system

Abstract

To create an environment where computing resources can be utilized without requiring specialized knowledge. [Solution] The system includes means for authenticating a user based on input information from a terminal, means for receiving and storing a program for providing computing resources uploaded by the user, means for analyzing the stored program and optimizing it to improve computing efficiency, means for executing the optimized program in a high-performance computing environment, and means for making the execution results available to the user.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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] Many users who want to utilize a high-performance computing environment have problems such as high barriers to utilization because specialized programming knowledge and settings are required. Also, in order to maximize the computational efficiency of a program, advanced optimization techniques are necessary, which is also a difficult task for general users. Therefore, these problems prevent the democratization of high-performance computing resources.

Means for Solving the Problems

[0005] This invention provides a system that receives and stores programs that facilitate access to a high-performance computing environment and provide computing resources, based on information input from a user's terminal. Furthermore, this system analyzes the stored programs, automatically evaluates the possibility of parallel processing using artificial intelligence, and performs optimization to improve execution efficiency. The optimized programs are automatically executed in the high-performance computing environment, and the execution results can be easily obtained by the user. This realizes an environment in which computing resources can be utilized without requiring specialized knowledge.

[0006] A "terminal" is a device used by users to input information and communicate with a high-performance computing system.

[0007] A "user" is an entity that seeks to utilize computing resources by using a high-performance computing system.

[0008] "Computational resources" refer to physical or virtual computing power provided for performing advanced computational processing.

[0009] A "program" is software code that a user wishes to execute in order to utilize computing resources.

[0010] "To save" means to record received information or programs into a persistent memory area.

[0011] "Analyzing" refers to the process of evaluating the structure and function of a program and considering the possibility of optimization.

[0012] "Optimization" refers to improving a program to enhance computational efficiency and resource utilization efficiency.

[0013] Artificial intelligence is a technology used to enable computers to mimic human intelligence and automate or streamline specific tasks.

[0014] "Parallel processing" is a technique that improves processing speed by executing multiple calculations simultaneously.

[0015] A "high-performance computing environment" is a dedicated hardware and software configuration for processing complex computational tasks at high speed.

[0016] "Execute" means to run a program code on computing resources and proceed with the calculation.

[0017] "Execution result" is a general term for data and outputs obtained by the execution of a program.

Brief Explanation of Drawings

[0018] [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. <000,0079> [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 combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0019] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a labeled 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.

[0022] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0023] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0024] 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).

[0025] 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."

[0026] [First Embodiment]

[0027] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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".

[0039] This invention provides a system that allows users to easily utilize a high-performance computing environment. Specific embodiments of the system are described below.

[0040] System Overview

[0041] Users access a dedicated web portal from an internet-connected device. The web portal is an interface that consistently provides functions such as user authentication, project creation, code upload, execution environment configuration, job execution, and result confirmation.

[0042] Explanation of each function

[0043] 1. User authentication and project creation

[0044] Users create or log in to an account on the web portal and create new projects that utilize computing resources. The server authenticates user information and stores project information.

[0045] 2. Code Upload and Analysis

[0046] The server receives the program code uploaded by the user and stores it securely. Afterward, an artificial intelligence module analyzes the code. During the analysis, bottlenecks for improving computational efficiency are identified, and opportunities for parallel processing optimization are discovered.

[0047] 3. Optimization and execution environment setup

[0048] Based on the analysis results, the server proposes optimizations and automatically optimizes the code after user approval. The user then configures the execution environment and prepares it for execution in a high-performance computing environment.

[0049] 4. Execution and Monitoring

[0050] The server executes optimized programs in the specified computing environment. Users can monitor job progress in real time and intervene as needed.

[0051] 5. Results Acquisition and Analysis

[0052] After execution is complete, the server stores the results in the user's project area, which the user can then use for further analysis.

[0053] Specific example

[0054] For example, consider a large-scale simulation based on weather data. The user creates a project and uploads the simulation program. The server analyzes the program and applies parallel processing to improve computation speed. The user specifies the necessary computing resources and instructs the server to run the simulation in a high-performance computing environment. As a result, the rapidly processed prediction data can be downloaded and used for further research.

[0055] This invention provides an environment in which anyone can perform advanced calculations without requiring the specialized knowledge or complex settings that were previously necessary.

[0056] The following describes the processing flow.

[0057] Step 1:

[0058] The user accesses the web portal from their device and authenticates themselves by entering their email address and password on the login page. The server receives the user's authentication information, verifies it against the database, and completes the login.

[0059] Step 2:

[0060] The user selects the "Create New Project" option from the dashboard, enters a project name and description, and creates the project. The server accepts this, registers the project in the database, and stores the related information.

[0061] Step 3:

[0062] Users upload program code from the project page. Once the selected file is sent from the terminal to the server, the server receives the file, stores it securely, and performs appropriate security checks.

[0063] Step 4:

[0064] The server sends the stored program code to an internal artificial intelligence module. The AI ​​analyzes the code and identifies opportunities for parallel processing and optimization of the programming structure. The analysis results are presented to the user as feedback.

[0065] Step 5:

[0066] The user reviews the optimization suggestions provided by the server and chooses whether to apply them. If the user approves the optimization, the server automatically optimizes the program code and prepares it for execution.

[0067] Step 6:

[0068] On the execution environment settings page, the user specifies resource requirements such as the number of CPU cores and memory size. Based on this, the server selects a high-performance computing environment and reserves the necessary resources.

[0069] Step 7:

[0070] The server begins executing optimized programs on the reserved computing environment. During this time, users can monitor the job progress and resource usage in real time from a dashboard.

[0071] Step 8:

[0072] Once the calculation is complete, the server stores the result data in the project's storage and sends a completion notification to the user. The user can then download the results from their device and use them for further analysis and reporting.

[0073] (Example 1)

[0074] 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."

[0075] With the advancement of modern science and technology, there is a growing demand for highly efficient processing of complex and large-scale data. However, utilizing high-performance computing environments requires specialized knowledge and complex environment setup, which places a burden on users. Furthermore, optimizing code and effectively allocating resources to maximize computational efficiency remain challenges. To solve these problems, there is a need for systems that allow users to easily access high-performance computing resources and enable efficient data processing.

[0076] 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.

[0077] In this invention, the server includes means for authenticating a user based on input information from a terminal, means for receiving and storing code for providing computing resources uploaded by the user, means for analyzing the stored code using a generation AI model and performing optimization to improve computing efficiency, and means for executing the optimized code in a high-performance data processing environment. As a result, users can efficiently and dynamically perform complex data processing using a high-performance computing environment without requiring specialized knowledge.

[0078] A "terminal" is a device used by a user to input data and connect to a system.

[0079] "Input information" refers to data or instructions that a user provides to the system through their device.

[0080] "User" refers to an individual or group that uses the system to perform calculations or data processing.

[0081] A "server" is a central device that processes requests from terminals via a network and performs data storage and calculations.

[0082] "Authentication" refers to the procedure for confirming that a user is a legitimate user.

[0083] "Computational resources" refers to the software and hardware configurations used to perform specific data processing or calculations.

[0084] "Code" is a set of instructions that make up part or all of a program, and is text expressed in a format that a computer can understand and execute.

[0085] A "generative AI model" refers to a computational model designed based on machine learning algorithms and used to perform predictions and optimizations for specific problems.

[0086] "Analysis" refers to the process of analyzing data and program code to identify specific patterns and areas for efficiency improvement.

[0087] "Optimization" refers to the process of modifying code or processing processes to improve computational efficiency and resource utilization.

[0088] A "high-performance data processing environment" refers to a specific configuration of computer hardware and software designed to perform complex and large-scale calculations quickly.

[0089] "Resource allocation" refers to the process of distributing computing resources such as computing power and memory in a high-performance data processing environment to specific tasks or users.

[0090] This invention provides a system configuration that allows users to efficiently utilize a high-performance data processing environment. Users access a dedicated web portal from an internet-connected terminal. This web portal provides an interface that centrally manages user authentication, program code upload and analysis, optimization suggestions, execution environment settings, job execution and progress monitoring, and acquisition of execution results. The server authenticates the user based on input information received from the terminal and securely stores the program code uploaded by the user.

[0091] During the analysis process, the server utilizes a generative AI model to analyze the code, identifying the applicability of parallel processing and bottlenecks for performance improvement. This model employs advanced machine learning algorithms for the analysis. Based on the analysis results, the server presents the user with code optimization suggestions. The user can approve the necessary optimizations and have them automatically applied. The user also specifies the required hardware resources (e.g., CPU, GPU, memory capacity) to prepare a high-performance data processing environment.

[0092] During the execution process, the server runs the optimized program in a high-performance computing environment. Users can monitor the job progress in real time via a terminal. Job parameters can be dynamically adjusted as needed. Once the calculation is complete, the server saves the results to a project space accessible to the user. Users can retrieve these results and perform further data analysis.

[0093] As a concrete example, if a user wants to perform a large-scale simulation using weather data, they would create a dedicated program and upload it to a web portal. The server would analyze this program and suggest an appropriate parallel processing method. The user would then select the necessary computing resources and instruct the execution. The resulting forecast data could then be downloaded and used for further research and analysis.

[0094] An example of a prompt message could be: "Explain how to optimize a program for weather simulations and run it in a high-performance computing environment." This system provides an environment where advanced data processing can be performed efficiently even without specialized knowledge.

[0095] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0096] Step 1:

[0097] The user accesses the web portal through their device and enters their login information. The server receives this input data and authenticates the user by comparing it with existing information in the database. If this authentication process is successful, the user's My Page screen is displayed.

[0098] Step 2:

[0099] The user enters the necessary information to create a new project (project name, purpose, etc.). The server saves this information, generates a new project ID, and registers it in the database. After the project is created, the user is moved to the code upload screen.

[0100] Step 3:

[0101] The user selects the program code they want to run from their terminal and uploads it to the server via a web portal. The server securely stores the code in storage and then prepares to begin the analysis process.

[0102] Step 4:

[0103] The server analyzes the stored program code using a generation AI model. This analysis examines the code structure and identifies bottlenecks and opportunities for parallel processing to improve computation speed. As a result of the analysis, optimization suggestions are generated.

[0104] Step 5:

[0105] The server notifies the user of optimization suggestions based on the analysis results. The user reviews the suggestions via their terminal and approves or modifies them as needed. After approval, the server automatically applies the optimizations to the program code.

[0106] Step 6:

[0107] The user configures the execution conditions (CPU, memory, GPU, etc.) for a high-performance data processing environment at the terminal. The server dynamically allocates the necessary resources based on the entered conditions. This configuration is reflected in the execution plan.

[0108] Step 7:

[0109] The server executes optimized programs in a specified high-performance data processing environment. During this execution process, the terminal monitors and displays the job progress in real time, providing the user with information on progress and resource usage.

[0110] Step 8:

[0111] Once data processing is complete, the server saves the results to the user's project area. The user can then retrieve this data via their terminal for further analysis and use. This data can then provide new insights.

[0112] (Application Example 1)

[0113] 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."

[0114] In the operation of automated machinery in the industrial sector, operational efficiency and route optimization are crucial. However, in real-world factory environments, it is difficult to flexibly respond to complex operating schedules and dynamic changes in resources. Furthermore, while there is a demand for faster and more accurate optimization processes, conventional systems often require specialized knowledge and additional configurations, limiting their use. There is a need to solve these problems and efficiently advance factory automation.

[0115] 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.

[0116] In this invention, the server includes means for authenticating a user based on input information from a terminal, means for receiving and storing a program for providing computing resources uploaded by the user, and means for analyzing the stored program and performing optimization to improve computing efficiency. This makes it possible to optimize the operating schedule of industrial machinery in a factory and improve operational efficiency.

[0117] A "terminal" is a device used by a user to input information and connect to a server via a network.

[0118] "User authentication" refers to the process by which a server verifies the user's identity and permissions based on information transmitted from the device.

[0119] A "program for providing computing resources" is software that a user provides to a server in order to efficiently perform a specific computing task.

[0120] "Optimization methods" refer to the process of automatically analyzing and correcting computational programs in order to improve their efficiency and performance.

[0121] A "high-performance computing environment" is a computing infrastructure designed to perform large-scale computational processing at high speed.

[0122] "Means of providing optimized route information to industrial machinery in real time" refers to the process of optimizing machine operation within a factory and generating and instructing efficient routes.

[0123] "Means of making execution results available to the user" refers to the process of making the results accessible and usable by the user after the calculation is completed.

[0124] "Evaluating the potential of parallel processing and automatically performing optimization" is a process that analyzes the ability of multiple processors to process a single computational task simultaneously and automatically adjusts it to maximize efficiency.

[0125] "Dynamically adjusting resource allocation and optimizing the operating schedule of industrial machinery" refers to the process of changing the allocation of computing resources according to the situation and efficiently restructuring the machine operation plan.

[0126] The system implementing this invention consists of terminals, servers, and a network to provide a high-performance computing environment. Users first access a dedicated web portal through their personal terminal and begin operations. The terminal is an internet-connected device.

[0127] The server authenticates users based on their input. Authenticated users are granted access to the system and can utilize a variety of functions. Programs uploaded by users are accepted by the server and securely stored. After storage, the server analyzes the program using a generative AI model and optimizes the results to improve computational efficiency. Here, TENSORFLOW® is used as an AI module to evaluate the potential of parallel processing.

[0128] Furthermore, the server dynamically allocates resources from a high-performance computing environment to execute programs according to user requests. During this process, calculation results are fed back to industrial machinery in real time, optimizing the factory's operational schedule. This supports the efficient operation of the factory.

[0129] A concrete example is the operation management of robots in an automotive parts manufacturing line. The server presents the operation route in the form of prompt messages based on the operating status data. In the format of "Analyze the data for robot path optimization and generate the next work instruction. Propose the optimal route and movement procedure from the current work point A to target point B," continuous optimization is performed using a generated AI model. Through this process, the user can achieve efficient production activities.

[0130] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0131] Step 1:

[0132] The user accesses a dedicated web portal using their personal device and enters their authentication information on the login screen. The server receives this information, compares it with the database, and authenticates the user. If authentication is successful, the user can proceed to the next step. The input is the user's authentication information, and the output is the granting of user privileges.

[0133] Step 2:

[0134] The user uses a terminal to upload the program code they want to perform calculations on to the server via a web portal. The server receives the code, saves it, and prepares it for analysis. The input is the program code, and the output is the saved program data. Here, files are received and saved to secure storage.

[0135] Step 3:

[0136] The server analyzes saved programs using a generation AI model to identify bottlenecks in the code. It analyzes the possibility of parallel processing in the program and proposes necessary optimizations. In this process, TensorFlow is used to reduce the computational load and maximize code efficiency. The input is saved program data, and the output is optimization proposals and analysis results.

[0137] Step 4:

[0138] The user reviews and approves the optimization proposals provided by the server. After approval, the server automatically optimizes the program and prepares it for execution in a high-performance computing environment. The input is the user's approval and optimization proposals, and the output is the optimized program.

[0139] Step 5:

[0140] The server executes an optimized program and generates real-time path information required for industrial machinery. The generated data is provided to robots in the factory in real time. The input is the optimized program, and the output is the generated path information. In this step, calculations are performed, and the robot's motion instructions are finalized.

[0141] 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.

[0142] This invention improves the user experience by incorporating a system that not only allows users to easily utilize a high-performance computing environment, but also by adding a function that recognizes and responds to the user's emotions. Specific embodiments of the system are described below.

[0143] System-wide configuration

[0144] Users access a dedicated web portal from an internet-connected device. In addition to basic functions such as user authentication, project creation, code upload, execution environment configuration, job execution, and result confirmation, the web portal also features an emotion engine that recognizes the user's emotions.

[0145] Detailed explanation of each function

[0146] 1. User authentication and project creation

[0147] Users create or log in to an account on the web portal and create a new project. The server authenticates the user's information and securely stores the project information. From this stage, the emotion engine reads the user's emotion data and optimizes the system interface response.

[0148] 2. Code Upload and Analysis

[0149] The user uploads program code to the project they have created. The server saves the received program and analyzes it using an artificial intelligence module. At this time, the emotion engine can sense the user's stress and anxiety and provide appropriate feedback.

[0150] 3. Optimization and execution environment setup

[0151] Based on the code analysis results, the server presents optimization suggestions to the user, and optimizes the code after obtaining approval. The user configures the execution environment, and the emotion engine detects the user's preferences and fatigue during this process, and makes configuration suggestions to the user.

[0152] 4. Execution and Monitoring

[0153] The server executes optimized programs in the computing environment. Users check the progress of jobs from a dashboard. During this time, the emotion engine monitors the user's emotions in real time and adjusts the interface and notifications as needed.

[0154] 5. Results Acquisition and Analysis

[0155] Once execution is complete, the server stores the results in the user's project area. The user downloads the result data and performs analysis. The emotion engine collects the user's emotional feedback and uses it to improve future system operations.

[0156] Specific example

[0157] For example, when a data science researcher performs large-scale data analysis, the user creates a project and uploads code. The server, through an emotion engine, senses the user's stress level and displays guidance to make the process smoother. This allows the user to work efficiently and comfortably.

[0158] This configuration not only allows for the use of a high-performance computing environment, but also provides user-friendly interactions, thereby improving both computational efficiency and user experience.

[0159] The following describes the processing flow.

[0160] Step 1:

[0161] The user accesses the system through their device and attempts to log in to their account on the web portal. The server receives the authentication information entered by the user and completes the authentication process by comparing it with the database.

[0162] Step 2:

[0163] When a user logs into the system, a dashboard is displayed on their terminal. The user enters the necessary information to create a new project and registers the project. The server stores the project in a database and manages it by assigning a project ID to the user.

[0164] Step 3:

[0165] Users access the project page from their device and upload the program code they want to run. The server securely receives this code, stores it in storage, and performs security checks.

[0166] Step 4:

[0167] The server sends the stored program code to a dedicated artificial intelligence module and begins analysis to improve computational efficiency. Simultaneously, the emotion engine monitors the user's input from multiple angles and evaluates their emotional state.

[0168] Step 5:

[0169] Based on the analysis results obtained from the artificial intelligence module, the server makes optimization suggestions to the user. The user reviews the suggestions on the screen and chooses whether to allow the optimization. At this point, the emotion engine provides feedback tailored to the user's emotions.

[0170] Step 6:

[0171] The user enters the necessary resource requirements from their terminal and configures the execution environment. The server dynamically allocates high-performance computing resources based on the entered information. The emotion engine continues to monitor emotional responses to the input operations.

[0172] Step 7:

[0173] The server runs programs optimized for a high-performance computing environment and collects data in real time while jobs are running. Users monitor progress and system performance from a dashboard on their terminals. If the emotion engine detects abnormal emotions, the server notifies the user.

[0174] Step 8:

[0175] Once the job is complete, the server stores the results in the user's project area. The user downloads the results from their terminal and begins analysis. The sentiment engine receives user feedback and uses it to improve future system responses.

[0176] (Example 2)

[0177] 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".

[0178] When using high-performance computing environments, the burden on users increases due to the setting up and execution of complex calculation processes, making efficient use of computing power difficult. Furthermore, conventional systems lack support that takes into account the user's emotions and fatigue levels, and thus have the problem of not adequately considering the quality of the user experience.

[0179] 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.

[0180] In this invention, the server includes means for authenticating an individual based on input information from a terminal device, means for receiving and storing a program uploaded by the individual to provide a computing device, means for analyzing the stored program and optimizing it to improve computing efficiency, and emotion recognition means for detecting the individual's emotional state and optimizing the system's operation and interface. This makes it possible to efficiently utilize a high-performance computing environment while taking the user's emotions into consideration.

[0181] A "terminal device" is an electronic device used by users to input information and access a system.

[0182] "Individual" refers to a user who utilizes the system and manages computing resources and programs.

[0183] A "computational device" is a part of a system that includes programs and hardware for performing high-performance calculations.

[0184] "Saving" refers to the process of accumulating and storing uploaded data and information in a database or storage device.

[0185] "Analysis" is the process of examining program code in detail and evaluating its operation and structure.

[0186] "Optimization" is the process of improving the structure and execution method of a program in order to increase computational efficiency.

[0187] "Intelligent function" refers to automated processing and learning using artificial intelligence technology.

[0188] "Emotion recognition means" refers to technology that detects the user's emotional state and adjusts the system's operation and interface based on that.

[0189] "Resource allocation" is the operation of efficiently distributing necessary resources within a computing environment.

[0190] This invention provides a system that allows users to effectively and efficiently utilize a high-performance computing environment. The system provides a dedicated web portal that users access through a terminal device. Through this portal, users can input, save, and execute program code for using the computing device.

[0191] The terminal device receives input data from the user and sends it to the server. After the user logs into the web portal and uploads program code, the server receives this data and securely stores it in storage. Generative AI models are used for program analysis and optimization, and the possibility of parallel processing is evaluated to improve efficiency. This maximizes the computational efficiency when the code is executed. The optimized program is then run on the server in a high-performance computing environment.

[0192] By incorporating emotion recognition capabilities, the server can detect the user's emotional state in real time and dynamically adjust the system interface to improve the user experience. For example, if a user is performing large-scale data analysis in a research project, the server can sense the user's tension during the analysis and display a relaxing suggestion message on the screen. In this way, incorporating emotion recognition aims to reduce user stress and provide a smooth and comfortable work environment.

[0193] An example of a prompt message is: "In a data science project, we want to perform analysis on a large dataset. Please use a system that can recognize emotions and provide appropriate guidance to set up the optimal computing environment and monitor progress."

[0194] This allows users to maximize their computing resources through the system while reducing psychological stress during computation.

[0195] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0196] Step 1:

[0197] The user accesses the web portal from their device and enters their login information. The device sends this information to the server. The server consults its database and authenticates the user based on the entered information. If authentication is successful, the user is granted access permissions and redirected to the project creation screen.

[0198] Step 2:

[0199] When a user creates a project, they select a program code file via their terminal and upload it to the server. The server accepts the file and saves it to storage. The saved program code is then analyzed by a generated AI model on the server. The input code is evaluated for its structure and potential for optimization, and an analysis report is generated as output.

[0200] Step 3:

[0201] The user reviews the proposed code optimizations based on the analysis report. The server prepares to execute the proposed optimization actions and awaits user approval. Once the user approves the optimizations, the server optimizes the program code. Based on the input approval information, the program structure is improved, and optimized code is generated as output.

[0202] Step 4:

[0203] The user uses a terminal to select configuration options for the execution environment, including the selection of necessary libraries and computing resources. The server uses emotion recognition to evaluate the user's emotional state in real time based on their selections. If the emotional state is determined to be unstable, the UI displays recommended settings and advice for relaxation.

[0204] Step 5:

[0205] Once ready, the user presses the run button to execute the optimized program in a high-performance computing environment. The server allocates computing resources and enables parallel processing. During execution, the server monitors the job's progress and outputs it to the dashboard in real time. The terminal displays this information to the user and provides notifications as needed.

[0206] Step 6:

[0207] Once the job execution is complete, the server generates result data and saves it to the user's project area. The user downloads the results using a terminal and performs a detailed analysis. During this process, the sentiment recognition function collects user feedback, which is used to further improve the system.

[0208] (Application Example 2)

[0209] 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".

[0210] Many high-performance computing systems have challenges that make it difficult for users to efficiently utilize the computing environment, particularly a lack of support that takes into account the user's emotions and stress during computation. As a result, user productivity and comfort have not been sufficiently improved.

[0211] 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.

[0212] In this invention, the server includes means for authenticating a user based on input information from a terminal, means for receiving and storing processing instructions for providing computing resources uploaded by the user, means for analyzing the stored processing instructions and optimizing them to improve computing efficiency, means for executing the optimized processing instructions in a high-performance computing environment, means for making the execution results available to the user, and means for determining the user's emotional state using emotion detection and dynamically adjusting the operating interface. This makes it possible to construct an efficient computing environment that takes into account the user's emotions during computing work.

[0213] A "terminal" is a computing device used by users to input information.

[0214] "Input information" refers to data or instructions that a user provides to the system via a terminal.

[0215] A "user" is an individual or group that uses the system and utilizes its computing resources and functions.

[0216] "Authentication means" refers to methods or devices used to verify the user's identity and ensure their eligibility to use the service.

[0217] "Computational resources" refer to the computing power and storage space used by users in a high-performance computing environment.

[0218] A "processing instruction" is program code or instructions that users upload and execute in order to utilize computing resources.

[0219] "Means of storage" refers to methods or devices for storing and retaining received data or programs in a memory device.

[0220] "Analysis" is the process of examining saved program code and investigating its contents and characteristics.

[0221] "Optimization" is a technique for adjusting programs and computing environments to improve the efficiency and speed of calculations.

[0222] A "high-performance computing environment" is an advanced computing system capable of performing large-scale data processing and calculations.

[0223] "Means for detecting emotions" refers to methods or devices for recognizing and judging emotions from a user's facial expressions, voice, etc.

[0224] An "operation interface" refers to the user interface, such as screens and menus, that users use when interacting with a system.

[0225] "Means of dynamic adjustment" refers to methods or devices that automatically change system settings and display content according to the user's state or environment.

[0226] In the system for implementing this invention, the program is designed so that the server, terminal, and user elements work together in coordination. The server receives input information from the user's terminal and authenticates the user. This authentication process ensures appropriate permissions and guarantees access to computing resources. Once authentication is complete, the user uploads processing instructions to the server to provide computing resources. The server accepts these processing instructions and stores them in its memory.

[0227] Subsequently, the server uses an AI module to analyze the processing instructions and optimize them to improve computational efficiency. For example, it can utilize the Microsoft® Emotion API to detect the user's emotional state and provide feedback according to the progress of the task. After the optimization process is complete, the server executes the improved processing instructions in a high-performance computing environment and provides the results to the user. In this process, data analysis can be performed using machine learning algorithms, for example, using TensorFlow.

[0228] As a concrete example, when a user performing data analysis in a factory submits a calculation task to the system, the server recognizes their emotions, and if stress is detected, it modifies the interface to simplify the operation. Furthermore, by entering a prompt such as, "I want to design an app that senses the stress and fatigue of factory workers. Please tell me how to use AI," the system will suggest an effective AI-powered solution.

[0229] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0230] Step 1:

[0231] The server receives input information from the terminal and authenticates the user. This input information includes the user's ID and password. The authentication process verifies the user information against the database to confirm that the user has legitimate authority. If authentication is successful, the user can log in to the system.

[0232] Step 2:

[0233] The user uploads processing instructions from their terminal to the server to provide computing resources. These instructions are, for example, program code for data analysis. The server receives them and securely stores them in its storage device.

[0234] Step 3:

[0235] The server analyzes stored processing instructions using an AI module. This analysis evaluates, for example, the possibility of parallel processing to efficiently execute the code. Processing instructions are used as input, and optimization suggestions and execution plans are generated as output. An AI model (e.g., a generative AI model) assists this analysis.

[0236] Step 4:

[0237] The server optimizes processing instructions based on the optimization suggestions obtained through analysis. This optimization aims to speed up calculations and make efficient use of resources. The optimized code is output and ready for execution in a high-performance computing environment.

[0238] Step 5:

[0239] The server executes optimized processing instructions in a high-performance computing environment. Here, the necessary computing resources are dynamically allocated. Optimized code is used as input, and the computational results are obtained as output.

[0240] Step 6:

[0241] During operation, the server monitors the user's emotions in real time using means to detect emotions. For example, it analyzes data from cameras and microphones to determine if the user is experiencing stress. This information is used to adjust the user interface or suggest breaks.

[0242] Step 7:

[0243] After processing is complete, the user retrieves the calculation results via their terminal. The server saves the results to a database, allowing the user to view them immediately. The output data can then be used by the user for further analysis and decision-making.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] [Second Embodiment]

[0248] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0249] 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.

[0250] 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).

[0251] 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.

[0252] 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.

[0253] 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).

[0254] 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.

[0255] 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.

[0256] 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.

[0257] 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.

[0258] 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.

[0259] 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".

[0260] This invention provides a system that allows users to easily utilize a high-performance computing environment. Specific embodiments of the system are described below.

[0261] System Overview

[0262] Users access a dedicated web portal from an internet-connected device. The web portal is an interface that consistently provides functions such as user authentication, project creation, code upload, execution environment configuration, job execution, and result confirmation.

[0263] Explanation of each function

[0264] 1. User authentication and project creation

[0265] Users create or log in to an account on the web portal and create new projects that utilize computing resources. The server authenticates user information and stores project information.

[0266] 2. Code Upload and Analysis

[0267] The server receives the program code uploaded by the user and stores it securely. Afterward, an artificial intelligence module analyzes the code. During the analysis, bottlenecks for improving computational efficiency are identified, and opportunities for parallel processing optimization are discovered.

[0268] 3. Optimization and execution environment setup

[0269] Based on the analysis results, the server proposes optimizations and automatically optimizes the code after user approval. The user then configures the execution environment and prepares it for execution in a high-performance computing environment.

[0270] 4. Execution and Monitoring

[0271] The server executes optimized programs in the specified computing environment. Users can monitor job progress in real time and intervene as needed.

[0272] 5. Results Acquisition and Analysis

[0273] After execution is complete, the server stores the results in the user's project area, which the user can then use for further analysis.

[0274] Specific example

[0275] For example, consider the case of performing a large-scale simulation based on meteorological data. The user creates a project and uploads a program for the simulation. The server analyzes the program and applies parallel processing to improve the calculation speed. The user specifies the necessary computing resources and instructs the execution in a high-performance computing environment. As a result, the processed prediction data can be downloaded at high speed and used for further research.

[0276] According to this invention, an environment is provided in which anyone can perform advanced calculations without the need for the specialized knowledge and complex settings that were required in the past.

[0277] The processing flow will be described below.

[0278] Step 1:

[0279] The user accesses the web portal from the terminal, enters the email address and password on the login page to authenticate themselves. The server receives the user's authentication information, checks it against the database, and completes the login.

[0280] Step 2:

[0281] The user selects the option to create a new project from the dashboard, enters the project name and summary, and creates the project. The server accepts this, registers the project in the database, and holds the related information.

[0282] Step 3:

[0283] The user uploads the program code from the project page. When the file selected from the terminal is sent to the server, the server receives the file, saves it securely, and performs appropriate security checks.

[0284] Step 4:

[0285] The server sends the saved program code to the internal artificial intelligence module. The artificial intelligence analyzes the code and identifies opportunities for parallel processing and optimization in programming structures. The analysis results are presented to the user as feedback.

[0286] Step 5:

[0287] The user checks the optimization content presented by the server and selects whether to apply it. If the optimization is approved, the server automatically optimizes the program code and prepares it for execution.

[0288] Step 6:

[0289] The user specifies resource requirements such as the number of CPU cores and memory size on the execution environment settings page. Based on this, the server selects a high-performance computing environment and secures the reservation of the necessary resources.

[0290] Step 7:

[0291] The server starts executing the optimized program on the reserved computing environment. During this time, the user can monitor the progress of the job and the resource usage status in real time from the dashboard.

[0292] Step 8:

[0293] When the calculation is completed, the server stores the result data in the project storage and sends a completion notice to the user. The user can download the results from the terminal and utilize them for further analysis and reporting.

[0294] (Example 1)

[0295] Next, 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".

[0296] With the advancement of modern science and technology, there is a growing demand for highly efficient processing of complex and large-scale data. However, utilizing high-performance computing environments requires specialized knowledge and complex environment setup, which places a burden on users. Furthermore, optimizing code and effectively allocating resources to maximize computational efficiency remain challenges. To solve these problems, there is a need for systems that allow users to easily access high-performance computing resources and enable efficient data processing.

[0297] 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.

[0298] In this invention, the server includes means for authenticating a user based on input information from a terminal, means for receiving and storing code for providing computing resources uploaded by the user, means for analyzing the stored code using a generation AI model and performing optimization to improve computing efficiency, and means for executing the optimized code in a high-performance data processing environment. As a result, users can efficiently and dynamically perform complex data processing using a high-performance computing environment without requiring specialized knowledge.

[0299] A "terminal" is a device used by a user to input data and connect to a system.

[0300] "Input information" refers to data or instructions that a user provides to the system through their device.

[0301] "User" refers to an individual or group that uses the system to perform calculations or data processing.

[0302] A "server" is a central device that processes requests from terminals via a network and performs data storage and calculations.

[0303] "Authentication" refers to the procedure for confirming that a user is a legitimate user.

[0304] "Computing resources" refers to the software and hardware components used for specific data processing and calculations.

[0305] "Code" is a set of instructions that constitutes part or all of a program, and is text expressed in a form that can be understood and executed by a computer.

[0306] "Generative AI model" refers to a computational model designed based on machine learning algorithms and used to perform predictions and optimizations for specific tasks.

[0307] "Analysis" refers to analyzing data or program code to identify specific patterns and points for efficiency improvement.

[0308] "Optimization" refers to the process of modifying code or processing processes to improve computational efficiency and resource utilization.

[0309] "High-performance data processing environment" refers to a specific computer hardware and software configuration designed for performing complex and large-scale calculations quickly.

[0310] "Resource allocation" refers to the process of distributing computer resources such as computing power and memory in a high-performance data processing environment to specific tasks or users.

[0311] This invention provides a system form that enables users to efficiently utilize a high-performance data processing environment. The user accesses a dedicated web portal from a terminal connected to the Internet. This web portal provides an interface that comprehensively manages user authentication, upload and analysis of program code, proposal of optimizations, setting of the execution environment, execution and progress monitoring of jobs, and acquisition of execution results. The server authenticates the user based on the input information received from the terminal and securely stores the program code uploaded by the user.

[0312] During the analysis process, the server utilizes a generative AI model to analyze the code, identifying the applicability of parallel processing and bottlenecks for performance improvement. This model employs advanced machine learning algorithms for the analysis. Based on the analysis results, the server presents the user with code optimization suggestions. The user can approve the necessary optimizations and have them automatically applied. The user also specifies the required hardware resources (e.g., CPU, GPU, memory capacity) to prepare a high-performance data processing environment.

[0313] During the execution process, the server runs the optimized program in a high-performance computing environment. Users can monitor the job progress in real time via a terminal. Job parameters can be dynamically adjusted as needed. Once the calculation is complete, the server saves the results to a project space accessible to the user. Users can retrieve these results and perform further data analysis.

[0314] As a concrete example, if a user wants to perform a large-scale simulation using weather data, they would create a dedicated program and upload it to a web portal. The server would analyze this program and suggest an appropriate parallel processing method. The user would then select the necessary computing resources and instruct the execution. The resulting forecast data could then be downloaded and used for further research and analysis.

[0315] An example of a prompt message could be: "Explain how to optimize a program for weather simulations and run it in a high-performance computing environment." This system provides an environment where advanced data processing can be performed efficiently even without specialized knowledge.

[0316] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0317] Step 1:

[0318] The user accesses the web portal through their device and enters their login information. The server receives this input data and authenticates the user by comparing it with existing information in the database. If this authentication process is successful, the user's My Page screen is displayed.

[0319] Step 2:

[0320] The user enters the necessary information to create a new project (project name, purpose, etc.). The server saves this information, generates a new project ID, and registers it in the database. After the project is created, the user is moved to the code upload screen.

[0321] Step 3:

[0322] The user selects the program code they want to run from their terminal and uploads it to the server via a web portal. The server securely stores the code in storage and then prepares to begin the analysis process.

[0323] Step 4:

[0324] The server analyzes the stored program code using a generation AI model. This analysis examines the code structure and identifies bottlenecks and opportunities for parallel processing to improve computation speed. As a result of the analysis, optimization suggestions are generated.

[0325] Step 5:

[0326] The server notifies the user of optimization suggestions based on the analysis results. The user reviews the suggestions via their terminal and approves or modifies them as needed. After approval, the server automatically applies the optimizations to the program code.

[0327] Step 6:

[0328] The user configures the execution conditions (CPU, memory, GPU, etc.) for a high-performance data processing environment at the terminal. The server dynamically allocates the necessary resources based on the entered conditions. This configuration is reflected in the execution plan.

[0329] Step 7:

[0330] The server executes optimized programs in a specified high-performance data processing environment. During this execution process, the terminal monitors and displays the job progress in real time, providing the user with information on progress and resource usage.

[0331] Step 8:

[0332] Once data processing is complete, the server saves the results to the user's project area. The user can then retrieve this data via their terminal for further analysis and use. This data can then provide new insights.

[0333] (Application Example 1)

[0334] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0335] In the operation of automated machinery in the industrial sector, operational efficiency and route optimization are crucial. However, in real-world factory environments, it is difficult to flexibly respond to complex operating schedules and dynamic changes in resources. Furthermore, while there is a demand for faster and more accurate optimization processes, conventional systems often require specialized knowledge and additional configurations, limiting their use. There is a need to solve these problems and efficiently advance factory automation.

[0336] 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.

[0337] In this invention, the server includes means for authenticating a user based on input information from a terminal, means for receiving and storing a program for providing computing resources uploaded by the user, and means for analyzing the stored program and performing optimization to improve computing efficiency. This makes it possible to optimize the operating schedule of industrial machinery in a factory and improve operational efficiency.

[0338] A "terminal" is a device used by a user to input information and connect to a server via a network.

[0339] "User authentication" refers to the process by which a server verifies the user's identity and permissions based on information transmitted from the device.

[0340] A "program for providing computing resources" is software that a user provides to a server in order to efficiently perform a specific computing task.

[0341] "Optimization methods" refer to the process of automatically analyzing and correcting computational programs in order to improve their efficiency and performance.

[0342] A "high-performance computing environment" is a computing infrastructure designed to perform large-scale computational processing at high speed.

[0343] "Means of providing optimized route information to industrial machinery in real time" refers to the process of optimizing machine operation within a factory and generating and instructing efficient routes.

[0344] "Means of making execution results available to the user" refers to the process of making the results accessible and usable by the user after the calculation is completed.

[0345] "Evaluating the potential of parallel processing and automatically performing optimization" is a process that analyzes the ability of multiple processors to process a single computational task simultaneously and automatically adjusts it to maximize efficiency.

[0346] "Dynamically adjusting resource allocation and optimizing the operating schedule of industrial machinery" refers to the process of changing the allocation of computing resources according to the situation and efficiently restructuring the machine operation plan.

[0347] The system implementing this invention consists of terminals, servers, and a network to provide a high-performance computing environment. Users first access a dedicated web portal through their personal terminal and begin operations. The terminal is an internet-connected device.

[0348] The server authenticates users based on their input. Authenticated users are granted access to the system and can utilize its various functions. Programs uploaded by users are accepted by the server and securely stored. After storage, the server analyzes the program using a generative AI model and optimizes the results to improve computational efficiency. Here, TensorFlow is used as the AI ​​module to evaluate the potential of parallel processing.

[0349] Furthermore, the server dynamically allocates resources from a high-performance computing environment to execute programs according to user requests. During this process, calculation results are fed back to industrial machinery in real time, optimizing the factory's operational schedule. This supports the efficient operation of the factory.

[0350] A concrete example is the operation management of robots in an automotive parts manufacturing line. The server presents the operation route in the form of prompt messages based on the operating status data. In the format of "Analyze the data for robot path optimization and generate the next work instruction. Propose the optimal route and movement procedure from the current work point A to target point B," continuous optimization is performed using a generated AI model. Through this process, the user can achieve efficient production activities.

[0351] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0352] Step 1:

[0353] The user accesses a dedicated web portal using their personal device and enters their authentication information on the login screen. The server receives this information, compares it with the database, and authenticates the user. If authentication is successful, the user can proceed to the next step. The input is the user's authentication information, and the output is the granting of user privileges.

[0354] Step 2:

[0355] The user uses a terminal to upload the program code they want to perform calculations on to the server via a web portal. The server receives the code, saves it, and prepares it for analysis. The input is the program code, and the output is the saved program data. Here, files are received and saved to secure storage.

[0356] Step 3:

[0357] The server analyzes saved programs using a generation AI model to identify bottlenecks in the code. It analyzes the possibility of parallel processing in the program and proposes necessary optimizations. In this process, TensorFlow is used to reduce the computational load and maximize code efficiency. The input is saved program data, and the output is optimization proposals and analysis results.

[0358] Step 4:

[0359] The user reviews and approves the optimization proposals provided by the server. After approval, the server automatically optimizes the program and prepares it for execution in a high-performance computing environment. The input is the user's approval and optimization proposals, and the output is the optimized program.

[0360] Step 5:

[0361] The server executes an optimized program and generates real-time path information required for industrial machinery. The generated data is provided to robots in the factory in real time. The input is the optimized program, and the output is the generated path information. In this step, calculations are performed, and the robot's motion instructions are finalized.

[0362] 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.

[0363] This invention improves the user experience by incorporating a system that not only allows users to easily utilize a high-performance computing environment, but also by adding a function that recognizes and responds to the user's emotions. Specific embodiments of the system are described below.

[0364] System-wide configuration

[0365] Users access a dedicated web portal from an internet-connected device. In addition to basic functions such as user authentication, project creation, code upload, execution environment configuration, job execution, and result confirmation, the web portal also features an emotion engine that recognizes the user's emotions.

[0366] Detailed explanation of each function

[0367] 1. User authentication and project creation

[0368] Users create or log in to an account on the web portal and create a new project. The server authenticates the user's information and securely stores the project information. From this stage, the emotion engine reads the user's emotion data and optimizes the system interface response.

[0369] 2. Code Upload and Analysis

[0370] The user uploads program code to the project they have created. The server saves the received program and analyzes it using an artificial intelligence module. At this time, the emotion engine can sense the user's stress and anxiety and provide appropriate feedback.

[0371] 3. Optimization and execution environment setup

[0372] Based on the code analysis results, the server presents optimization suggestions to the user, and optimizes the code after obtaining approval. The user configures the execution environment, and the emotion engine detects the user's preferences and fatigue during this process, and makes configuration suggestions to the user.

[0373] 4. Execution and Monitoring

[0374] The server executes optimized programs in the computing environment. Users check the progress of jobs from a dashboard. During this time, the emotion engine monitors the user's emotions in real time and adjusts the interface and notifications as needed.

[0375] 5. Results Acquisition and Analysis

[0376] Once execution is complete, the server stores the results in the user's project area. The user downloads the result data and performs analysis. The emotion engine collects the user's emotional feedback and uses it to improve future system operations.

[0377] Specific example

[0378] For example, when a data science researcher performs large-scale data analysis, the user creates a project and uploads code. The server, through an emotion engine, senses the user's stress level and displays guidance to make the process smoother. This allows the user to work efficiently and comfortably.

[0379] This configuration not only allows for the use of a high-performance computing environment, but also provides user-friendly interactions, thereby improving both computational efficiency and user experience.

[0380] The following describes the processing flow.

[0381] Step 1:

[0382] The user accesses the system through their device and attempts to log in to their account on the web portal. The server receives the authentication information entered by the user and completes the authentication process by comparing it with the database.

[0383] Step 2:

[0384] When a user logs into the system, a dashboard is displayed on their terminal. The user enters the necessary information to create a new project and registers the project. The server stores the project in a database and manages it by assigning a project ID to the user.

[0385] Step 3:

[0386] Users access the project page from their device and upload the program code they want to run. The server securely receives this code, stores it in storage, and performs security checks.

[0387] Step 4:

[0388] The server sends the stored program code to a dedicated artificial intelligence module and begins analysis to improve computational efficiency. Simultaneously, the emotion engine monitors the user's input from multiple angles and evaluates their emotional state.

[0389] Step 5:

[0390] Based on the analysis results obtained from the artificial intelligence module, the server makes optimization suggestions to the user. The user reviews the suggestions on the screen and chooses whether to allow the optimization. At this point, the emotion engine provides feedback tailored to the user's emotions.

[0391] Step 6:

[0392] The user enters the necessary resource requirements from their terminal and configures the execution environment. The server dynamically allocates high-performance computing resources based on the entered information. The emotion engine continues to monitor emotional responses to the input operations.

[0393] Step 7:

[0394] The server runs programs optimized for a high-performance computing environment and collects data in real time while jobs are running. Users monitor progress and system performance from a dashboard on their terminals. If the emotion engine detects abnormal emotions, the server notifies the user.

[0395] Step 8:

[0396] Once the job is complete, the server stores the results in the user's project area. The user downloads the results from their terminal and begins analysis. The sentiment engine receives user feedback and uses it to improve future system responses.

[0397] (Example 2)

[0398] 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".

[0399] When using high-performance computing environments, the burden on users increases due to the setting up and execution of complex calculation processes, making efficient use of computing power difficult. Furthermore, conventional systems lack support that takes into account the user's emotions and fatigue levels, and thus have the problem of not adequately considering the quality of the user experience.

[0400] 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.

[0401] In this invention, the server includes means for authenticating an individual based on input information from a terminal device, means for receiving and storing a program uploaded by the individual to provide a computing device, means for analyzing the stored program and optimizing it to improve computing efficiency, and emotion recognition means for detecting the individual's emotional state and optimizing the system's operation and interface. This makes it possible to efficiently utilize a high-performance computing environment while taking the user's emotions into consideration.

[0402] A "terminal device" is an electronic device used by users to input information and access a system.

[0403] "Individual" refers to a user who utilizes the system and manages computing resources and programs.

[0404] A "computational device" is a part of a system that includes programs and hardware for performing high-performance calculations.

[0405] "Saving" refers to the process of accumulating and storing uploaded data and information in a database or storage device.

[0406] "Analysis" is the process of examining program code in detail and evaluating its operation and structure.

[0407] "Optimization" is the process of improving the structure and execution method of a program in order to increase computational efficiency.

[0408] "Intelligent function" refers to automated processing and learning using artificial intelligence technology.

[0409] "Emotion recognition means" refers to technology that detects the user's emotional state and adjusts the system's operation and interface based on that.

[0410] "Resource allocation" is the operation of efficiently distributing necessary resources within a computing environment.

[0411] This invention provides a system that allows users to effectively and efficiently utilize a high-performance computing environment. The system provides a dedicated web portal that users access through a terminal device. Through this portal, users can input, save, and execute program code for using the computing device.

[0412] The terminal device receives input data from the user and sends it to the server. After the user logs into the web portal and uploads program code, the server receives this data and securely stores it in storage. Generative AI models are used for program analysis and optimization, and the possibility of parallel processing is evaluated to improve efficiency. This maximizes the computational efficiency when the code is executed. The optimized program is then run on the server in a high-performance computing environment.

[0413] By incorporating emotion recognition capabilities, the server can detect the user's emotional state in real time and dynamically adjust the system interface to improve the user experience. For example, if a user is performing large-scale data analysis in a research project, the server can sense the user's tension during the analysis and display a relaxing suggestion message on the screen. In this way, incorporating emotion recognition aims to reduce user stress and provide a smooth and comfortable work environment.

[0414] An example of a prompt message is: "In a data science project, we want to perform analysis on a large dataset. Please use a system that can recognize emotions and provide appropriate guidance to set up the optimal computing environment and monitor progress."

[0415] This allows users to maximize their computing resources through the system while reducing psychological stress during computation.

[0416] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0417] Step 1:

[0418] The user accesses the web portal from their device and enters their login information. The device sends this information to the server. The server consults its database and authenticates the user based on the entered information. If authentication is successful, the user is granted access permissions and redirected to the project creation screen.

[0419] Step 2:

[0420] When a user creates a project, they select a program code file via their terminal and upload it to the server. The server accepts the file and saves it to storage. The saved program code is then analyzed by a generated AI model on the server. The input code is evaluated for its structure and potential for optimization, and an analysis report is generated as output.

[0421] Step 3:

[0422] The user reviews the proposed code optimizations based on the analysis report. The server prepares to execute the proposed optimization actions and awaits user approval. Once the user approves the optimizations, the server optimizes the program code. Based on the input approval information, the program structure is improved, and optimized code is generated as output.

[0423] Step 4:

[0424] The user uses a terminal to select configuration options for the execution environment, including the selection of necessary libraries and computing resources. The server uses emotion recognition to evaluate the user's emotional state in real time based on their selections. If the emotional state is determined to be unstable, the UI displays recommended settings and advice for relaxation.

[0425] Step 5:

[0426] Once ready, the user presses the run button to execute the optimized program in a high-performance computing environment. The server allocates computing resources and enables parallel processing. During execution, the server monitors the job's progress and outputs it to the dashboard in real time. The terminal displays this information to the user and provides notifications as needed.

[0427] Step 6:

[0428] Once the job execution is complete, the server generates result data and saves it to the user's project area. The user downloads the results using a terminal and performs a detailed analysis. During this process, the sentiment recognition function collects user feedback, which is used to further improve the system.

[0429] (Application Example 2)

[0430] 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."

[0431] Many high-performance computing systems have challenges that make it difficult for users to efficiently utilize the computing environment, particularly a lack of support that takes into account the user's emotions and stress during computation. As a result, user productivity and comfort have not been sufficiently improved.

[0432] 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.

[0433] In this invention, the server includes means for authenticating a user based on input information from a terminal, means for receiving and storing processing instructions for providing computing resources uploaded by the user, means for analyzing the stored processing instructions and optimizing them to improve computing efficiency, means for executing the optimized processing instructions in a high-performance computing environment, means for making the execution results available to the user, and means for determining the user's emotional state using emotion detection and dynamically adjusting the operating interface. This makes it possible to construct an efficient computing environment that takes into account the user's emotions during computing work.

[0434] A "terminal" is a computing device used by users to input information.

[0435] "Input information" refers to data or instructions that a user provides to the system via a terminal.

[0436] A "user" is an individual or group that uses the system and utilizes its computing resources and functions.

[0437] "Authentication means" refers to methods or devices used to verify the user's identity and ensure their eligibility to use the service.

[0438] "Computational resources" refer to the computing power and storage space used by users in a high-performance computing environment.

[0439] A "processing instruction" is program code or instructions that users upload and execute in order to utilize computing resources.

[0440] "Means of storage" refers to methods or devices for storing and retaining received data or programs in a memory device.

[0441] "Analysis" is the process of examining saved program code and investigating its contents and characteristics.

[0442] "Optimization" is a technique for adjusting programs and computing environments to improve the efficiency and speed of calculations.

[0443] A "high-performance computing environment" is an advanced computing system capable of performing large-scale data processing and calculations.

[0444] "Means for detecting emotions" refers to methods or devices for recognizing and judging emotions from a user's facial expressions, voice, etc.

[0445] An "operation interface" refers to the user interface, such as screens and menus, that users use when interacting with a system.

[0446] "Means of dynamic adjustment" refers to methods or devices that automatically change system settings and display content according to the user's state or environment.

[0447] In the system for implementing this invention, the program is designed so that the server, terminal, and user elements work together in coordination. The server receives input information from the user's terminal and authenticates the user. This authentication process ensures appropriate permissions and guarantees access to computing resources. Once authentication is complete, the user uploads processing instructions to the server to provide computing resources. The server accepts these processing instructions and stores them in its memory.

[0448] Subsequently, the server uses an AI module to analyze the processing instructions and optimize them to improve computational efficiency. For example, it can utilize the Microsoft Emotion API to detect the user's emotional state and provide feedback based on the progress of the task. After the optimization process is complete, the server executes the improved processing instructions in a high-performance computing environment and provides the results to the user. In this process, data analysis can be performed using machine learning algorithms, such as those using TensorFlow.

[0449] As a concrete example, when a user performing data analysis in a factory submits a calculation task to the system, the server recognizes their emotions, and if stress is detected, it modifies the interface to simplify the operation. Furthermore, by entering a prompt such as, "I want to design an app that senses the stress and fatigue of factory workers. Please tell me how to use AI," the system will suggest an effective AI-powered solution.

[0450] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0451] Step 1:

[0452] The server receives input information from the terminal and authenticates the user. This input information includes the user's ID and password. The authentication process verifies the user information against the database to confirm that the user has legitimate authority. If authentication is successful, the user can log in to the system.

[0453] Step 2:

[0454] The user uploads processing instructions from their terminal to the server to provide computing resources. These instructions are, for example, program code for data analysis. The server receives them and securely stores them in its storage device.

[0455] Step 3:

[0456] The server analyzes stored processing instructions using an AI module. This analysis evaluates, for example, the possibility of parallel processing to efficiently execute the code. Processing instructions are used as input, and optimization suggestions and execution plans are generated as output. An AI model (e.g., a generative AI model) assists this analysis.

[0457] Step 4:

[0458] The server optimizes processing instructions based on the optimization suggestions obtained through analysis. This optimization aims to speed up calculations and make efficient use of resources. The optimized code is output and ready for execution in a high-performance computing environment.

[0459] Step 5:

[0460] The server executes optimized processing instructions in a high-performance computing environment. Here, the necessary computing resources are dynamically allocated. Optimized code is used as input, and the computational results are obtained as output.

[0461] Step 6:

[0462] During operation, the server monitors the user's emotions in real time using means to detect emotions. For example, it analyzes data from cameras and microphones to determine if the user is experiencing stress. This information is used to adjust the user interface or suggest breaks.

[0463] Step 7:

[0464] After processing is complete, the user retrieves the calculation results via their terminal. The server saves the results to a database, allowing the user to view them immediately. The output data can then be used by the user for further analysis and decision-making.

[0465] 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.

[0466] 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.

[0467] 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.

[0468] [Third Embodiment]

[0469] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0470] 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.

[0471] 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).

[0472] 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.

[0473] 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.

[0474] 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).

[0475] 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.

[0476] 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.

[0477] 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.

[0478] 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.

[0479] 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.

[0480] 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".

[0481] This invention provides a system that allows users to easily utilize a high-performance computing environment. Specific embodiments of the system are described below.

[0482] System Overview

[0483] Users access a dedicated web portal from an internet-connected device. The web portal is an interface that consistently provides functions such as user authentication, project creation, code upload, execution environment configuration, job execution, and result confirmation.

[0484] Explanation of each function

[0485] 1. User authentication and project creation

[0486] Users create or log in to an account on the web portal and create new projects that utilize computing resources. The server authenticates user information and stores project information.

[0487] 2. Code Upload and Analysis

[0488] The server receives the program code uploaded by the user and stores it securely. Afterward, an artificial intelligence module analyzes the code. During the analysis, bottlenecks for improving computational efficiency are identified, and opportunities for parallel processing optimization are discovered.

[0489] 3. Optimization and execution environment setup

[0490] Based on the analysis results, the server proposes optimizations and automatically optimizes the code after user approval. The user then configures the execution environment and prepares it for execution in a high-performance computing environment.

[0491] 4. Execution and Monitoring

[0492] The server executes optimized programs in the specified computing environment. Users can monitor job progress in real time and intervene as needed.

[0493] 5. Results Acquisition and Analysis

[0494] After execution is complete, the server stores the results in the user's project area, which the user can then use for further analysis.

[0495] Specific example

[0496] For example, consider a large-scale simulation based on weather data. The user creates a project and uploads the simulation program. The server analyzes the program and applies parallel processing to improve computation speed. The user specifies the necessary computing resources and instructs the server to run the simulation in a high-performance computing environment. As a result, the rapidly processed prediction data can be downloaded and used for further research.

[0497] This invention provides an environment in which anyone can perform advanced calculations without requiring the specialized knowledge or complex settings that were previously necessary.

[0498] The following describes the processing flow.

[0499] Step 1:

[0500] The user accesses the web portal from their device and authenticates themselves by entering their email address and password on the login page. The server receives the user's authentication information, verifies it against the database, and completes the login.

[0501] Step 2:

[0502] The user selects the "Create New Project" option from the dashboard, enters a project name and description, and creates the project. The server accepts this, registers the project in the database, and stores the related information.

[0503] Step 3:

[0504] Users upload program code from the project page. Once the selected file is sent from the terminal to the server, the server receives the file, stores it securely, and performs appropriate security checks.

[0505] Step 4:

[0506] The server sends the stored program code to an internal artificial intelligence module. The AI ​​analyzes the code and identifies opportunities for parallel processing and optimization of the programming structure. The analysis results are presented to the user as feedback.

[0507] Step 5:

[0508] The user reviews the optimization suggestions provided by the server and chooses whether to apply them. If the user approves the optimization, the server automatically optimizes the program code and prepares it for execution.

[0509] Step 6:

[0510] On the execution environment settings page, the user specifies resource requirements such as the number of CPU cores and memory size. Based on this, the server selects a high-performance computing environment and reserves the necessary resources.

[0511] Step 7:

[0512] The server begins executing optimized programs on the reserved computing environment. During this time, users can monitor the job progress and resource usage in real time from a dashboard.

[0513] Step 8:

[0514] Once the calculation is complete, the server stores the result data in the project's storage and sends a completion notification to the user. The user can then download the results from their device and use them for further analysis and reporting.

[0515] (Example 1)

[0516] 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."

[0517] With the advancement of modern science and technology, there is a growing demand for highly efficient processing of complex and large-scale data. However, utilizing high-performance computing environments requires specialized knowledge and complex environment setup, which places a burden on users. Furthermore, optimizing code and effectively allocating resources to maximize computational efficiency remain challenges. To solve these problems, there is a need for systems that allow users to easily access high-performance computing resources and enable efficient data processing.

[0518] 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.

[0519] In this invention, the server includes means for authenticating a user based on input information from a terminal, means for receiving and storing code for providing computing resources uploaded by the user, means for analyzing the stored code using a generation AI model and performing optimization to improve computing efficiency, and means for executing the optimized code in a high-performance data processing environment. As a result, users can efficiently and dynamically perform complex data processing using a high-performance computing environment without requiring specialized knowledge.

[0520] A "terminal" is a device used by a user to input data and connect to a system.

[0521] "Input information" refers to data or instructions that a user provides to the system through their device.

[0522] "User" refers to an individual or group that uses the system to perform calculations or data processing.

[0523] A "server" is a central device that processes requests from terminals via a network and performs data storage and calculations.

[0524] "Authentication" refers to the procedure for confirming that a user is a legitimate user.

[0525] "Computational resources" refers to the software and hardware configurations used to perform specific data processing or calculations.

[0526] "Code" is a set of instructions that make up part or all of a program, and is text expressed in a format that a computer can understand and execute.

[0527] A "generative AI model" refers to a computational model designed based on machine learning algorithms and used to perform predictions and optimizations for specific problems.

[0528] "Analysis" refers to the process of analyzing data and program code to identify specific patterns and areas for efficiency improvement.

[0529] "Optimization" refers to the process of modifying code or processing processes to improve computational efficiency and resource utilization.

[0530] A "high-performance data processing environment" refers to a specific configuration of computer hardware and software designed to perform complex and large-scale calculations quickly.

[0531] "Resource allocation" refers to the process of distributing computing resources such as computing power and memory in a high-performance data processing environment to specific tasks or users.

[0532] This invention provides a system configuration that allows users to efficiently utilize a high-performance data processing environment. Users access a dedicated web portal from an internet-connected terminal. This web portal provides an interface that centrally manages user authentication, program code upload and analysis, optimization suggestions, execution environment settings, job execution and progress monitoring, and acquisition of execution results. The server authenticates the user based on input information received from the terminal and securely stores the program code uploaded by the user.

[0533] During the analysis process, the server utilizes a generative AI model to analyze the code, identifying the applicability of parallel processing and bottlenecks for performance improvement. This model employs advanced machine learning algorithms for the analysis. Based on the analysis results, the server presents the user with code optimization suggestions. The user can approve the necessary optimizations and have them automatically applied. The user also specifies the required hardware resources (e.g., CPU, GPU, memory capacity) to prepare a high-performance data processing environment.

[0534] During the execution process, the server runs the optimized program in a high-performance computing environment. Users can monitor the job progress in real time via a terminal. Job parameters can be dynamically adjusted as needed. Once the calculation is complete, the server saves the results to a project space accessible to the user. Users can retrieve these results and perform further data analysis.

[0535] As a concrete example, if a user wants to perform a large-scale simulation using weather data, they would create a dedicated program and upload it to a web portal. The server would analyze this program and suggest an appropriate parallel processing method. The user would then select the necessary computing resources and instruct the execution. The resulting forecast data could then be downloaded and used for further research and analysis.

[0536] An example of a prompt message could be: "Explain how to optimize a program for weather simulations and run it in a high-performance computing environment." This system provides an environment where advanced data processing can be performed efficiently even without specialized knowledge.

[0537] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0538] Step 1:

[0539] The user accesses the web portal through their device and enters their login information. The server receives this input data and authenticates the user by comparing it with existing information in the database. If this authentication process is successful, the user's My Page screen is displayed.

[0540] Step 2:

[0541] The user enters the necessary information to create a new project (project name, purpose, etc.). The server saves this information, generates a new project ID, and registers it in the database. After the project is created, the user is moved to the code upload screen.

[0542] Step 3:

[0543] The user selects the program code they want to run from their terminal and uploads it to the server via a web portal. The server securely stores the code in storage and then prepares to begin the analysis process.

[0544] Step 4:

[0545] The server analyzes the stored program code using a generation AI model. This analysis examines the code structure and identifies bottlenecks and opportunities for parallel processing to improve computation speed. As a result of the analysis, optimization suggestions are generated.

[0546] Step 5:

[0547] The server notifies the user of optimization suggestions based on the analysis results. The user reviews the suggestions via their terminal and approves or modifies them as needed. After approval, the server automatically applies the optimizations to the program code.

[0548] Step 6:

[0549] The user configures the execution conditions (CPU, memory, GPU, etc.) for a high-performance data processing environment at the terminal. The server dynamically allocates the necessary resources based on the entered conditions. This configuration is reflected in the execution plan.

[0550] Step 7:

[0551] The server executes optimized programs in a specified high-performance data processing environment. During this execution process, the terminal monitors and displays the job progress in real time, providing the user with information on progress and resource usage.

[0552] Step 8:

[0553] Once data processing is complete, the server saves the results to the user's project area. The user can then retrieve this data via their terminal for further analysis and use. This data can then provide new insights.

[0554] (Application Example 1)

[0555] 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."

[0556] In the operation of automated machinery in the industrial sector, operational efficiency and route optimization are crucial. However, in real-world factory environments, it is difficult to flexibly respond to complex operating schedules and dynamic changes in resources. Furthermore, while there is a demand for faster and more accurate optimization processes, conventional systems often require specialized knowledge and additional configurations, limiting their use. There is a need to solve these problems and efficiently advance factory automation.

[0557] 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.

[0558] In this invention, the server includes means for authenticating a user based on input information from a terminal, means for receiving and storing a program for providing computing resources uploaded by the user, and means for analyzing the stored program and performing optimization to improve computing efficiency. This makes it possible to optimize the operating schedule of industrial machinery in a factory and improve operational efficiency.

[0559] A "terminal" is a device used by a user to input information and connect to a server via a network.

[0560] "User authentication" refers to the process by which a server verifies the user's identity and permissions based on information transmitted from the device.

[0561] A "program for providing computing resources" is software that a user provides to a server in order to efficiently perform a specific computing task.

[0562] "Optimization methods" refer to the process of automatically analyzing and correcting computational programs in order to improve their efficiency and performance.

[0563] A "high-performance computing environment" is a computing infrastructure designed to perform large-scale computational processing at high speed.

[0564] "Means of providing optimized route information to industrial machinery in real time" refers to the process of optimizing machine operation within a factory and generating and instructing efficient routes.

[0565] "Means of making execution results available to the user" refers to the process of making the results accessible and usable by the user after the calculation is completed.

[0566] "Evaluating the potential of parallel processing and automatically performing optimization" is a process that analyzes the ability of multiple processors to process a single computational task simultaneously and automatically adjusts it to maximize efficiency.

[0567] "Dynamically adjusting resource allocation and optimizing the operating schedule of industrial machinery" refers to the process of changing the allocation of computing resources according to the situation and efficiently restructuring the machine operation plan.

[0568] The system implementing this invention consists of terminals, servers, and a network to provide a high-performance computing environment. Users first access a dedicated web portal through their personal terminal and begin operations. The terminal is an internet-connected device.

[0569] The server authenticates users based on their input. Authenticated users are granted access to the system and can utilize its various functions. Programs uploaded by users are accepted by the server and securely stored. After storage, the server analyzes the program using a generative AI model and optimizes the results to improve computational efficiency. Here, TensorFlow is used as the AI ​​module to evaluate the potential of parallel processing.

[0570] Furthermore, the server dynamically allocates resources from a high-performance computing environment to execute programs according to user requests. During this process, calculation results are fed back to industrial machinery in real time, optimizing the factory's operational schedule. This supports the efficient operation of the factory.

[0571] A concrete example is the operation management of robots in an automotive parts manufacturing line. The server presents the operation route in the form of prompt messages based on the operating status data. In the format of "Analyze the data for robot path optimization and generate the next work instruction. Propose the optimal route and movement procedure from the current work point A to target point B," continuous optimization is performed using a generated AI model. Through this process, the user can achieve efficient production activities.

[0572] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0573] Step 1:

[0574] The user accesses a dedicated web portal using their personal device and enters their authentication information on the login screen. The server receives this information, compares it with the database, and authenticates the user. If authentication is successful, the user can proceed to the next step. The input is the user's authentication information, and the output is the granting of user privileges.

[0575] Step 2:

[0576] The user uses a terminal to upload the program code they want to perform calculations on to the server via a web portal. The server receives the code, saves it, and prepares it for analysis. The input is the program code, and the output is the saved program data. Here, files are received and saved to secure storage.

[0577] Step 3:

[0578] The server analyzes saved programs using a generation AI model to identify bottlenecks in the code. It analyzes the possibility of parallel processing in the program and proposes necessary optimizations. In this process, TensorFlow is used to reduce the computational load and maximize code efficiency. The input is saved program data, and the output is optimization proposals and analysis results.

[0579] Step 4:

[0580] The user reviews and approves the optimization proposals provided by the server. After approval, the server automatically optimizes the program and prepares it for execution in a high-performance computing environment. The input is the user's approval and optimization proposals, and the output is the optimized program.

[0581] Step 5:

[0582] The server executes an optimized program and generates real-time path information required for industrial machinery. The generated data is provided to robots in the factory in real time. The input is the optimized program, and the output is the generated path information. In this step, calculations are performed, and the robot's motion instructions are finalized.

[0583] 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.

[0584] This invention improves the user experience by incorporating a system that not only allows users to easily utilize a high-performance computing environment, but also by adding a function that recognizes and responds to the user's emotions. Specific embodiments of the system are described below.

[0585] System-wide configuration

[0586] Users access a dedicated web portal from an internet-connected device. In addition to basic functions such as user authentication, project creation, code upload, execution environment configuration, job execution, and result confirmation, the web portal also features an emotion engine that recognizes the user's emotions.

[0587] Detailed explanation of each function

[0588] 1. User authentication and project creation

[0589] Users create or log in to an account on the web portal and create a new project. The server authenticates the user's information and securely stores the project information. From this stage, the emotion engine reads the user's emotion data and optimizes the system interface response.

[0590] 2. Code Upload and Analysis

[0591] The user uploads program code to the project they have created. The server saves the received program and analyzes it using an artificial intelligence module. At this time, the emotion engine can sense the user's stress and anxiety and provide appropriate feedback.

[0592] 3. Optimization and execution environment setup

[0593] Based on the code analysis results, the server presents optimization suggestions to the user, and optimizes the code after obtaining approval. The user configures the execution environment, and the emotion engine detects the user's preferences and fatigue during this process, and makes configuration suggestions to the user.

[0594] 4. Execution and Monitoring

[0595] The server executes optimized programs in the computing environment. Users check the progress of jobs from a dashboard. During this time, the emotion engine monitors the user's emotions in real time and adjusts the interface and notifications as needed.

[0596] 5. Results Acquisition and Analysis

[0597] Once execution is complete, the server stores the results in the user's project area. The user downloads the result data and performs analysis. The emotion engine collects the user's emotional feedback and uses it to improve future system operations.

[0598] Specific example

[0599] For example, when a data science researcher performs large-scale data analysis, the user creates a project and uploads code. The server, through an emotion engine, senses the user's stress level and displays guidance to make the process smoother. This allows the user to work efficiently and comfortably.

[0600] This configuration not only allows for the use of a high-performance computing environment, but also provides user-friendly interactions, thereby improving both computational efficiency and user experience.

[0601] The following describes the processing flow.

[0602] Step 1:

[0603] The user accesses the system through their device and attempts to log in to their account on the web portal. The server receives the authentication information entered by the user and completes the authentication process by comparing it with the database.

[0604] Step 2:

[0605] When a user logs into the system, a dashboard is displayed on their terminal. The user enters the necessary information to create a new project and registers the project. The server stores the project in a database and manages it by assigning a project ID to the user.

[0606] Step 3:

[0607] Users access the project page from their device and upload the program code they want to run. The server securely receives this code, stores it in storage, and performs security checks.

[0608] Step 4:

[0609] The server sends the stored program code to a dedicated artificial intelligence module and begins analysis to improve computational efficiency. Simultaneously, the emotion engine monitors the user's input from multiple angles and evaluates their emotional state.

[0610] Step 5:

[0611] Based on the analysis results obtained from the artificial intelligence module, the server makes optimization suggestions to the user. The user reviews the suggestions on the screen and chooses whether to allow the optimization. At this point, the emotion engine provides feedback tailored to the user's emotions.

[0612] Step 6:

[0613] The user enters the necessary resource requirements from their terminal and configures the execution environment. The server dynamically allocates high-performance computing resources based on the entered information. The emotion engine continues to monitor emotional responses to the input operations.

[0614] Step 7:

[0615] The server runs programs optimized for a high-performance computing environment and collects data in real time while jobs are running. Users monitor progress and system performance from a dashboard on their terminals. If the emotion engine detects abnormal emotions, the server notifies the user.

[0616] Step 8:

[0617] Once the job is complete, the server stores the results in the user's project area. The user downloads the results from their terminal and begins analysis. The sentiment engine receives user feedback and uses it to improve future system responses.

[0618] (Example 2)

[0619] 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."

[0620] When using high-performance computing environments, the burden on users increases due to the setting up and execution of complex calculation processes, making efficient use of computing power difficult. Furthermore, conventional systems lack support that takes into account the user's emotions and fatigue levels, and thus have the problem of not adequately considering the quality of the user experience.

[0621] 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.

[0622] In this invention, the server includes means for authenticating an individual based on input information from a terminal device, means for receiving and storing a program uploaded by the individual to provide a computing device, means for analyzing the stored program and optimizing it to improve computing efficiency, and emotion recognition means for detecting the individual's emotional state and optimizing the system's operation and interface. This makes it possible to efficiently utilize a high-performance computing environment while taking the user's emotions into consideration.

[0623] A "terminal device" is an electronic device used by users to input information and access a system.

[0624] "Individual" refers to a user who utilizes the system and manages computing resources and programs.

[0625] A "computational device" is a part of a system that includes programs and hardware for performing high-performance calculations.

[0626] "Saving" refers to the process of accumulating and storing uploaded data and information in a database or storage device.

[0627] "Analysis" is the process of examining program code in detail and evaluating its operation and structure.

[0628] "Optimization" is the process of improving the structure and execution method of a program in order to increase computational efficiency.

[0629] "Intelligent function" refers to automated processing and learning using artificial intelligence technology.

[0630] "Emotion recognition means" refers to technology that detects the user's emotional state and adjusts the system's operation and interface based on that.

[0631] "Resource allocation" is the operation of efficiently distributing necessary resources within a computing environment.

[0632] This invention provides a system that allows users to effectively and efficiently utilize a high-performance computing environment. The system provides a dedicated web portal that users access through a terminal device. Through this portal, users can input, save, and execute program code for using the computing device.

[0633] The terminal device receives input data from the user and sends it to the server. After the user logs into the web portal and uploads program code, the server receives this data and securely stores it in storage. Generative AI models are used for program analysis and optimization, and the possibility of parallel processing is evaluated to improve efficiency. This maximizes the computational efficiency when the code is executed. The optimized program is then run on the server in a high-performance computing environment.

[0634] By incorporating emotion recognition capabilities, the server can detect the user's emotional state in real time and dynamically adjust the system interface to improve the user experience. For example, if a user is performing large-scale data analysis in a research project, the server can sense the user's tension during the analysis and display a relaxing suggestion message on the screen. In this way, incorporating emotion recognition aims to reduce user stress and provide a smooth and comfortable work environment.

[0635] An example of a prompt message is: "In a data science project, we want to perform analysis on a large dataset. Please use a system that can recognize emotions and provide appropriate guidance to set up the optimal computing environment and monitor progress."

[0636] This allows users to maximize their computing resources through the system while reducing psychological stress during computation.

[0637] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0638] Step 1:

[0639] The user accesses the web portal from their device and enters their login information. The device sends this information to the server. The server consults its database and authenticates the user based on the entered information. If authentication is successful, the user is granted access permissions and redirected to the project creation screen.

[0640] Step 2:

[0641] When a user creates a project, they select a program code file via their terminal and upload it to the server. The server accepts the file and saves it to storage. The saved program code is then analyzed by a generated AI model on the server. The input code is evaluated for its structure and potential for optimization, and an analysis report is generated as output.

[0642] Step 3:

[0643] The user reviews the proposed code optimizations based on the analysis report. The server prepares to execute the proposed optimization actions and awaits user approval. Once the user approves the optimizations, the server optimizes the program code. Based on the input approval information, the program structure is improved, and optimized code is generated as output.

[0644] Step 4:

[0645] The user uses a terminal to select configuration options for the execution environment, including the selection of necessary libraries and computing resources. The server uses emotion recognition to evaluate the user's emotional state in real time based on their selections. If the emotional state is determined to be unstable, the UI displays recommended settings and advice for relaxation.

[0646] Step 5:

[0647] Once ready, the user presses the run button to execute the optimized program in a high-performance computing environment. The server allocates computing resources and enables parallel processing. During execution, the server monitors the job's progress and outputs it to the dashboard in real time. The terminal displays this information to the user and provides notifications as needed.

[0648] Step 6:

[0649] Once the job execution is complete, the server generates result data and saves it to the user's project area. The user downloads the results using a terminal and performs a detailed analysis. During this process, the sentiment recognition function collects user feedback, which is used to further improve the system.

[0650] (Application Example 2)

[0651] 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."

[0652] Many high-performance computing systems have challenges that make it difficult for users to efficiently utilize the computing environment, particularly a lack of support that takes into account the user's emotions and stress during computation. As a result, user productivity and comfort have not been sufficiently improved.

[0653] 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.

[0654] In this invention, the server includes means for authenticating a user based on input information from a terminal, means for receiving and storing processing instructions for providing computing resources uploaded by the user, means for analyzing the stored processing instructions and optimizing them to improve computing efficiency, means for executing the optimized processing instructions in a high-performance computing environment, means for making the execution results available to the user, and means for determining the user's emotional state using emotion detection and dynamically adjusting the operating interface. This makes it possible to construct an efficient computing environment that takes into account the user's emotions during computing work.

[0655] A "terminal" is a computing device used by users to input information.

[0656] "Input information" refers to data or instructions that a user provides to the system via a terminal.

[0657] A "user" is an individual or group that uses the system and utilizes its computing resources and functions.

[0658] "Authentication means" refers to methods or devices used to verify the user's identity and ensure their eligibility to use the service.

[0659] "Computational resources" refer to the computing power and storage space used by users in a high-performance computing environment.

[0660] A "processing instruction" is program code or instructions that users upload and execute in order to utilize computing resources.

[0661] "Means of storage" refers to methods or devices for storing and retaining received data or programs in a memory device.

[0662] "Analysis" is the process of examining saved program code and investigating its contents and characteristics.

[0663] "Optimization" is a technique for adjusting programs and computing environments to improve the efficiency and speed of calculations.

[0664] A "high-performance computing environment" is an advanced computing system capable of performing large-scale data processing and calculations.

[0665] "Means for detecting emotions" refers to methods or devices for recognizing and judging emotions from a user's facial expressions, voice, etc.

[0666] An "operation interface" refers to the user interface, such as screens and menus, that users use when interacting with a system.

[0667] "Means of dynamic adjustment" refers to methods or devices that automatically change system settings and display content according to the user's state or environment.

[0668] In the system for implementing this invention, the program is designed so that the server, terminal, and user elements work together in coordination. The server receives input information from the user's terminal and authenticates the user. This authentication process ensures appropriate permissions and guarantees access to computing resources. Once authentication is complete, the user uploads processing instructions to the server to provide computing resources. The server accepts these processing instructions and stores them in its memory.

[0669] Subsequently, the server uses an AI module to analyze the processing instructions and optimize them to improve computational efficiency. For example, it can utilize the Microsoft Emotion API to detect the user's emotional state and provide feedback based on the progress of the task. After the optimization process is complete, the server executes the improved processing instructions in a high-performance computing environment and provides the results to the user. In this process, data analysis can be performed using machine learning algorithms, such as those using TensorFlow.

[0670] As a concrete example, when a user performing data analysis in a factory submits a calculation task to the system, the server recognizes their emotions, and if stress is detected, it modifies the interface to simplify the operation. Furthermore, by entering a prompt such as, "I want to design an app that senses the stress and fatigue of factory workers. Please tell me how to use AI," the system will suggest an effective AI-powered solution.

[0671] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0672] Step 1:

[0673] The server receives input information from the terminal and authenticates the user. This input information includes the user's ID and password. The authentication process verifies the user information against the database to confirm that the user has legitimate authority. If authentication is successful, the user can log in to the system.

[0674] Step 2:

[0675] The user uploads processing instructions from their terminal to the server to provide computing resources. These instructions are, for example, program code for data analysis. The server receives them and securely stores them in its storage device.

[0676] Step 3:

[0677] The server analyzes stored processing instructions using an AI module. This analysis evaluates, for example, the possibility of parallel processing to efficiently execute the code. Processing instructions are used as input, and optimization suggestions and execution plans are generated as output. An AI model (e.g., a generative AI model) assists this analysis.

[0678] Step 4:

[0679] The server optimizes processing instructions based on the optimization suggestions obtained through analysis. This optimization aims to speed up calculations and make efficient use of resources. The optimized code is output and ready for execution in a high-performance computing environment.

[0680] Step 5:

[0681] The server executes optimized processing instructions in a high-performance computing environment. Here, the necessary computing resources are dynamically allocated. Optimized code is used as input, and the computational results are obtained as output.

[0682] Step 6:

[0683] During operation, the server monitors the user's emotions in real time using means to detect emotions. For example, it analyzes data from cameras and microphones to determine if the user is experiencing stress. This information is used to adjust the user interface or suggest breaks.

[0684] Step 7:

[0685] After processing is complete, the user retrieves the calculation results via their terminal. The server saves the results to a database, allowing the user to view them immediately. The output data can then be used by the user for further analysis and decision-making.

[0686] 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.

[0687] 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.

[0688] 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.

[0689] [Fourth Embodiment]

[0690] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0691] 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.

[0692] 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).

[0693] 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.

[0694] 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.

[0695] 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).

[0696] 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.

[0697] 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.

[0698] 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.

[0699] 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.

[0700] 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.

[0701] 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.

[0702] 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".

[0703] This invention provides a system that allows users to easily utilize a high-performance computing environment. Specific embodiments of the system are described below.

[0704] System Overview

[0705] Users access a dedicated web portal from an internet-connected device. The web portal is an interface that consistently provides functions such as user authentication, project creation, code upload, execution environment configuration, job execution, and result confirmation.

[0706] Explanation of each function

[0707] 1. User authentication and project creation

[0708] Users create or log in to an account on the web portal and create new projects that utilize computing resources. The server authenticates user information and stores project information.

[0709] 2. Code Upload and Analysis

[0710] The server receives the program code uploaded by the user and stores it securely. Afterward, an artificial intelligence module analyzes the code. During the analysis, bottlenecks for improving computational efficiency are identified, and opportunities for parallel processing optimization are discovered.

[0711] 3. Optimization and execution environment setup

[0712] Based on the analysis results, the server proposes optimizations and automatically optimizes the code after user approval. The user then configures the execution environment and prepares it for execution in a high-performance computing environment.

[0713] 4. Execution and Monitoring

[0714] The server executes optimized programs in the specified computing environment. Users can monitor job progress in real time and intervene as needed.

[0715] 5. Results Acquisition and Analysis

[0716] After execution is complete, the server stores the results in the user's project area, which the user can then use for further analysis.

[0717] Specific example

[0718] For example, consider a large-scale simulation based on weather data. The user creates a project and uploads the simulation program. The server analyzes the program and applies parallel processing to improve computation speed. The user specifies the necessary computing resources and instructs the server to run the simulation in a high-performance computing environment. As a result, the rapidly processed prediction data can be downloaded and used for further research.

[0719] This invention provides an environment in which anyone can perform advanced calculations without requiring the specialized knowledge or complex settings that were previously necessary.

[0720] The following describes the processing flow.

[0721] Step 1:

[0722] The user accesses the web portal from their device and authenticates themselves by entering their email address and password on the login page. The server receives the user's authentication information, verifies it against the database, and completes the login.

[0723] Step 2:

[0724] The user selects the "Create New Project" option from the dashboard, enters a project name and description, and creates the project. The server accepts this, registers the project in the database, and stores the related information.

[0725] Step 3:

[0726] Users upload program code from the project page. Once the selected file is sent from the terminal to the server, the server receives the file, stores it securely, and performs appropriate security checks.

[0727] Step 4:

[0728] The server sends the stored program code to an internal artificial intelligence module. The AI ​​analyzes the code and identifies opportunities for parallel processing and optimization of the programming structure. The analysis results are presented to the user as feedback.

[0729] Step 5:

[0730] The user reviews the optimization suggestions provided by the server and chooses whether to apply them. If the user approves the optimization, the server automatically optimizes the program code and prepares it for execution.

[0731] Step 6:

[0732] On the execution environment settings page, the user specifies resource requirements such as the number of CPU cores and memory size. Based on this, the server selects a high-performance computing environment and reserves the necessary resources.

[0733] Step 7:

[0734] The server begins executing optimized programs on the reserved computing environment. During this time, users can monitor the job progress and resource usage in real time from a dashboard.

[0735] Step 8:

[0736] Once the calculation is complete, the server stores the result data in the project's storage and sends a completion notification to the user. The user can then download the results from their device and use them for further analysis and reporting.

[0737] (Example 1)

[0738] 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".

[0739] With the advancement of modern science and technology, there is a growing demand for highly efficient processing of complex and large-scale data. However, utilizing high-performance computing environments requires specialized knowledge and complex environment setup, which places a burden on users. Furthermore, optimizing code and effectively allocating resources to maximize computational efficiency remain challenges. To solve these problems, there is a need for systems that allow users to easily access high-performance computing resources and enable efficient data processing.

[0740] 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.

[0741] In this invention, the server includes means for authenticating a user based on input information from a terminal, means for receiving and storing code for providing computing resources uploaded by the user, means for analyzing the stored code using a generation AI model and performing optimization to improve computing efficiency, and means for executing the optimized code in a high-performance data processing environment. As a result, users can efficiently and dynamically perform complex data processing using a high-performance computing environment without requiring specialized knowledge.

[0742] A "terminal" is a device used by a user to input data and connect to a system.

[0743] "Input information" refers to data or instructions that a user provides to the system through their device.

[0744] "User" refers to an individual or group that uses the system to perform calculations or data processing.

[0745] A "server" is a central device that processes requests from terminals via a network and performs data storage and calculations.

[0746] "Authentication" refers to the procedure for confirming that a user is a legitimate user.

[0747] "Computational resources" refers to the software and hardware configurations used to perform specific data processing or calculations.

[0748] "Code" is a set of instructions that make up part or all of a program, and is text expressed in a format that a computer can understand and execute.

[0749] A "generative AI model" refers to a computational model designed based on machine learning algorithms and used to perform predictions and optimizations for specific problems.

[0750] "Analysis" refers to the process of analyzing data and program code to identify specific patterns and areas for efficiency improvement.

[0751] "Optimization" refers to the process of modifying code or processing processes to improve computational efficiency and resource utilization.

[0752] A "high-performance data processing environment" refers to a specific configuration of computer hardware and software designed to perform complex and large-scale calculations quickly.

[0753] "Resource allocation" refers to the process of distributing computing resources such as computing power and memory in a high-performance data processing environment to specific tasks or users.

[0754] This invention provides a system configuration that allows users to efficiently utilize a high-performance data processing environment. Users access a dedicated web portal from an internet-connected terminal. This web portal provides an interface that centrally manages user authentication, program code upload and analysis, optimization suggestions, execution environment settings, job execution and progress monitoring, and acquisition of execution results. The server authenticates the user based on input information received from the terminal and securely stores the program code uploaded by the user.

[0755] During the analysis process, the server utilizes a generative AI model to analyze the code, identifying the applicability of parallel processing and bottlenecks for performance improvement. This model employs advanced machine learning algorithms for the analysis. Based on the analysis results, the server presents the user with code optimization suggestions. The user can approve the necessary optimizations and have them automatically applied. The user also specifies the required hardware resources (e.g., CPU, GPU, memory capacity) to prepare a high-performance data processing environment.

[0756] During the execution process, the server runs the optimized program in a high-performance computing environment. Users can monitor the job progress in real time via a terminal. Job parameters can be dynamically adjusted as needed. Once the calculation is complete, the server saves the results to a project space accessible to the user. Users can retrieve these results and perform further data analysis.

[0757] As a concrete example, if a user wants to perform a large-scale simulation using weather data, they would create a dedicated program and upload it to a web portal. The server would analyze this program and suggest an appropriate parallel processing method. The user would then select the necessary computing resources and instruct the execution. The resulting forecast data could then be downloaded and used for further research and analysis.

[0758] An example of a prompt message could be: "Explain how to optimize a program for weather simulations and run it in a high-performance computing environment." This system provides an environment where advanced data processing can be performed efficiently even without specialized knowledge.

[0759] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0760] Step 1:

[0761] The user accesses the web portal through their device and enters their login information. The server receives this input data and authenticates the user by comparing it with existing information in the database. If this authentication process is successful, the user's My Page screen is displayed.

[0762] Step 2:

[0763] The user enters the necessary information to create a new project (project name, purpose, etc.). The server saves this information, generates a new project ID, and registers it in the database. After the project is created, the user is moved to the code upload screen.

[0764] Step 3:

[0765] The user selects the program code they want to run from their terminal and uploads it to the server via a web portal. The server securely stores the code in storage and then prepares to begin the analysis process.

[0766] Step 4:

[0767] The server analyzes the stored program code using a generation AI model. This analysis examines the code structure and identifies bottlenecks and opportunities for parallel processing to improve computation speed. As a result of the analysis, optimization suggestions are generated.

[0768] Step 5:

[0769] The server notifies the user of optimization suggestions based on the analysis results. The user reviews the suggestions via their terminal and approves or modifies them as needed. After approval, the server automatically applies the optimizations to the program code.

[0770] Step 6:

[0771] The user configures the execution conditions (CPU, memory, GPU, etc.) for a high-performance data processing environment at the terminal. The server dynamically allocates the necessary resources based on the entered conditions. This configuration is reflected in the execution plan.

[0772] Step 7:

[0773] The server executes optimized programs in a specified high-performance data processing environment. During this execution process, the terminal monitors and displays the job progress in real time, providing the user with information on progress and resource usage.

[0774] Step 8:

[0775] Once data processing is complete, the server saves the results to the user's project area. The user can then retrieve this data via their terminal for further analysis and use. This data can then provide new insights.

[0776] (Application Example 1)

[0777] 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".

[0778] In the operation of automated machinery in the industrial sector, operational efficiency and route optimization are crucial. However, in real-world factory environments, it is difficult to flexibly respond to complex operating schedules and dynamic changes in resources. Furthermore, while there is a demand for faster and more accurate optimization processes, conventional systems often require specialized knowledge and additional configurations, limiting their use. There is a need to solve these problems and efficiently advance factory automation.

[0779] 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.

[0780] In this invention, the server includes means for authenticating a user based on input information from a terminal, means for receiving and storing a program for providing computing resources uploaded by the user, and means for analyzing the stored program and performing optimization to improve computing efficiency. This makes it possible to optimize the operating schedule of industrial machinery in a factory and improve operational efficiency.

[0781] A "terminal" is a device used by a user to input information and connect to a server via a network.

[0782] "User authentication" refers to the process by which a server verifies the user's identity and permissions based on information transmitted from the device.

[0783] A "program for providing computing resources" is software that a user provides to a server in order to efficiently perform a specific computing task.

[0784] "Optimization methods" refer to the process of automatically analyzing and correcting computational programs in order to improve their efficiency and performance.

[0785] A "high-performance computing environment" is a computing infrastructure designed to perform large-scale computational processing at high speed.

[0786] "Means of providing optimized route information to industrial machinery in real time" refers to the process of optimizing machine operation within a factory and generating and instructing efficient routes.

[0787] "Means of making execution results available to the user" refers to the process of making the results accessible and usable by the user after the calculation is completed.

[0788] "Evaluating the potential of parallel processing and automatically performing optimization" is a process that analyzes the ability of multiple processors to process a single computational task simultaneously and automatically adjusts it to maximize efficiency.

[0789] "Dynamically adjusting resource allocation and optimizing the operating schedule of industrial machinery" refers to the process of changing the allocation of computing resources according to the situation and efficiently restructuring the machine operation plan.

[0790] The system implementing this invention consists of terminals, servers, and a network to provide a high-performance computing environment. Users first access a dedicated web portal through their personal terminal and begin operations. The terminal is an internet-connected device.

[0791] The server authenticates users based on their input. Authenticated users are granted access to the system and can utilize its various functions. Programs uploaded by users are accepted by the server and securely stored. After storage, the server analyzes the program using a generative AI model and optimizes the results to improve computational efficiency. Here, TensorFlow is used as the AI ​​module to evaluate the potential of parallel processing.

[0792] Furthermore, the server dynamically allocates resources from a high-performance computing environment to execute programs according to user requests. During this process, calculation results are fed back to industrial machinery in real time, optimizing the factory's operational schedule. This supports the efficient operation of the factory.

[0793] A concrete example is the operation management of robots in an automotive parts manufacturing line. The server presents the operation route in the form of prompt messages based on the operating status data. In the format of "Analyze the data for robot path optimization and generate the next work instruction. Propose the optimal route and movement procedure from the current work point A to target point B," continuous optimization is performed using a generated AI model. Through this process, the user can achieve efficient production activities.

[0794] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0795] Step 1:

[0796] The user accesses a dedicated web portal using their personal device and enters their authentication information on the login screen. The server receives this information, compares it with the database, and authenticates the user. If authentication is successful, the user can proceed to the next step. The input is the user's authentication information, and the output is the granting of user privileges.

[0797] Step 2:

[0798] The user uses a terminal to upload the program code they want to perform calculations on to the server via a web portal. The server receives the code, saves it, and prepares it for analysis. The input is the program code, and the output is the saved program data. Here, files are received and saved to secure storage.

[0799] Step 3:

[0800] The server analyzes saved programs using a generation AI model to identify bottlenecks in the code. It analyzes the possibility of parallel processing in the program and proposes necessary optimizations. In this process, TensorFlow is used to reduce the computational load and maximize code efficiency. The input is saved program data, and the output is optimization proposals and analysis results.

[0801] Step 4:

[0802] The user reviews and approves the optimization proposals provided by the server. After approval, the server automatically optimizes the program and prepares it for execution in a high-performance computing environment. The input is the user's approval and optimization proposals, and the output is the optimized program.

[0803] Step 5:

[0804] The server executes an optimized program and generates real-time path information required for industrial machinery. The generated data is provided to robots in the factory in real time. The input is the optimized program, and the output is the generated path information. In this step, calculations are performed, and the robot's motion instructions are finalized.

[0805] 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.

[0806] This invention improves the user experience by incorporating a system that not only allows users to easily utilize a high-performance computing environment, but also by adding a function that recognizes and responds to the user's emotions. Specific embodiments of the system are described below.

[0807] System-wide configuration

[0808] Users access a dedicated web portal from an internet-connected device. In addition to basic functions such as user authentication, project creation, code upload, execution environment configuration, job execution, and result confirmation, the web portal also features an emotion engine that recognizes the user's emotions.

[0809] Detailed explanation of each function

[0810] 1. User authentication and project creation

[0811] Users create or log in to an account on the web portal and create a new project. The server authenticates the user's information and securely stores the project information. From this stage, the emotion engine reads the user's emotion data and optimizes the system interface response.

[0812] 2. Code Upload and Analysis

[0813] The user uploads program code to the project they have created. The server saves the received program and analyzes it using an artificial intelligence module. At this time, the emotion engine can sense the user's stress and anxiety and provide appropriate feedback.

[0814] 3. Optimization and execution environment setup

[0815] Based on the code analysis results, the server presents optimization suggestions to the user, and optimizes the code after obtaining approval. The user configures the execution environment, and the emotion engine detects the user's preferences and fatigue during this process, and makes configuration suggestions to the user.

[0816] 4. Execution and Monitoring

[0817] The server executes optimized programs in the computing environment. Users check the progress of jobs from a dashboard. During this time, the emotion engine monitors the user's emotions in real time and adjusts the interface and notifications as needed.

[0818] 5. Results Acquisition and Analysis

[0819] Once execution is complete, the server stores the results in the user's project area. The user downloads the result data and performs analysis. The emotion engine collects the user's emotional feedback and uses it to improve future system operations.

[0820] Specific example

[0821] For example, when a data science researcher performs large-scale data analysis, the user creates a project and uploads code. The server, through an emotion engine, senses the user's stress level and displays guidance to make the process smoother. This allows the user to work efficiently and comfortably.

[0822] This configuration not only allows for the use of a high-performance computing environment, but also provides user-friendly interactions, thereby improving both computational efficiency and user experience.

[0823] The following describes the processing flow.

[0824] Step 1:

[0825] The user accesses the system through their device and attempts to log in to their account on the web portal. The server receives the authentication information entered by the user and completes the authentication process by comparing it with the database.

[0826] Step 2:

[0827] When a user logs into the system, a dashboard is displayed on their terminal. The user enters the necessary information to create a new project and registers the project. The server stores the project in a database and manages it by assigning a project ID to the user.

[0828] Step 3:

[0829] Users access the project page from their device and upload the program code they want to run. The server securely receives this code, stores it in storage, and performs security checks.

[0830] Step 4:

[0831] The server sends the stored program code to a dedicated artificial intelligence module and begins analysis to improve computational efficiency. Simultaneously, the emotion engine monitors the user's input from multiple angles and evaluates their emotional state.

[0832] Step 5:

[0833] Based on the analysis results obtained from the artificial intelligence module, the server makes optimization suggestions to the user. The user reviews the suggestions on the screen and chooses whether to allow the optimization. At this point, the emotion engine provides feedback tailored to the user's emotions.

[0834] Step 6:

[0835] The user enters the necessary resource requirements from their terminal and configures the execution environment. The server dynamically allocates high-performance computing resources based on the entered information. The emotion engine continues to monitor emotional responses to the input operations.

[0836] Step 7:

[0837] The server runs programs optimized for a high-performance computing environment and collects data in real time while jobs are running. Users monitor progress and system performance from a dashboard on their terminals. If the emotion engine detects abnormal emotions, the server notifies the user.

[0838] Step 8:

[0839] Once the job is complete, the server stores the results in the user's project area. The user downloads the results from their terminal and begins analysis. The sentiment engine receives user feedback and uses it to improve future system responses.

[0840] (Example 2)

[0841] 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".

[0842] When using high-performance computing environments, the burden on users increases due to the setting up and execution of complex calculation processes, making efficient use of computing power difficult. Furthermore, conventional systems lack support that takes into account the user's emotions and fatigue levels, and thus have the problem of not adequately considering the quality of the user experience.

[0843] 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.

[0844] In this invention, the server includes means for authenticating an individual based on input information from a terminal device, means for receiving and storing a program uploaded by the individual to provide a computing device, means for analyzing the stored program and optimizing it to improve computing efficiency, and emotion recognition means for detecting the individual's emotional state and optimizing the system's operation and interface. This makes it possible to efficiently utilize a high-performance computing environment while taking the user's emotions into consideration.

[0845] A "terminal device" is an electronic device used by users to input information and access a system.

[0846] "Individual" refers to a user who utilizes the system and manages computing resources and programs.

[0847] A "computational device" is a part of a system that includes programs and hardware for performing high-performance calculations.

[0848] "Saving" refers to the process of accumulating and storing uploaded data and information in a database or storage device.

[0849] "Analysis" is the process of examining program code in detail and evaluating its operation and structure.

[0850] "Optimization" is the process of improving the structure and execution method of a program in order to increase computational efficiency.

[0851] "Intelligent function" refers to automated processing and learning using artificial intelligence technology.

[0852] "Emotion recognition means" refers to technology that detects the user's emotional state and adjusts the system's operation and interface based on that.

[0853] "Resource allocation" is the operation of efficiently distributing necessary resources within a computing environment.

[0854] This invention provides a system that allows users to effectively and efficiently utilize a high-performance computing environment. The system provides a dedicated web portal that users access through a terminal device. Through this portal, users can input, save, and execute program code for using the computing device.

[0855] The terminal device receives input data from the user and sends it to the server. After the user logs into the web portal and uploads program code, the server receives this data and securely stores it in storage. Generative AI models are used for program analysis and optimization, and the possibility of parallel processing is evaluated to improve efficiency. This maximizes the computational efficiency when the code is executed. The optimized program is then run on the server in a high-performance computing environment.

[0856] By incorporating emotion recognition capabilities, the server can detect the user's emotional state in real time and dynamically adjust the system interface to improve the user experience. For example, if a user is performing large-scale data analysis in a research project, the server can sense the user's tension during the analysis and display a relaxing suggestion message on the screen. In this way, incorporating emotion recognition aims to reduce user stress and provide a smooth and comfortable work environment.

[0857] An example of a prompt message is: "In a data science project, we want to perform analysis on a large dataset. Please use a system that can recognize emotions and provide appropriate guidance to set up the optimal computing environment and monitor progress."

[0858] This allows users to maximize their computing resources through the system while reducing psychological stress during computation.

[0859] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0860] Step 1:

[0861] The user accesses the web portal from their device and enters their login information. The device sends this information to the server. The server consults its database and authenticates the user based on the entered information. If authentication is successful, the user is granted access permissions and redirected to the project creation screen.

[0862] Step 2:

[0863] When a user creates a project, they select a program code file via their terminal and upload it to the server. The server accepts the file and saves it to storage. The saved program code is then analyzed by a generated AI model on the server. The input code is evaluated for its structure and potential for optimization, and an analysis report is generated as output.

[0864] Step 3:

[0865] The user reviews the proposed code optimizations based on the analysis report. The server prepares to execute the proposed optimization actions and awaits user approval. Once the user approves the optimizations, the server optimizes the program code. Based on the input approval information, the program structure is improved, and optimized code is generated as output.

[0866] Step 4:

[0867] The user uses a terminal to select configuration options for the execution environment, including the selection of necessary libraries and computing resources. The server uses emotion recognition to evaluate the user's emotional state in real time based on their selections. If the emotional state is determined to be unstable, the UI displays recommended settings and advice for relaxation.

[0868] Step 5:

[0869] Once ready, the user presses the run button to execute the optimized program in a high-performance computing environment. The server allocates computing resources and enables parallel processing. During execution, the server monitors the job's progress and outputs it to the dashboard in real time. The terminal displays this information to the user and provides notifications as needed.

[0870] Step 6:

[0871] Once the job execution is complete, the server generates result data and saves it to the user's project area. The user downloads the results using a terminal and performs a detailed analysis. During this process, the sentiment recognition function collects user feedback, which is used to further improve the system.

[0872] (Application Example 2)

[0873] 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".

[0874] Many high-performance computing systems have challenges that make it difficult for users to efficiently utilize the computing environment, particularly a lack of support that takes into account the user's emotions and stress during computation. As a result, user productivity and comfort have not been sufficiently improved.

[0875] 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.

[0876] In this invention, the server includes means for authenticating a user based on input information from a terminal, means for receiving and storing processing instructions for providing computing resources uploaded by the user, means for analyzing the stored processing instructions and optimizing them to improve computing efficiency, means for executing the optimized processing instructions in a high-performance computing environment, means for making the execution results available to the user, and means for determining the user's emotional state using emotion detection and dynamically adjusting the operating interface. This makes it possible to construct an efficient computing environment that takes into account the user's emotions during computing work.

[0877] A "terminal" is a computing device used by users to input information.

[0878] "Input information" refers to data or instructions that a user provides to the system via a terminal.

[0879] A "user" is an individual or group that uses the system and utilizes its computing resources and functions.

[0880] "Authentication means" refers to methods or devices used to verify the user's identity and ensure their eligibility to use the service.

[0881] "Computational resources" refer to the computing power and storage space used by users in a high-performance computing environment.

[0882] A "processing instruction" is program code or instructions that users upload and execute in order to utilize computing resources.

[0883] "Means of storage" refers to methods or devices for storing and retaining received data or programs in a memory device.

[0884] "Analysis" is the process of examining saved program code and investigating its contents and characteristics.

[0885] "Optimization" is a technique for adjusting programs and computing environments to improve the efficiency and speed of calculations.

[0886] A "high-performance computing environment" is an advanced computing system capable of performing large-scale data processing and calculations.

[0887] "Means for detecting emotions" refers to methods or devices for recognizing and judging emotions from a user's facial expressions, voice, etc.

[0888] An "operation interface" refers to the user interface, such as screens and menus, that users use when interacting with a system.

[0889] "Means of dynamic adjustment" refers to methods or devices that automatically change system settings and display content according to the user's state or environment.

[0890] In the system for implementing this invention, the program is designed so that the server, terminal, and user elements work together in coordination. The server receives input information from the user's terminal and authenticates the user. This authentication process ensures appropriate permissions and guarantees access to computing resources. Once authentication is complete, the user uploads processing instructions to the server to provide computing resources. The server accepts these processing instructions and stores them in its memory.

[0891] Subsequently, the server uses an AI module to analyze the processing instructions and optimize them to improve computational efficiency. For example, it can utilize the Microsoft Emotion API to detect the user's emotional state and provide feedback based on the progress of the task. After the optimization process is complete, the server executes the improved processing instructions in a high-performance computing environment and provides the results to the user. In this process, data analysis can be performed using machine learning algorithms, such as those using TensorFlow.

[0892] As a concrete example, when a user performing data analysis in a factory submits a calculation task to the system, the server recognizes their emotions, and if stress is detected, it modifies the interface to simplify the operation. Furthermore, by entering a prompt such as, "I want to design an app that senses the stress and fatigue of factory workers. Please tell me how to use AI," the system will suggest an effective AI-powered solution.

[0893] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0894] Step 1:

[0895] The server receives input information from the terminal and authenticates the user. This input information includes the user's ID and password. The authentication process verifies the user information against the database to confirm that the user has legitimate authority. If authentication is successful, the user can log in to the system.

[0896] Step 2:

[0897] The user uploads processing instructions from their terminal to the server to provide computing resources. These instructions are, for example, program code for data analysis. The server receives them and securely stores them in its storage device.

[0898] Step 3:

[0899] The server analyzes stored processing instructions using an AI module. This analysis evaluates, for example, the possibility of parallel processing to efficiently execute the code. Processing instructions are used as input, and optimization suggestions and execution plans are generated as output. An AI model (e.g., a generative AI model) assists this analysis.

[0900] Step 4:

[0901] The server optimizes processing instructions based on the optimization suggestions obtained through analysis. This optimization aims to speed up calculations and make efficient use of resources. The optimized code is output and ready for execution in a high-performance computing environment.

[0902] Step 5:

[0903] The server executes optimized processing instructions in a high-performance computing environment. Here, the necessary computing resources are dynamically allocated. Optimized code is used as input, and the computational results are obtained as output.

[0904] Step 6:

[0905] During operation, the server monitors the user's emotions in real time using means to detect emotions. For example, it analyzes data from cameras and microphones to determine if the user is experiencing stress. This information is used to adjust the user interface or suggest breaks.

[0906] Step 7:

[0907] After processing is complete, the user retrieves the calculation results via their terminal. The server saves the results to a database, allowing the user to view them immediately. The output data can then be used by the user for further analysis and decision-making.

[0908] 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.

[0909] 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.

[0910] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0911] 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.

[0912] 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.

[0913] 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.

[0914] 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.

[0915] 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.

[0916] 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."

[0917] 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.

[0918] 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.

[0919] 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.

[0920] 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.

[0921] 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.

[0922] 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.

[0923] 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.

[0924] 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.

[0925] 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.

[0926] 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.

[0927] 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.

[0928] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0929] The following is further disclosed regarding the embodiments described above.

[0930] (Claim 1)

[0931] A means of authenticating a user based on information entered from the terminal,

[0932] A means for receiving and storing programs that provide computing resources uploaded by users,

[0933] A means for analyzing saved programs and performing optimizations to improve computational efficiency,

[0934] A means of running an optimized program in a high-performance computing environment,

[0935] A means to make the execution results available to the user,

[0936] A system that includes this.

[0937] (Claim 2)

[0938] The system according to claim 1, which uses artificial intelligence to evaluate the possibility of parallel processing in program analysis and automatically performs optimization.

[0939] (Claim 3)

[0940] The system according to claim 1, which dynamically adjusts the allocation of resources in a high-performance computing environment based on user input.

[0941] "Example 1"

[0942] (Claim 1)

[0943] A means of authenticating users based on information entered from the terminal,

[0944] A means of receiving and storing code for providing computing resources uploaded by users,

[0945] A means of analyzing saved code using a generation AI model and performing optimization to improve computational efficiency,

[0946] A means of executing optimized code in a high-performance data processing environment,

[0947] A means for users to monitor the progress of the execution in real time,

[0948] A means to make the execution results available to the user,

[0949] A system that includes this.

[0950] (Claim 2)

[0951] The system according to claim 1, which, in code analysis, evaluates the possibility of parallel data processing using a generative AI model and automatically performs optimization.

[0952] (Claim 3)

[0953] The system according to claim 1, which dynamically adjusts the allocation of resources in a high-performance data processing environment based on input from a user.

[0954] "Application Example 1"

[0955] (Claim 1)

[0956] A means of authenticating a user based on information entered from the terminal,

[0957] A means for receiving and storing programs that provide computing resources uploaded by users,

[0958] A means for analyzing saved programs and performing optimizations to improve computational efficiency,

[0959] A means of running an optimized program in a high-performance computing environment,

[0960] A means of providing optimized route information for industrial machinery in real time,

[0961] A means to make the execution results available to the user,

[0962] A system that includes this.

[0963] (Claim 2)

[0964] The system according to claim 1, which uses artificial intelligence to evaluate the possibility of parallel processing in program analysis and automatically performs optimization.

[0965] (Claim 3)

[0966] The system according to claim 1, which dynamically adjusts resource allocation in a high-performance computing environment based on user input and optimizes the operating schedule of industrial machinery.

[0967] "Example 2 of combining an emotion engine"

[0968] (Claim 1)

[0969] A means of authenticating an individual based on information entered from a terminal device,

[0970] A means of receiving and storing programs for providing computing devices uploaded by individuals,

[0971] A means for analyzing saved programs and performing optimizations to improve computational efficiency,

[0972] A means of running an optimized program in a high-performance computing environment,

[0973] A means to make the execution results available to individuals,

[0974] An emotion recognition means that detects an individual's emotional state and optimizes the system's operation and interface,

[0975] A system that includes this.

[0976] (Claim 2)

[0977] The system according to claim 1, which uses intelligent functions to evaluate the possibility of parallel processing in program analysis and automatically performs optimization.

[0978] (Claim 3)

[0979] The system according to claim 1, which dynamically adjusts the allocation of resources in a high-performance computing environment based on input from an individual.

[0980] "Application example 2 of combining emotional engines"

[0981] (Claim 1)

[0982] A means of authenticating users based on information entered from the terminal,

[0983] A means for receiving and storing processing instructions for providing computing resources uploaded by users,

[0984] A means for analyzing saved processing instructions and performing optimization to improve computational efficiency,

[0985] A means for executing optimized processing instructions in a high-performance computing environment,

[0986] A means to make the execution results available to the user,

[0987] A means for detecting emotions to determine the user's emotional state and dynamically adjust the operating interface,

[0988] A system that includes this.

[0989] (Claim 2)

[0990] The system according to claim 1, which uses artificial intelligence to evaluate the possibility of parallel processing in the analysis of processing instructions and automatically performs optimization.

[0991] (Claim 3)

[0992] The system according to claim 1, which dynamically adjusts the resource allocation of a high-performance computing environment based on input from the user, and further provides notifications to alleviate the user's fatigue state using means for detecting emotions. [Explanation of Symbols]

[0993] 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 authenticating a user based on information entered from the terminal, A means for receiving and storing programs that provide computing resources uploaded by users, A means for analyzing saved programs and performing optimizations to improve computational efficiency, A means of running an optimized program in a high-performance computing environment, A means to make the execution results available to the user, A system that includes this.

2. The system according to claim 1, which uses artificial intelligence to evaluate the possibility of parallel processing in program analysis and automatically performs optimization.

3. The system according to claim 1, which dynamically adjusts the allocation of resources in a high-performance computing environment based on user input.

Images