input is formatted requirements data sent from the terminal, and the output is the analyzed project structure information.

[0713] Step 4:

[0714] The server generates the foundational data for the virtual environment based on the analyzed structural information. This includes data that defines the project's UI prototype and functional flow. The input is the project structural information analyzed by the AI, and the output is the foundational data for generating the virtual environment.

[0715] Step 5:

[0716] The terminal uses the underlying data sent from the server to generate and visualize the project's virtual environment. Specifically, it uses a 3D modeling engine to visualize elements and provides the user with an interactive experience. The input is the underlying data of the virtual environment sent from the server, and the output is a user-manipulable visual virtual space.

[0717] Step 6:

[0718] The server evaluates the generated virtual environment and assesses the feasibility of the project. This includes technical feasibility, resource efficiency, and project duration estimates. The input is data from the visualized virtual environment, and the output is the assessment results and analytical information.

[0719] Step 7:

[0720] The user reviews feedback and suggestions for improvement from the server via their terminal and modifies the project requirements. If necessary, they re-enter the requirements and run the analysis and virtualization process again. The input is the feedback from the server, and the output is the revised project requirements.

[0721] This series of steps enables the system to efficiently clarify project requirements and assess feasibility.

[0722] (Application Example 1)

[0723] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0724] In construction projects, unclear requirements and insufficient feasibility assessments during the design phase can lead to plan changes and work delays. Furthermore, the difficulty in intuitively verifying plans on-site and inadequate communication among stakeholders can hinder consensus building, posing a significant challenge.

[0725] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0726] In this invention, the server includes means for inputting project specifications, processing means using generative artificial intelligence to analyze the project specifications, means for constructing a three-dimensional virtual environment based on the analyzed project specifications and presenting it visually, and means for enabling the display and operation of the virtual environment on-site via a mobile terminal. This makes it possible to clarify project specifications at the design stage and to construct a highly feasible plan. Furthermore, it can facilitate rapid plan confirmation on-site and smooth communication among stakeholders.

[0727] "Project specifications" refer to the requirements and conditions related to the design and planning of a construction project, and detailed designs and work plans are formulated based on these specifications.

[0728] "Generative artificial intelligence" is an artificial intelligence technology that has the ability to learn from large amounts of data, analyze input information, and generate appropriate output.

[0729] A "three-dimensional virtual environment" is a three-dimensional space generated on a computer, used to visually simulate the completed form of a project.

[0730] A "mobile device" is a communication device that a user can carry with them, and includes hardware capable of accessing the internet and running applications.

[0731] The embodiments for carrying out the invention are as follows:

[0732] First, users input specifications for the construction project using mobile devices such as smartphones or tablets. These devices are provided with a user interface for text or voice input. The information entered by the user constitutes the "project specifications," which include the project requirements and conditions.

[0733] Next, the server uses generative artificial intelligence (generative AI model) to analyze the project specifications received from the user. The analysis utilizes a generative AI model trained on large-scale data (e.g., OpenAI GPT-4) to understand the structure and requirements of the input information. Based on these analysis results, the server generates the foundational data for constructing a three-dimensional virtual environment.

[0734] Subsequently, a three-dimensional virtual environment is constructed on the terminal and visually presented to the user. This allows the user to interactively check the completed form of the project. This virtual environment can be modified and adjusted in real time, allowing for a visual understanding of the design and construction plan of the construction project.

[0735] Furthermore, the server evaluates the project's technical feasibility and potential challenges based on the visualized virtual environment. This evaluation result is fed back to the user's mobile device as an analysis, facilitating the user to review and adjust the project specifications as needed.

[0736] As a concrete example, in a project where a user designs a new school building, they are prompted with the message, "Please enter the design requirements for the new school building. Please include the number of classrooms, facility layout, material selection, etc." and input the design requirements. In the generated 3D virtual environment, they can verify the suitability of the design and modify the requirements as needed. Through this process, the user can develop an appropriate plan while communicating smoothly with stakeholders.

[0737] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0738] Step 1:

[0739] The user enters project specifications using a mobile device. This input is done via text or voice. Specifically, the user enters design details into the device's interface in response to a prompt such as, "Please enter the design requirements for the new school building." This input data is sent to the server. The input is the project specification, and the output is the transmission of the specification data.

[0740] Step 2:

[0741] The server analyzes the received specification data using a generative AI model. Here, the generative AI model performs textual analysis of the project specifications and creates a data structure to understand the requirements. Specifically, the generative AI analyzes the input requirements and models the structure of the construction project. The input is the specification data, and the output is the data structure of the analysis result.

[0742] Step 3:

[0743] The server generates data for a three-dimensional virtual environment based on the data structure of the analysis results. Specifically, the server uses a 3D modeling tool to construct a virtual model of the construction project. This model is designed to allow the user to intuitively grasp the overall picture of the project. The input is the data structure of the analysis results, and the output is the virtual environment data.

[0744] Step 4:

[0745] The terminal receives virtual environment data sent from the server and presents it visually to the user. Specifically, a three-dimensional virtual environment is displayed on the terminal's screen, allowing the user to examine each part of the model in detail. The input is virtual environment data, and the output is visual feedback to the user.

[0746] Step 5:

[0747] Users interact with a three-dimensional virtual environment to verify and modify their designs. Specifically, users operate a terminal to click and drag elements within the model, and the results of these operations are displayed on the terminal. The input is the user's actions, and the output is the display of those results.

[0748] Step 6:

[0749] The server evaluates the technical feasibility and potential challenges of a project based on the results of operations from the terminal, and provides feedback to the user. Specifically, the server performs simulations, analyzes the evaluation results, and sends them to the user. The input is operation result data, and the output is an evaluation result report.

[0750] Step 7:

[0751] Based on the evaluation report received, the user reviews the project specifications again as needed and makes necessary corrections. Specifically, the user checks the evaluation report, re-enters the proposed revisions to the project specifications into the terminal, and the new specification data is sent to the server. The input is the specification revision based on the evaluation results, and the output is the refined specification data.

[0752] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0753] This invention is a system that efficiently visualizes project requirements and evaluates feasibility while considering user emotions. This system is mainly composed of a server, terminals, and an emotion engine.

[0754] Input and analysis of project requirements

[0755] The user begins by using their device to input project requirements via text or voice. For example, they can enter detailed development requirements for a new mobile application. The entered data is then sent from the device to the server.

[0756] The server uses generative artificial intelligence to analyze requirements based on user input. During the analysis process, data is abstracted and prepared for a virtual environment. This analysis helps understand the project structure and interface elements.

[0757] Virtual environment creation and visualization

[0758] Based on the analyzed data, the server generates a virtual environment. This includes the project design and a prototype of the operation flow. The terminal receives this and displays it to the user in an interactive format. This allows the user to gain a concrete understanding of the completed project and confirm the proposed specifications.

[0759] Application of the emotion engine

[0760] An emotion engine built into the device recognizes the user's emotions during visualization. This uses data collected through the camera and microphone. For example, the engine analyzes the user's facial expressions and vocal intonation, and records their emotional response to project requirements as feedback.

[0761] The server uses this sentiment data to dynamically adjust the user experience. For example, if a user expresses frustration, the system can improve usability by displaying an interface guide.

[0762] Feasibility assessment and proposal for improvement

[0763] The server evaluates the feasibility of the project based on a visualized virtual environment and sentiment data. This evaluation takes into account technical limitations, user feedback, and potential risks. The terminal presents these evaluation results to the user, clarifying the project's progress and areas for improvement.

[0764] By applying this system, users can gain support for clarifying requirements early in a project, improving the user experience with emotional considerations in mind, and making rapid decisions.

[0765] The following describes the processing flow.

[0766] Step 1:

[0767] The user enters project requirements using a terminal. The user describes detailed specifications and desired features in the interface via text message or voice input.

[0768] Step 2:

[0769] The terminal sends the entered requirement data to the server. This transmission is performed via a secure protocol, ensuring data integrity.

[0770] Step 3:

[0771] The server uses a generative artificial intelligence model to analyze the requirements data it receives. The server analyzes the text data of the requirements and identifies the core elements necessary for generating the virtual environment.

[0772] Step 4:

[0773] Based on the analysis results, the server generates a virtual environment. This environment includes the project's layout and interface design. The virtual environment data is then sent to the terminal.

[0774] Step 5:

[0775] The terminal visualizes and displays the virtual environment to the user. The user can interact with it, review the generated prototype, and evaluate its usability and visuals.

[0776] Step 6:

[0777] An emotion engine built into the device recognizes the user's emotions. Using the camera and microphone, it analyzes the user's facial expressions and voice tone in real time and sends the emotion data to a server.

[0778] Step 7:

[0779] The server evaluates the user experience based on emotional data and the virtual environment. The server analyzes the user's response to the project's feasibility and generates feedback.

[0780] Step 8:

[0781] The terminal displays evaluation results and feedback from the server to the user. Suggested improvements and technical adjustments are shown, allowing the user to modify their requirements based on this information.

[0782] Step 9:

[0783] The user modifies the project requirements based on feedback and re-enters them. The modified requirements are then analyzed again on the server, and a new virtual environment is generated.

[0784] (Example 2)

[0785] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0786] In the early stages of project development, it is crucial to efficiently visualize user requirements and enhance feasibility while considering emotional responses. However, conventional systems fail to adequately improve the user experience, leading to challenges such as misunderstandings of requirements and inefficient prototype creation. Furthermore, there is a lack of mechanisms for systems to dynamically utilize user emotional feedback.

[0787] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0788] In this invention, the server includes a device for inputting project requirements, a computing device using generative artificial intelligence for analyzing the project requirements, and a device for generating and visualizing a virtual environment based on the analyzed project requirements. This makes it possible to quickly generate a virtual prototype based on user requirements and dynamically improve usability by utilizing emotional feedback obtained by an emotion recognition engine.

[0789] "Project requirements" is a concept that refers to the specific requirements regarding the functions and specifications of a project.

[0790] "Device" refers to a mechanical or electronic mechanism designed to perform a specific function.

[0791] "Generative artificial intelligence" refers to an artificial intelligence model that learns on its own based on given data and has the ability to perform necessary analysis and predictions.

[0792] A "computational device" refers to a hardware or software system that receives input data and performs programmed arithmetic operations.

[0793] A "virtual environment" refers to a simulated or modeled environment created using computer technology, providing a domain where users can interact.

[0794] "Visualization" refers to the process of transforming abstract or complex data into a more easily understandable visual representation.

[0795] "Feasibility" refers to a measure of whether a particular plan or project can be carried out physically, technically, or economically.

[0796] "Analysis results" refer to conclusions or insights obtained by processing and analyzing data and information.

[0797] An "emotion recognition engine" refers to a system or software that analyzes voice and facial expression data to determine a user's emotional state.

[0798] "User experience" refers to the overall feeling or impression that a user experiences when using a system.

[0799] This invention is a system that efficiently visualizes project requirements and dynamically evaluates their feasibility while considering user emotions. This system is primarily implemented using a server, terminals, and an emotion recognition engine.

[0800] The user enters project requirements using a terminal. The terminal accepts input via text and voice, providing an interface that allows the user to freely express specific requirements, such as "develop customer management functionality for a new mobile application." The entered data is sent from the terminal to the server.

[0801] The server uses a generative AI model to analyze the received project requirements. This generative AI model uses a predefined prompt, "Analyze project requirements and specify the necessary components," to abstract the input requirements and extract the necessary interface elements. Based on the analyzed information, the server generates a virtual environment and visualizes the project's interface design and operation flow.

[0802] The generated virtual environment is sent back to the terminal and displayed interactively to the user. This allows the user to dynamically review the prototype of the proposed project and evaluate the specifications in detail.

[0803] Furthermore, the emotion recognition engine built into the device collects the user's emotions in real time through the camera and microphone. This analyzes the user's facial expressions and vocal intonation, and their emotional response to project requirements is recorded as feedback.

[0804] The server analyzes this collected emotional data and dynamically adjusts the user experience. For example, if a user shows a confused expression, the device can display additional guidance to improve usability. In this way, users can improve the UX by incorporating emotional feedback from the early stages of the project, enabling them to make quick and effective decisions.

[0805] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0806] Step 1:

[0807] The user enters project requirements through their device. The input is done via text or voice, and the device converts this into digital request data and sends it to the server. A specific example of input might be a requirement such as, "A new mobile application needs customer management functionality." The submitted data arrives at the server as information containing the basic project requirements.

[0808] Step 2:

[0809] The server launches a generative AI model to analyze the received requirements data. Here, the prompt "Analyze project requirements and specify the necessary components" is used to instruct the AI ​​to perform the analysis. The AI ​​model abstracts the requirements, computes interface and functional elements, and outputs the results as a series of datasets. These datasets contain the project's structural information.

[0810] Step 3:

[0811] Based on the analyzed data, the server generates a virtual environment. This virtual environment includes the project's design and operational flow. The server sends this information to the terminal, allowing the user to visually verify it. The output sent from the server is a digital model of the prototype.

[0812] Step 4:

[0813] The terminal processes the received virtual environment data and displays it to the user in an interactive format. This display allows the user to observe the concrete visuals of the project and evaluate whether it meets the requirements. The displayed output is a user-operable interface screen.

[0814] Step 5:

[0815] The emotion recognition engine built into the device collects user emotion data through the camera and microphone. The engine analyzes the user's facial expressions and voice tone to output an emotion state, which is then transmitted to a server in real time. This output includes emotional information such as the user's happiness or dissatisfaction.

[0816] Step 6:

[0817] The server uses the received sentiment data to dynamically adjust the user experience when necessary. For example, if the user is confused, the server sends a guide or tutorial screen to the device. This transmission outputs instructions for improving the interface, which are then displayed to the user on the device.

[0818] Step 7:

[0819] Finally, the server comprehensively evaluates the collected technical and emotional data to determine the project's feasibility. The resulting evaluation is sent to the terminal as a report, including the technical evaluation and user feedback, and presented to the user. This allows the user to specifically understand areas for improvement in the project and the next steps.

[0820] (Application Example 2)

[0821] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0822] Traditional project management systems rely heavily on human judgment for visualizing project requirements and evaluating feasibility, and because they fail to consider the user's feelings, improvements in the user experience are limited. Furthermore, they lack support for resolving users' potential anxieties and problems early on, beyond simply meeting requirements.

[0823] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0824] In this invention, the server includes a computation means using generative artificial intelligence to analyze project requirements, a means for generating and visualizing a virtual environment based on the analyzed requirements, and a means for analyzing user emotions and dynamically adjusting the experience. This allows for clarification of requirements through concrete visualization from the early stages of a project, and by adjusting responses based on user emotions, a better user experience can be provided, enabling faster decision-making.

[0825] "Project requirements" refer to the specific conditions and criteria necessary to achieve the project's objectives.

[0826] "Generative artificial intelligence" is an AI technology that can create new information based on input data and perform judgments and analyses.

[0827] A "virtual environment" refers to a space or situational simulation that provides a realistic experience generated on a computer.

[0828] "Visualization" is a technique that makes abstract data and information easier to understand by representing them graphically.

[0829] "Emotional data" refers to information about a user's feelings obtained and analyzed from their facial expressions and voice.

[0830] "Dynamic adjustment of user experience" refers to the immediate optimization of services and interfaces provided in response to user reactions and emotions.

[0831] This invention is a system primarily implemented using a server and a terminal. The server utilizes generative artificial intelligence to analyze project requirements input by the user. This analysis abstracts the project structure and generates the necessary virtual environment. The generated virtual environment includes the design and operation flow, and is visualized by the terminal. The user can interact with this virtual environment through the terminal.

[0832] The device incorporates an emotion engine that acquires user emotion data through the camera and microphone. This emotion data is obtained by analyzing the user's facial expressions and vocal intonation. Based on this emotion data, the server dynamically adjusts the user experience. Specifically, if the user shows signs of anxiety or confusion, the server improves the user experience by providing operational guides and navigation.

[0833] For example, if a user enters the requirements for a "simple cooking project at home," the server generates the project's steps and layout as a virtual environment, which is then visualized on the user's device. If the user experiences any anxiety while progressing through the cooking steps, the system will offer support, such as asking, "Do you have all the ingredients?"

[0834] An example of a prompt to the generating AI model would be: "Analyze the user's project requirements and generate a virtual environment in an easy-to-understand format. Based on sentiment data, suggest improvements to the project and enhance the user experience. The project is about 'simple cooking at home.' Engage in dialogue that responds to the user's emotions."

[0835] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0836] Step 1:

[0837] Users enter project requirements using a terminal. This input can be in text or voice format. The entered information is collected by the terminal and sent to the server. The input data includes specific project goals and conditions.

[0838] Step 2:

[0839] The server analyzes the received project requirements using generative artificial intelligence. This analysis process extracts the structural elements of the project from the input data and further abstracts related data. As a result, foundational data for generating the virtual environment is output.

[0840] Step 3:

[0841] Based on the analyzed project requirements, the server generates a virtual environment. This virtual environment includes the design and operational flow for a visual representation of the project. The generated data is sent to the terminal and displayed to the user, allowing them to concretely visualize the project concept.

[0842] Step 4:

[0843] The device uses a built-in emotion engine to collect user emotion data. This data is collected through the camera and microphone, and the user's facial expressions and voice intonation are analyzed. As a result of this emotion analysis, the user's emotional state is output.

[0844] Step 5:

[0845] The server uses emotional data to dynamically adjust the user experience. For example, if a user shows signs of anxiety, the server will instruct the device to provide operational guidance or suggest interactions. This prompting is done to improve the user experience.

[0846] Step 6:

[0847] The server evaluates the feasibility of the project based on the virtual environment and sentiment data. This evaluation process takes into account technical constraints and potential risks. The evaluation results are presented to the user as an analysis. This allows the user to identify areas for improvement in the project and make adjustments as needed.

[0848] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0849] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0850] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0851] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0852] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0853] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0854] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0855] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0856] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0857] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0858] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0859] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0860] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0861] 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.

[0862] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0863] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0864] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0865] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0866] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0867] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0868] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0869] The following is further disclosed regarding the embodiments described above.

[0870] (Claim 1)

[0871] A means of entering project requirements,

[0872] A computation means using generative artificial intelligence to analyze the aforementioned project requirements,

[0873] A means for generating and visualizing a virtual environment based on the aforementioned analyzed project requirements,

[0874] A means of evaluating the feasibility of a project using a visualized virtual environment,

[0875] A means for presenting the analysis results based on the aforementioned evaluation,

[0876] A system that includes this.

[0877] (Claim 2)

[0878] The system according to claim 1, further comprising means for automatically detecting potential problems in the visualized virtual environment of the project.

[0879] (Claim 3)

[0880] The system according to claim 1, comprising means for inputting the project requirements via text and voice.

[0881] "Example 1"

[0882] (Claim 1)

[0883] A means for users to input project requirements via a computing device,

[0884] An information processing means using generative artificial intelligence to analyze the aforementioned project requirements,

[0885] Based on the analyzed project requirements, the computer device provides means for generating and displaying a virtual space,

[0886] A means of assessing the feasibility of a project using a visualized virtual space,

[0887] A means to present the evaluation results based on the aforementioned assessment and encourage re-evaluation,

[0888] A system that includes this.

[0889] (Claim 2)

[0890] The system according to claim 1, comprising means for automatically identifying potential problems in the virtual space.

[0891] (Claim 3)

[0892] The system according to claim 1, comprising a device for inputting the project requirements by text and voice.

[0893] "Application Example 1"

[0894] (Claim 1)

[0895] A means of inputting project specifications,

[0896] A processing means using generative artificial intelligence to analyze the aforementioned project specifications,

[0897] A means of constructing a three-dimensional virtual environment based on the analyzed project specifications and presenting it visually,

[0898] A means of evaluating the feasibility of a project using a visually presented virtual environment,

[0899] Means for providing analysis results based on the aforementioned evaluation,

[0900] A means to enable the display and operation of a virtual environment on-site via a mobile device,

[0901] A system that includes this.

[0902] (Claim 2)

[0903] The system according to claim 1, further comprising means for automatically identifying potential issues in the three-dimensional virtual environment.

[0904] (Claim 3)

[0905] The system according to claim 1, further comprising means for inputting the project specifications via text information and audio information.

[0906] "Example 2 of combining an emotion engine"

[0907] (Claim 1)

[0908] A device for inputting project requirements,

[0909] A computing device using generative artificial intelligence to analyze the aforementioned project requirements,

[0910] A device that generates and visualizes a virtual environment based on the aforementioned analyzed project requirements,

[0911] A device that uses a visualized virtual environment to evaluate the feasibility of a project,

[0912] A device for presenting analysis results based on the aforementioned evaluation,

[0913] An emotion recognition engine for collecting user emotion data,

[0914] A device for adjusting the user experience using the aforementioned emotional data,

[0915] A system that includes this.

[0916] (Claim 2)

[0917] The system according to claim 1, wherein the emotion recognition engine has a device for analyzing the user's emotional response in a visualized virtual environment and dynamically improving usability.

[0918] (Claim 3)

[0919] The system according to claim 1, comprising a device through which the aforementioned project requirements are input via text and voice.

[0920] "Application example 2 when combining with an emotional engine"

[0921] (Claim 1)

[0922] A means of entering project requirements,

[0923] A computation means using generative artificial intelligence to analyze the aforementioned project requirements,

[0924] A means for generating and visualizing a virtual environment based on the aforementioned analyzed project requirements,

[0925] A means of evaluating the feasibility of a project using a visualized virtual environment,

[0926] Means for acquiring emotional data,

[0927] A means for analyzing user emotions and dynamically adjusting the user experience based on the analysis results,

[0928] A means for presenting the analysis results based on the aforementioned evaluation and sentiment data,

[0929] A system that includes this.

[0930] (Claim 2)

[0931] The system according to claim 1, further comprising means for automatically detecting potential problems in a visualized virtual environment of the project, and means for suggesting support in accordance with the user's emotions.

[0932] (Claim 3)

[0933] The system according to claim 1, wherein the aforementioned project requirements are input via text and voice, and further comprises means for analyzing emotions from the user's voice and video. [Explanation of Symbols]

[0934] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of entering project requirements, A computation means using generative artificial intelligence to analyze the aforementioned project requirements, A means for generating and visualizing a virtual environment based on the aforementioned analyzed project requirements, A means of evaluating the feasibility of a project using a visualized virtual environment, A means for presenting the analysis results based on the aforementioned evaluation, A system that includes this.

2. The system according to claim 1, further comprising means for automatically detecting potential problems in the visualized virtual environment of the project.

3. The system according to claim 1, further comprising means for inputting the project requirements via text and voice.

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