System

Abstract

A system is provided.SOLUTION: A system comprising: means for allowing a user to associate a repository storing a source code with authentication means; means for acquiring the source code from the repository using the authentication means; means for analyzing the acquired source code to identify a programming language and a library being used; means for selecting an optimum deployment platform based on the programming language and the library; and means for automatically performing deployment on the selected deployment platform.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In the development of modern web services and web tools, the deployment process is complex and diverse, especially for individual developers and in educational settings. In particular, selecting the optimal deployment platform for achieving high performance at low cost requires detailed understanding of each platform's features, pricing plans, supported languages, and other information, and then evaluating and comparing them. This is a time-consuming and labor-intensive task, placing a burden on developers. Furthermore, the deployment process is complicated and requires technical knowledge, presenting a significant barrier, especially for beginners. It is necessary to solve this problem and provide an environment that allows developers to focus on their core development tasks. [Means for solving the problem]

[0005] To solve the above-mentioned problems, the present invention provides the following means. First, a user links a repository storing source code with an authentication means, and retrieves the source code from the repository using the authentication means. A generation AI analyzes the retrieved source code and identifies the programming languages ​​and libraries used. Next, based on the identified programming languages ​​and libraries, the optimal deployment platform is selected from multiple options. This selection involves evaluating information about each platform and choosing the one with the best cost performance. Finally, automatic deployment to the selected deployment platform significantly reduces the user's effort and makes it easy to publish products. This makes it easy for individual developers and educational institutions to deploy web services and web tools, thereby increasing development motivation.

[0006] A "repository" is a database for storing source code and entire projects and for version control.

[0007] An "authentication method" is a system or protocol for verifying a user's identity and granting appropriate access rights.

[0008] "Source code" means the original code of a computer program written in a programming language.

[0009] "Generative AI" is an artificial intelligence system that uses machine learning algorithms to analyze data and make predictions.

[0010] A "programming language" is a formal language for writing computer programs.

[0011] A "library" is a collection of reusable program code, modules, or software components that provide specific functionality.

[0012] A "deployment platform" is a service or system that provides the infrastructure for hosting and operating applications and services.

[0013] "Automated deployment" refers to the process of deploying applications and services to a deployment platform without requiring manual user intervention.

[0014] "Cost performance" is an indicator that shows the efficiency of the effects and performance obtained in relation to the costs invested. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] This invention is a system for efficient deployment of web services and web tools for individual developers and educational institutions. This system works in conjunction with a repository that stores source code, analyzes the source code, selects the optimal deployment platform, and automatically deploys the code.

[0037] System configuration

[0038] The system consists of the following main components:

[0039] 1. Authentication Methods

[0040] 2. How to obtain source code

[0041] 3. Generation AI analysis means

[0042] 4. How to Select the Optimal Deployment Platform

[0043] 5. Automated Deployment Methods

[0044] Authentication Method

[0045] User: Accesses the system's website in a browser. The user authenticates using an account in a version control system such as GitHub. OAuth 2.0 is used for authentication, which ensures secure authentication.

[0046] How to get source code

[0047] Server: Using the authentication token, the source code is cloned from the repository specified by the user. The cloned source code is temporarily stored on the server side for analysis.

[0048] Generation AI analysis means

[0049] Server: The generated AI analyzes the cloned source code, identifying the programming languages, libraries, and components (front-end, back-end, database, etc.) used within the source code.

[0050] How to select the optimal deployment platform

[0051] Server: Based on the analysis results, multiple deployment platforms are compared and evaluated from an internal database. Evaluation criteria include cost performance, supported languages, ease of deployment, etc. The optimal deployment platform is selected.

[0052] For example, if the source code is composed of Node.js and React, the server will determine that Netlify or Vercel is the best choice. Also, if MongoDB is used as the backend, it will consider using MongoDB Atlas or Heroku.

[0053] Automated deployment methods

[0054] Server: Automatically deploys to the selected deployment platform. The server uses the deployment platform's API to automatically build and deploy the source code.

[0055] Notification of deployment results

[0056] Server: Once the deployment is complete, the user is notified via email, dashboard alerts, etc.

[0057] Users: They receive a notification and can visit the provided URL to view the deployed web service or web tool.

[0058] Specific operation example

[0059] 1. A user performs practical training on the engineering education platform and pushes the completed web tool to the repository.

[0060] 2. The user visits the system's website, authenticates with GitHub, and selects the repository to deploy to.

[0061] 3. The server retrieves the repository and the generative AI analyzes the source code.

[0062] 4. The server selects the optimal deployment platform and determines it to be Netlify.

[0063] 5. The server uses the Netlify API to automatically deploy the front-end app.

[0064] 6. The user receives a notification that the deployment is complete and can confirm by visiting the provided URL.

[0065] In this way, the present invention is a system that automates the deployment process of web services and web tools, achieving low cost and high performance, thereby providing great convenience to individual developers and users in educational settings.

[0066] The processing flow will be explained below.

[0067] Step 1:

[0068] The user accesses the system's website in a browser and authenticates their GitHub account using OAuth 2.0. The user clicks the "Log in with GitHub" button and the GitHub authentication page is displayed. The user then enters their GitHub username and password to authenticate. If authentication is successful, the system obtains a GitHub authentication token.

[0069] Step 2:

[0070] The server uses the obtained authentication token to access the GitHub API and retrieve the user's repository list. The server sends a request to the GitHub API and receives the user's repository list in JSON format. The server parses this data and formats it into a format that can be displayed to the user.

[0071] Step 3:

[0072] The user selects the project they want to deploy from the displayed list of repositories. The user clicks the "Select repository to deploy to" button and enters the required configuration information (e.g., branch name, environment variables, etc.).

[0073] Step 4:

[0074] The server clones the selected repository. The server clones the repository locally using the Git command (e.g., git clone https: / / github.com / username / repositoryname.git). Once the clone is complete, the entire source code will be stored in the local directory.

[0075] Step 5:

[0076] The server uses generative AI to analyze the cloned source code. The generative AI analyzes the file structure of the source code and identifies the programming languages, libraries, and components (front-end, back-end, database, etc.) used. For example, it extracts dependencies from the package.json file and identifies the technology stack used.

[0077] Step 6:

[0078] The server selects the optimal deployment platform based on the analysis results. It retrieves multiple platform candidates from an internal database and compares them based on evaluation criteria such as cost performance, supported languages, and ease of deployment. Finally, it selects the most suitable platform.

[0079] Step 7:

[0080] The server automatically deploys to the selected deployment platform. For example, if Netlify is selected, it uses the Netlify API to build and deploy the source code, sends the necessary configuration settings (such as environment variables and build commands), and starts the deployment process.

[0081] Step 8:

[0082] The server notifies the user that the deployment is complete by sending an email, posting a notification on the dashboard, etc. The user receives the notification and accesses the provided URL to check the operation of the deployed web service or web tool.

[0083] In this way, the system provides an environment in which users can automatically deploy and easily publish web services and web tools they create at low cost and with high performance.

[0084] Example 1

[0085] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0086] Existing deployment methods for web services and web tools involve a lot of manual work, which is time-consuming and labor-intensive for individual developers and educational institutions. In addition, there is a lack of know-how to select the optimal deployment platform, which can result in high costs and low performance deployments.

[0087] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0088] In this invention, the server includes: means for linking a repository in which a user stores source code with authentication means; means for retrieving the source code from the repository using the authentication means; means for analyzing the retrieved source code using a generative AI model to identify the programming languages, libraries, and components used; means for selecting an optimal deployment platform based on the analysis results; means for automatically deploying to the selected deployment platform; and means for notifying the user of completion of deployment. This automates the deployment process for web services and web tools, enabling low-cost, high-performance deployment.

[0089] "Authentication method" refers to a technology that authenticates users accessing a website using a third-party authentication protocol (e.g., OAuth 2.0).

[0090] A "repository" is a place where source code and other related data are stored and managed in a version control system (e.g., GitHub).

[0091] "Source code" is a set of instructions written in a programming language that defines the behavior of a particular software or web application.

[0092] A "generative AI model" is a system that uses artificial intelligence to automatically generate and analyze text and code, and specific examples include natural language processing models (e.g., GPT-4).

[0093] "Analysis" is the process of examining source code in detail to identify the programming languages, libraries, and components used.

[0094] A "programming language" is an artificial language used to develop software and web applications, and examples include JavaScript, Python, and Java.

[0095] A "library" is a collection of reusable code that is used to easily access specific functionality or services.

[0096] "Components" are major parts of the source code, such as the front end, back end, and database.

[0097] A "deployment platform" is an online service for building and deploying source code into an executable form, and examples include Netlify, Vercel, and Heroku.

[0098] "Deployment" is the process of placing developed software in an executable environment and making it accessible to users.

[0099] "Notification" refers to the means by which users are notified that the deployment is complete, including, for example, email or dashboard alerts.

[0100] The present invention provides a system for efficiently deploying Web services and Web tools for individual developers and in educational settings. Detailed embodiments of the system will be described below.

[0101] System configuration

[0102] The system consists of the following main components:

[0103] 1. Authentication Methods

[0104] 2. How to obtain source code

[0105] 3. Generation AI analysis means

[0106] 4. How to Select the Optimal Deployment Platform

[0107] 5. Automated Deployment Methods

[0108] 6. Deployment result notification method

[0109] Authentication Method

[0110] A user accesses the system's website in a browser and authenticates with their GitHub account. OAuth 2.0 is used as the authentication method, which ensures secure authentication. Specifically, the user clicks the "Log in with GitHub" button and is redirected to the GitHub authentication page. GitHub then returns an authentication token, which the server receives and establishes a user session.

[0111] How to get source code

[0112] The server clones the repository specified by the user through the GitHub API using the obtained authentication token, and the source code is temporarily stored on the server for analysis.

[0113] Generation AI analysis means

[0114] The server inputs the cloned source code into a generative AI model (e.g., GPT-4) and performs analysis using the following prompt: "Analyze the source code below to identify the programming languages, libraries, and components used. Then, suggest the optimal deployment platform. Enter your source code here: <source code>." The generative AI model returns the analysis results to the server.

[0115] How to select the optimal deployment platform

[0116] The server evaluates multiple deployment platforms from an internal database based on the analysis results of the generative AI model. Evaluation criteria include cost-effectiveness, supported programming languages, and ease of deployment. For example, if the source code is composed of Node.js and React, Netlify or Vercel may be deemed suitable. If MongoDB is used as the backend, MongoDB Atlas and other platforms may also be considered.

[0117] Automated deployment methods

[0118] The server automatically deploys to the selected deployment platform, specifically by building and deploying the source code using the selected platform's API (e.g., Netlify API or Vercel API).

[0119] Deployment result notification method

[0120] The server will notify the user once the deployment is complete, either by email or via an alert on the website dashboard.

[0121] After receiving the notification, the user can access the provided URL to check the deployed web service or web tool.

[0122] Specific operation example

[0123] 1. A user develops a web tool on the educational platform and pushes the code to a GitHub repository.

[0124] 2. The user visits the system's website, authenticates with their GitHub account, and selects the repository they want to clone.

[0125] 3. The server clones the repository and analyzes the source code using a generative AI model such as GPT-4.

[0126] 4. Based on the analysis results, the server selects the optimal deployment platform (e.g., Netlify).

[0127] 5. The server performs the deployment using the Netlify API.

[0128] 6. The server notifies the user after the deployment is complete, and the user can access the provided URL to check the deployment results.

[0129] The system automates the deployment process of web services and web tools, ensuring efficient and high-performance deployment.

[0130] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0131] Step 1: Authentication

[0132] Input: The user visits the system's website in a browser and clicks the "Log in with GitHub" button.

[0133] How it works: When a user clicks the "Log in with GitHub" button, the system redirects the user to GitHub's authentication page using the OAuth 2.0 protocol.

[0134] Data processing: After the user enters their authentication information, GitHub returns an authentication token.

[0135] Output: An authentication token is sent to the server and a session is established for the user.

[0136] Step 2: Get the source code

[0137] Input: After the user has authenticated, they select the repository they want to deploy to.

[0138] What it does: The server uses the authentication token to clone the specified GitHub repository via the API.

[0139] Data processing: The source code is obtained through the GitHub API and temporarily stored on the server.

[0140] Output: A temporarily saved source code is prepared.

[0141] Step 3: Analyzing the source code

[0142] Input: Source code stored on the server.

[0143] How it works: The server inputs the source code into a generative AI model (e.g., GPT-4) and performs the analysis.

[0144] Data processing: Use the following prompt: "Analyze the source code below to identify the programming languages, libraries, and components used. Then, suggest the best deployment platform. Enter the source code here: <source code>". The generative AI model performs the analysis.

[0145] Output: The analysis results provide information about the programming languages, libraries, and components used.

[0146] Step 4: Select the optimal deployment platform

[0147] Input: Analysis results from the generative AI model.

[0148] How it works: Based on the analysis results, the server evaluates multiple deployment platforms from an internal database.

[0149] Data processing: Evaluation criteria include cost-effectiveness, supported programming languages, and ease of deployment.

[0150] Output: The optimal deployment platform (e.g. Netlify) is selected.

[0151] Step 5: Automated deployment

[0152] Input: Selected deployment platform information and source code.

[0153] How it works: The server uses the API of the platform of your choice (e.g., Netlify) to build and deploy the source code.

[0154] Data processing: Build and deploy source code through the Netlify API.

[0155] Output: Deployment is complete and a URL for the published web service is generated.

[0156] Step 6: Notification of deployment results

[0157] Input: Deployment completion information and the URL of the published web service.

[0158] Action: The server sends a notification to the user that the deployment is complete.

[0159] Data processing: Generate emails and dashboard alerts as notification methods.

[0160] Output: The user will be notified and can check the deployment result via the provided URL.

[0161] In this way, the processing flow of the entire system is completed, allowing users to achieve efficient and high-performance deployment.

[0162] (Application example 1)

[0163] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0164] In modern logistics centers, software updates and deployments for inventory management systems, delivery management systems, and other systems are complicated and require a large amount of resources and time. This has created a need for automating the software deployment process to make it efficient and fast. Furthermore, selecting the optimal deployment platform for the programming languages ​​and libraries used requires specialized knowledge and is not a straightforward task. Solving these challenges and improving the operational efficiency of logistics centers is essential.

[0165] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0166] In this invention, the server includes means for linking a repository in which a user stores source code with authentication means, means for acquiring the source code from the repository using the authentication means, means for analyzing the acquired source code and identifying the programming language and library used, means for analyzing the source code of software used in an inventory management system or a delivery management system in a logistics center and selecting an optimal deployment platform, and means for automatically deploying the software to the selected deployment platform. This enables the software deployment process to be automated, efficiently, and quickly.

[0167] A "repository" is a data storage for storing and versioning source code and other development files.

[0168] An "authentication method" is a mechanism for verifying a user's identity and securely accessing a repository, such as OAuth 2.0.

[0169] "Source code acquisition means" is a mechanism for acquiring source code from a repository through authentication means.

[0170] The "source code analysis means" is a mechanism for analyzing acquired source code and identifying the programming language and library used.

[0171] A "deployment platform" is an infrastructure service for deploying source code into an execution environment.

[0172] The "optimal deployment platform selection means" is a mechanism for selecting the optimal deployment platform based on the results of source code analysis.

[0173] An "automated deployment mechanism" is a mechanism for automatically building and deploying source code to a selected deployment platform.

[0174] A "logistics center" is a facility that manages inventory and delivery of goods.

[0175] An "inventory management system" is a system that manages the receipt, storage, and shipping of inventory at a logistics center.

[0176] A "delivery management system" is a system that plans and tracks product deliveries at a logistics center.

[0177] The present invention relates to a system for automating the software deployment process for inventory management systems and delivery management systems in a logistics center. This system consists of the following main components:

[0178] System configuration

[0179] Authentication method: Users access the system's website from a browser or device. They authenticate using OAuth 2.0 with their repository management system account. This authentication allows them to access the repository securely.

[0180] Source code acquisition method: The server clones the source code from the specified repository using the authenticated token. The cloned source code is temporarily stored on the server side and becomes the target of analysis.

[0181] Generative AI analysis method: The server analyzes the cloned source code. The generative AI identifies the programming languages, libraries, and components (front-end, back-end, database, etc.) in the source code.

[0182] Optimal deployment platform selection method: The server selects the optimal deployment platform based on the analysis results. Evaluation criteria include cost performance, supported languages, ease of deployment, etc.

[0183] Automatic deployment method: The server automatically builds and deploys the source code using the API of the selected deployment platform.

[0184] Notification of deployment results: Once the deployment is complete, the server will notify the user. Notification methods include email and dashboard alerts. The user can check the deployed system by accessing the provided URL.

[0185] Specific examples of embodiments

[0186] 1. Examples of authentication:

[0187] A user accesses the system's website from a browser and authenticates with an account in the repository management system using OAuth 2.0, which ensures security and grants access to the specified repository.

[0188] 2. Example of source code acquisition:

[0189] After authentication is complete, the server uses the OAuth 2.0 access token to clone the source code from the repository, using Git.

[0190] 3. Specific examples of analysis by generative AI:

[0191] The cloned source code is analyzed by generative AI, which identifies the programming languages ​​(e.g., Node.js, Python), libraries (e.g., React, Django), and other components used.

[0192] 4. Examples of choosing the best deployment platform:

[0193] The server then selects the appropriate deployment platform from its internal database based on the analysis results. For example, if the source code is written in Node.js and React, it may determine that Netlify or another front-end deployment platform is the best fit.

[0194] 5. Examples of automated deployment:

[0195] The server uses the API of the selected deployment platform to automatically build and deploy the source code. If the deployment is successful, a deployment URL is generated and notified to the user.

[0196] 6. Examples of deployment notifications:

[0197] After the deployment is complete, the server notifies the user via email or an alert function on the website dashboard, making it easy for users to check the deployed system.

[0198] Prompt Sentence Examples

[0199] Analyze the following source code, select the optimal deployment platform, and provide instructions for automated deployment.

[0200] Repository URL: https: / / github.com / user / repo

[0201] Language used: Node.js, React

[0202] Potential deployment platforms: Netlify, Heroku

[0203] As described above, the present invention is a system that automates the software deployment process in a logistics center, thereby realizing efficiency and speed, thereby enabling smooth business operations.

[0204] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0205] Step 1:

[0206] Performing authentication

[0207] Input: User's repository management system account information

[0208] How it works: A user accesses the system's website through a browser or device and authenticates using OAuth 2.0. If authentication is successful, an access token is issued.

[0209] Data processing / calculation: Obtaining and saving access tokens

[0210] Output: An access token with permissions to access the repository.

[0211] Step 2:

[0212] Get the source code

[0213] Input: Repository URL and Access Token

[0214] How it works: The server uses the access token to clone source code from the specified repository using a Git client, and the cloned source code is temporarily stored on the server.

[0215] Data processing / calculation: Download and save source code

[0216] Output: Source code stored on the server

[0217] Step 3:

[0218] Source code analysis

[0219] Input: Saved source code

[0220] How it works: The server uses a generative AI model to analyze the source code, which identifies the programming languages, libraries, and components used.

[0221] Data processing / computation: Identifying programming languages, libraries, and components

[0222] Output: Analysis results (list of languages ​​and libraries used)

[0223] Step 4:

[0224] Selecting the optimal deployment platform

[0225] Input: Source code analysis results

[0226] How it works: Based on the analysis results, the server compares and evaluates multiple deployment platforms from an internal database. Evaluation criteria include cost-effectiveness, supported languages, ease of deployment, etc. It then uses a generative AI model to select the most suitable deployment platform.

[0227] Data Processing / Computation: Evaluating and Selecting Deployment Platforms

[0228] Output: Selected deployment platform

[0229] Step 5:

[0230] Implementing automatic deployment

[0231] Input: Selected deployment platform and source code

[0232] How it works: The server builds and deploys the source code using the API of the chosen deployment platform, for example Netlify or other cloud deployment services.

[0233] Data processing / calculation: Building and deploying source code via API

[0234] Output: URL of the deployed system

[0235] Step 6:

[0236] Deployment status notifications

[0237] Input: Deploy result

[0238] How it works: After the deployment is complete, the server notifies the user of the deployment results, providing information to the user via email and dashboard alerts.

[0239] Data processing / calculation: Generation and transmission of notification data

[0240] Output: The deployment URL notified to the user

[0241] Through this procedure, the software deployment process at the logistics center is automated, efficient, and fast.

[0242] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0243] This invention is a system for efficient deployment of web services and web tools for individual developers and in educational settings, and is equipped with a function to recognize user emotions and suggest the optimal deployment platform based on those emotions. This system works in conjunction with a repository that stores source code, analyzes the source code, selects the optimal deployment platform, and automatically deploys the code. In addition, it incorporates an emotion engine that recognizes and analyzes user emotions.

[0244] System configuration

[0245] The system consists of the following main components:

[0246] 1. Authentication Methods

[0247] 2. How to obtain source code

[0248] 3. Generation AI analysis means

[0249] 4. How to Select the Optimal Deployment Platform

[0250] 5. Automated Deployment Methods

[0251] 6. Emotion Engine

[0252] Authentication Method

[0253] User: Accesses the system's website in a browser and authenticates with their GitHub account using OAuth 2.0. The user clicks the "Log in with GitHub" button and the GitHub authentication page is displayed. Next, the user enters their GitHub username and password to authenticate. If authentication is successful, the system obtains a GitHub authentication token.

[0254] How to get source code

[0255] Server: Using the obtained authentication token, the source code is cloned from the repository specified by the user. The cloned source code is temporarily stored on the server side for analysis.

[0256] Generation AI analysis means

[0257] Server: The generated AI analyzes the cloned source code, identifying the programming languages, libraries, and components (front-end, back-end, database, etc.) used within the source code.

[0258] How to select the optimal deployment platform

[0259] Server: Based on the analysis results, multiple deployment platforms are compared and evaluated from an internal database. Evaluation criteria include cost performance, supported languages, ease of deployment, etc. Finally, the most suitable platform is selected.

[0260] For example, if the source code is composed of Node.js and React, the server will determine that Netlify or Vercel is the best choice. Also, if MongoDB is used as the backend, it will consider using MongoDB Atlas or Heroku.

[0261] Automated deployment methods

[0262] Server: Automates deployment to the selected deployment platform. For example, if Netlify is selected, it uses the Netlify API to build and deploy the source code, sends the necessary configuration settings (environment variables, build commands, etc.), and starts the deployment process.

[0263] Emotion Engine

[0264] Server: Using an emotion engine, it recognizes the user's emotions and suggests the optimal deployment platform based on them. It analyzes the user's emotional state from their input and operation history. For example, if the user is feeling stressed, it simplifies the deployment procedure or provides gentle guidance.

[0265] Notification of deployment results

[0266] Server: Once the deployment is complete, the user is notified via email, dashboard notification, etc.

[0267] Users: They receive a notification and can visit the provided URL to view the deployed web service or web tool.

[0268] Specific operation example

[0269] 1. A user performs practical training on the engineering education platform and pushes the completed web tool to the repository.

[0270] 2. The user visits the system's website, authenticates with GitHub, and selects the repository to deploy to.

[0271] 3. The server retrieves the repository and the generative AI analyzes the source code.

[0272] 4. The emotion engine analyzes the user's emotional state and suggests a simplified deployment procedure if it determines that the user is feeling stressed.

[0273] 5. The server selects the optimal deployment platform and determines it to be Netlify.

[0274] 6. The server uses the Netlify API to automatically deploy the front-end app.

[0275] 7. The user receives a notification that the deployment is complete and can confirm by visiting the provided URL.

[0276] In this way, the present invention is a system that automates the deployment process of web services and web tools and provides support that is tailored to the user's emotional state, thereby achieving low-cost, high-performance deployment and reducing the burden on developers.

[0277] The processing flow will be explained below.

[0278] Step 1:

[0279] The user accesses the system's website in a browser and authenticates their GitHub account using OAuth 2.0. The user clicks the "Log in with GitHub" button and the GitHub authentication page is displayed. The user then enters their GitHub username and password to authenticate. If authentication is successful, the system obtains a GitHub authentication token.

[0280] Step 2:

[0281] The server uses the obtained authentication token to access the GitHub API and retrieve the user's repository list. The server sends a request to the GitHub API and receives the user's repository list in JSON format. The server parses this data and formats it into a format that can be displayed to the user.

[0282] Step 3:

[0283] The user selects the project they want to deploy from the displayed list of repositories. The user clicks the "Select repository to deploy to" button and enters the required configuration information (e.g., branch name, environment variables, etc.).

[0284] Step 4:

[0285] The server clones the selected repository. The server clones the repository locally using the Git command (e.g., git clone https: / / github.com / username / repositoryname.git). Once the clone is complete, the entire source code will be stored in the local directory.

[0286] Step 5:

[0287] The server uses generative AI to analyze the cloned source code. The generative AI analyzes the file structure of the source code and identifies the programming languages, libraries, and components (front-end, back-end, database, etc.) used. For example, it extracts dependencies from the package.json file and identifies the technology stack used.

[0288] Step 6:

[0289] The server uses an emotion engine to recognize the user's emotions, analyzes the user's emotional state based on their operation history and input, and determines whether they are feeling stressed or anxious.

[0290] Step 7:

[0291] The server selects the optimal deployment platform based on the analysis results of the Hikari technology stack and the user's emotional state. Multiple platform candidates are retrieved from an internal database and compared based on criteria such as cost performance, supported languages, and ease of deployment. For example, if the user is feeling stressed, a platform that simplifies the process will be prioritized.

[0292] Step 8:

[0293] The server automatically deploys to the selected deployment platform. For example, if Netlify is selected, it uses the Netlify API to build and deploy the source code, sends the necessary configuration settings (such as environment variables and build commands), and starts the deployment process.

[0294] Step 9:

[0295] The server notifies the user that the deployment is complete by sending an email, posting a notification on the dashboard, etc. The user receives the notification and accesses the provided URL to check the operation of the deployed web service or web tool.

[0296] In this way, the system can automatically deploy user-created web services and web tools with low cost and high performance, and also provide support that takes into account the user's emotional state.

[0297] Example 2

[0298] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0299] In modern web development, deployment tasks for individual developers and educational institutions are complex and involve many manual steps, making it difficult to perform efficiently. Furthermore, the selection of the optimal deployment platform does not take into account the emotional state of the user, which often leads to stress and reduced development productivity. Furthermore, there is a lack of a unified selection method for the platform that takes into account evaluation criteria such as cost performance and supported languages.

[0300] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0301] In this invention, the server includes means for linking a repository in which a user stores source code with authentication means, means for retrieving the source code from the repository using the authentication means, means for analyzing the retrieved source code and identifying the programming language and library used, means for selecting an optimal deployment platform based on the programming language and library, means for automatically deploying to the selected deployment platform, and an emotion engine for recognizing the emotional state of the user and suggesting the optimal deployment platform based on the emotional state. This makes the deployment of web services and web tools more efficient and provides support according to the user's emotional state, enabling low-stress, high-performance deployment.

[0302] A "repository" is a digital storage for storing and managing source code and related data.

[0303] "Authentication" refers to the processes and technologies used to verify a user's identity and grant appropriate access privileges.

[0304] A "server" is a computer system that provides data and services in response to requests from clients.

[0305] "Source code" is a set of instructions written in a programming language that defines the behavior of a piece of software or a web application.

[0306] "Generative AI" is a type of artificial intelligence that uses machine learning and deep learning techniques to extract patterns and information from data and provide new data and insights.

[0307] A "deployment platform" refers to the infrastructure and services that deploy software and web applications into an execution environment and make them accessible to users.

[0308] An "emotion engine" refers to technology and algorithms that analyze data such as user input and operation history to recognize the user's emotional state.

[0309] "Automated deployment" is the process of deploying software or web applications to a specified execution environment without manual intervention.

[0310] This invention is a system for efficient deployment of web services and web tools for individual developers and in educational settings, and in particular has the function of recognizing user emotions and proposing the optimal deployment platform based on that. This system works in conjunction with a repository that stores source code, analyzes the source code, selects the optimal deployment platform, and automatically deploys it. In addition, it incorporates an emotion engine that recognizes and analyzes user emotions.

[0311] System configuration

[0312] The system consists of the following main components:

[0313] 1. Authentication Methods

[0314] 2. How to obtain source code

[0315] 3. Generation AI analysis means

[0316] 4. How to Select the Optimal Deployment Platform

[0317] 5. Automated Deployment Methods

[0318] 6. Emotion Engine

[0319] Authentication Method

[0320] The authentication method involves the process where the user accesses the system's website in a browser and authenticates their repository service account (e.g., GitHub) using OAuth 2.0. The user clicks the "Log in with Repository Service" button, and an authentication page is displayed. The user then enters their repository service username and password to authenticate. If authentication is successful, the server obtains an authentication token for the repository service.

[0321] How to get source code

[0322] The source code acquisition means includes a process in which the server clones the source code from the repository specified by the user using the authentication token acquired by the server. The cloned source code is temporarily stored on the server side for analysis.

[0323] Generation AI analysis means

[0324] The generative AI analysis method involves a process in which the server uses generative AI to analyze the cloned source code, which identifies the programming languages, libraries, and components (front-end, back-end, database, etc.) used within the source code.

[0325] How to select the optimal deployment platform

[0326] The optimal deployment platform selection method involves the server comparing and evaluating multiple deployment platforms from an internal database based on the analysis results. Evaluation criteria include cost performance, supported languages, ease of deployment, etc. Finally, the most suitable platform is selected. For example, if the source code is composed of Node.js and React, the server will determine that Netlify or Vercel is optimal. Also, if a database service is used as the backend, the server will also consider the deployment platform appropriate for that service.

[0327] Automated deployment methods

[0328] The automated deployment process involves the server automatically deploying to the selected deployment platform. For example, if Netlify is selected, it uses the Netlify API to build and deploy the source code, sends the necessary configuration settings (such as environment variables and build commands), and starts the deployment process.

[0329] Emotion Engine

[0330] The emotion engine involves a process in which the server recognizes the user's emotional state and suggests the optimal deployment platform based on that. For example, if the user is feeling stressed, the server analyzes the user's emotional state from their input and operation history, and simplifies the deployment procedure or provides gentle guidance.

[0331] Specific examples

[0332] 1. Users practice on the learning platform and push the completed web tool to the repository.

[0333] 2. The user accesses the system website, authenticates with the repository service, and selects the repository to deploy to.

[0334] 3. The server retrieves the repository and the generative AI analyzes the source code.

[0335] 4. The emotion engine analyzes the user's emotional state and suggests a simplified deployment procedure if it determines that the user is feeling stressed.

[0336] 5. The server selects the optimal deployment platform, for example, Netlify.

[0337] 6. The server automatically deploys the web app using the Netlify API.

[0338] 7. The user receives a notification that the deployment is complete and can confirm by accessing the provided URL.

[0339] Prompt Sentence Examples

[0340] "I'd like to deploy a web app built with Node.js and React. Can you briefly explain the automated deployment process? Also, could you suggest the best deployment platform to use?"

[0341] In this way, this invention is a system that automates the deployment process of web services and web tools and provides support that is tailored to the user's emotional state, thereby achieving efficient and stress-free deployment.

[0342] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0343] Step 1:

[0344] A user accesses the system's website in a browser and clicks the "Log in with Repository Service" button. The input is the user's click action, and the output is the display of an authentication page. As a specific operation, the browser redirects the user to the authentication page of the repository service.

[0345] Step 2:

[0346] The user enters a username and password on the repository service authentication page to perform authentication. The input is the user's authentication information, and the output is an authentication token. Specifically, the authentication process ends and the server receives the authentication token from the repository service.

[0347] Step 3:

[0348] The server uses the authentication token it has obtained to clone source code from the repository specified by the user. The input is the authentication token and repository specification, and the output is the cloned source code. Specifically, the server uses an API to obtain the contents of the repository and temporarily stores them.

[0349] Step 4:

[0350] The server analyzes the cloned source code using generative AI. The input is the cloned source code, and the output is the identification of programming languages, libraries, and components. Specifically, the generative AI analyzes the source code and identifies the languages, libraries, and system configurations used.

[0351] Step 5:

[0352] Based on the analysis results, the server compares and evaluates multiple deployment platforms from an internal database. The input is the analysis results, and the output is the selection of the optimal deployment platform. Specifically, the server evaluates the platforms based on cost performance, supported languages, and ease of deployment, and selects the appropriate platform.

[0353] Step 6:

[0354] The emotion engine analyzes the user's emotional state based on their input and operation history. The input is the user's operation history data, and the output is the evaluation result of the emotional state. Specifically, the emotion engine calculates stress levels and other emotional indicators and generates appropriate feedback.

[0355] Step 7:

[0356] Based on the evaluation result of the emotional state, the server may simplify the deployment procedure or guide the user with a gentle message. The input is the evaluation result of the emotional state and the deployment procedure, and the output is a notification to the user and a simplified deployment procedure. Specifically, the server sends an easy-to-understand message to the user.

[0357] Step 8:

[0358] The server automatically deploys to the optimal deployment platform. The input is the selected deployment platform and source code, and the output is a deployment completion notification. Specifically, the server automatically builds and deploys the source code using the API of the selected platform.

[0359] Step 9:

[0360] After the deployment is complete, the server notifies the user of the results. The input is the deployment completion status, and the output is the user notification. Specifically, the server notifies the user of the deployment results via email or a notification on the dashboard.

[0361] Step 10:

[0362] The user accesses the provided URL and checks the deployed web service or web tool. The input is the notified URL, and the output is confirmation of the deployment results. Specifically, the user accesses the URL in a browser and checks the actual operation of the service.

[0363] (Application example 2)

[0364] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0365] In traditional software development, developers must manually select an appropriate deployment platform and execute the deployment procedure, which is time-consuming, labor-intensive, and time-consuming. Furthermore, when developers feel stressed or impatient, the efficiency of the deployment process decreases and errors become more likely. To address these issues, a system is needed that can automatically adjust the deployment procedure and suggest the optimal deployment platform based on the user's emotional state.

[0366] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for authenticating a database in which a user stores source code, means for retrieving the source code from the database using the authentication means, means for analyzing the retrieved source code and identifying the programming language and library used, means for selecting an optimal deployment platform based on the programming language and library, means for automatically deploying on the selected deployment platform, means for analyzing the emotional state of the user, and means for adjusting the deployment procedure based on the emotional state. This automates the deployment process and makes it possible to provide optimal support according to the user's emotional state.

[0367] "Source code" means a description of the program used in the design and development of software.

[0368] A "database" is an information system that stores data in an organized manner so that it can be efficiently managed, searched, and retrieved.

[0369] "Authentication" is the process of verifying that a particular user or system is legitimate.

[0370] A "programming language" is an artificial language used to create software and applications.

[0371] A "library" is a collection of reusable routines or code in programming.

[0372] A "deployment platform" is an environment for running software applications.

[0373] "Analysis" is the process of examining and evaluating data or information in detail.

[0374] "Automated deployment" is the act of placing software or applications into a specified environment without manual intervention.

[0375] "Emotional state" refers to the user's mental and emotional state.

[0376] "Adjustment" is the act of changing settings or procedures to accommodate specific conditions.

[0377] An embodiment of the present invention will be described.

[0378] System Configuration

[0379] The system consists of the following main components:

[0380] 1. Authentication Methods

[0381] 2. How to obtain source code

[0382] 3. Generation AI analysis means

[0383] 4. How to Select the Optimal Deployment Platform

[0384] 5. Automated Deployment Methods

[0385] 6. Emotion Engine

[0386] Authentication Method

[0387] The user accesses the system's website in a browser and authenticates the GitHub account using OAuth 2.0. The user clicks the "Log in with GitHub" button, and the GitHub authentication page is displayed. The user then enters their GitHub username and password to authenticate. If authentication is successful, the server obtains a GitHub authentication token.

[0388] How to get source code

[0389] The server uses the obtained authentication token to clone the source code from the repository specified by the user. The cloned source code is temporarily stored on the server side for analysis.

[0390] Generation AI analysis means

[0391] The server then uses a generative AI model to analyze the cloned source code, which identifies the programming languages, libraries, and components (e.g., front-end, back-end, database, etc.) used within the source code.

[0392] How to select the optimal deployment platform

[0393] Based on the analysis results, the server compares and evaluates multiple deployment platforms from an internal database. Evaluation criteria include cost performance, supported languages, ease of deployment, etc., and ultimately selects the most suitable platform. For example, if the source code is composed of Python and Flask, the server will determine that Heroku is the best choice.

[0394] Automated deployment methods

[0395] The server automatically deploys to the selected deployment platform (for example, if Heroku is selected, it uses the Heroku API to build and deploy the source code, sends the necessary configuration settings (such as environment variables and build commands), and starts the deployment process.

[0396] Emotion Engine

[0397] The server uses an emotion engine to recognize the user's emotional state and propose the optimal deployment procedure based on that. It analyzes the user's emotional state from their input and operation history. For example, if the user is feeling stressed, it simplifies the deployment procedure or provides a gentle message.

[0398] Notification of deployment results

[0399] Once the deployment is complete, the server will notify the user via email, a notification on the dashboard, etc. Users can receive the notification and access the provided URL to check the deployed system and tools.

[0400] Specific operation example

[0401] 1. A user pushes a new robot control program to GitHub and deploys it using the RoboDeploy app.

[0402] 2. The user opens the app, authenticates with GitHub, and selects the repository to deploy.

[0403] 3. The server retrieves the repository and the generative AI analyzes the source code.

[0404] 4. The emotion engine analyzes the user's emotional state and, if it determines that the user is feeling stressed, suggests a simplified deployment procedure.

[0405] 5. The server selects the optimal deployment platform and determines it to be Heroku.

[0406] 6. The server automatically performs the deployment using the Heroku API.

[0407] 7. The user receives a notification that the deployment is complete and visits the provided URL to see the results.

[0408] Prompt Sentence Examples

[0409] An engineer deploying a new robot control program opens the RoboDeploy app. After successfully authenticating with GitHub and specifying the target repository, the app analyzes the code and automatically deploys it to the optimal Heroku repository. Describe the process of the emotion engine detecting the user's stress level and deploying it in a simplified manner.

[0410] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0411] Step 1: Authentication

[0412] The server authenticates a user's GitHub account when the user accesses the system's website in a browser. The user clicks the "Log in with GitHub" button, and the GitHub authentication page is displayed. Next, the user enters their GitHub username and password to authenticate. The input is the username and password, and the output is an authentication token. The server obtains this authentication token.

[0413] Step 2: Get the source code

[0414] The server uses the authentication token to clone the source code from the repository specified by the user. The input is the authentication token and the repository URL, and the output is the source code. The cloned source code is temporarily stored on the server side for analysis. Specifically, the server calls the GitHub API to retrieve the contents of the specified repository.

[0415] Step 3: Analysis by generative AI

[0416] The server analyzes the cloned source code using a generative AI model. The input is the source code, and the output is the programming language, library, and components used. The generative AI model performs data analysis to identify various elements in the source code (language, library, framework, etc.). Specifically, the server inputs the source code into the AI ​​model and obtains the analysis results.

[0417] Step 4: Select the optimal deployment platform

[0418] The server compares and evaluates multiple deployment platforms from an internal database based on the analysis results. The input is the analysis results, and the output is the optimal deployment platform. Evaluation criteria include cost performance, supported languages, ease of deployment, etc. Specifically, the server calculates an evaluation score for each platform based on the analysis results and selects the most suitable platform.

[0419] Step 5: Automatic deployment

[0420] The server automatically performs deployment to the selected deployment platform. The input is the selected deployment platform information and source code, and the output is the deployment result. For example, if Heroku is selected, the server will use the Heroku API to build and deploy the source code. Specifically, the server will call the API to instruct the build and deployment.

[0421] Step 6: Analyze emotional state

[0422] The server uses an emotion engine to analyze the user's emotional state. The input is the user's operation history and input data, and the output is the analyzed emotional state. Specifically, the server inputs operation logs and session data into the emotion engine to recognize the user's stress level and emotional state.

[0423] Step 7: Adjust the deployment procedure

[0424] The server adjusts the deployment procedure based on the analyzed emotional state. The input is the emotional state, and the output is the adjusted deployment procedure. For example, if the user is feeling stressed, the server simplifies the deployment procedure or guides the user with a gentle message. Specifically, the server generates a simplified procedure and presents it to the user.

[0425] Step 8: Notification

[0426] The server notifies the user when the deployment is complete. The input is the deployment completion status, and the output is a notification message. Notification methods include email and notifications on the dashboard. As a specific operation, the server generates a notification message and sends it to the user via the specified method.

[0427] The above is a specific description of each processing step in the deployment process.

[0428] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0429] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0430] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0431] [Second embodiment]

[0432] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0433] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0434] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0435] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0436] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0437] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0438] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0439] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0440] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0441] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0442] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0443] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0444] This invention is a system for efficient deployment of web services and web tools for individual developers and educational institutions. This system works in conjunction with a repository that stores source code, analyzes the source code, selects the optimal deployment platform, and automatically deploys the code.

[0445] System configuration

[0446] The system consists of the following main components:

[0447] 1. Authentication Methods

[0448] 2. How to obtain source code

[0449] 3. Generation AI analysis means

[0450] 4. How to Select the Optimal Deployment Platform

[0451] 5. Automated Deployment Methods

[0452] Authentication Method

[0453] User: Accesses the system's website in a browser. The user authenticates using an account in a version control system such as GitHub. OAuth 2.0 is used for authentication, which ensures secure authentication.

[0454] How to get source code

[0455] Server: Using the authentication token, the source code is cloned from the repository specified by the user. The cloned source code is temporarily stored on the server side for analysis.

[0456] Generation AI analysis means

[0457] Server: The generated AI analyzes the cloned source code, identifying the programming languages, libraries, and components (front-end, back-end, database, etc.) used within the source code.

[0458] How to select the optimal deployment platform

[0459] Server: Based on the analysis results, multiple deployment platforms are compared and evaluated from an internal database. Evaluation criteria include cost performance, supported languages, ease of deployment, etc. The optimal deployment platform is selected.

[0460] For example, if the source code is composed of Node.js and React, the server will determine that Netlify or Vercel is the best choice. Also, if MongoDB is used as the backend, it will consider using MongoDB Atlas or Heroku.

[0461] Automated deployment methods

[0462] Server: Automatically deploys to the selected deployment platform. The server uses the deployment platform's API to automatically build and deploy the source code.

[0463] Notification of deployment results

[0464] Server: Once the deployment is complete, the user is notified via email, dashboard alerts, etc.

[0465] Users: They receive a notification and can visit the provided URL to view the deployed web service or web tool.

[0466] Specific operation example

[0467] 1. A user performs practical training on the engineering education platform and pushes the completed web tool to the repository.

[0468] 2. The user visits the system's website, authenticates with GitHub, and selects the repository to deploy to.

[0469] 3. The server retrieves the repository and the generative AI analyzes the source code.

[0470] 4. The server selects the optimal deployment platform and determines it to be Netlify.

[0471] 5. The server uses the Netlify API to automatically deploy the front-end app.

[0472] 6. The user receives a notification that the deployment is complete and can confirm by visiting the provided URL.

[0473] In this way, the present invention is a system that automates the deployment process of web services and web tools, achieving low cost and high performance, thereby providing great convenience to individual developers and users in educational settings.

[0474] The processing flow will be explained below.

[0475] Step 1:

[0476] The user accesses the system's website in a browser and authenticates their GitHub account using OAuth 2.0. The user clicks the "Log in with GitHub" button and the GitHub authentication page is displayed. The user then enters their GitHub username and password to authenticate. If authentication is successful, the system obtains a GitHub authentication token.

[0477] Step 2:

[0478] The server uses the obtained authentication token to access the GitHub API and retrieve the user's repository list. The server sends a request to the GitHub API and receives the user's repository list in JSON format. The server parses this data and formats it into a format that can be displayed to the user.

[0479] Step 3:

[0480] The user selects the project they want to deploy from the displayed list of repositories. The user clicks the "Select repository to deploy to" button and enters the required configuration information (e.g., branch name, environment variables, etc.).

[0481] Step 4:

[0482] The server clones the selected repository. The server clones the repository locally using the Git command (e.g., git clone https: / / github.com / username / repositoryname.git). Once the clone is complete, the entire source code will be stored in the local directory.

[0483] Step 5:

[0484] The server uses generative AI to analyze the cloned source code. The generative AI analyzes the file structure of the source code and identifies the programming languages, libraries, and components (front-end, back-end, database, etc.) used. For example, it extracts dependencies from the package.json file and identifies the technology stack used.

[0485] Step 6:

[0486] The server selects the optimal deployment platform based on the analysis results. It retrieves multiple platform candidates from an internal database and compares them based on evaluation criteria such as cost performance, supported languages, and ease of deployment. Finally, it selects the most suitable platform.

[0487] Step 7:

[0488] The server automatically deploys to the selected deployment platform. For example, if Netlify is selected, it uses the Netlify API to build and deploy the source code, sends the necessary configuration settings (such as environment variables and build commands), and starts the deployment process.

[0489] Step 8:

[0490] The server notifies the user that the deployment is complete by sending an email, posting a notification on the dashboard, etc. The user receives the notification and accesses the provided URL to check the operation of the deployed web service or web tool.

[0491] In this way, the system provides an environment in which users can automatically deploy and easily publish web services and web tools they create at low cost and with high performance.

[0492] Example 1

[0493] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0494] Existing deployment methods for web services and web tools involve a lot of manual work, which is time-consuming and labor-intensive for individual developers and educational institutions. In addition, there is a lack of know-how to select the optimal deployment platform, which can result in high costs and low performance deployments.

[0495] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0496] In this invention, the server includes: means for linking a repository in which a user stores source code with authentication means; means for retrieving the source code from the repository using the authentication means; means for analyzing the retrieved source code using a generative AI model to identify the programming languages, libraries, and components used; means for selecting an optimal deployment platform based on the analysis results; means for automatically deploying to the selected deployment platform; and means for notifying the user of completion of deployment. This automates the deployment process for web services and web tools, enabling low-cost, high-performance deployment.

[0497] "Authentication method" refers to a technology that authenticates users accessing a website using a third-party authentication protocol (e.g., OAuth 2.0).

[0498] A "repository" is a place where source code and other related data are stored and managed in a version control system (e.g., GitHub).

[0499] "Source code" is a set of instructions written in a programming language that defines the behavior of a particular software or web application.

[0500] A "generative AI model" is a system that uses artificial intelligence to automatically generate and analyze text and code, and specific examples include natural language processing models (e.g., GPT-4).

[0501] "Analysis" is the process of examining source code in detail to identify the programming languages, libraries, and components used.

[0502] A "programming language" is an artificial language used to develop software and web applications, and examples include JavaScript, Python, and Java.

[0503] A "library" is a collection of reusable code that is used to easily access specific functionality or services.

[0504] "Components" are major parts of the source code, such as the front end, back end, and database.

[0505] A "deployment platform" is an online service for building and deploying source code into an executable form, and examples include Netlify, Vercel, and Heroku.

[0506] "Deployment" is the process of placing developed software in an executable environment and making it accessible to users.

[0507] "Notification" refers to the means by which users are notified that the deployment is complete, including, for example, email or dashboard alerts.

[0508] The present invention provides a system for efficiently deploying Web services and Web tools for individual developers and in educational settings. Detailed embodiments of the system will be described below.

[0509] System configuration

[0510] The system consists of the following main components:

[0511] 1. Authentication Methods

[0512] 2. How to obtain source code

[0513] 3. Generation AI analysis means

[0514] 4. How to Select the Optimal Deployment Platform

[0515] 5. Automated Deployment Methods

[0516] 6. Deployment result notification method

[0517] Authentication Method

[0518] A user accesses the system's website in a browser and authenticates with their GitHub account. OAuth 2.0 is used as the authentication method, which ensures secure authentication. Specifically, the user clicks the "Log in with GitHub" button and is redirected to the GitHub authentication page. GitHub then returns an authentication token, which the server receives and establishes a user session.

[0519] How to get source code

[0520] The server clones the repository specified by the user through the GitHub API using the obtained authentication token, and the source code is temporarily stored on the server for analysis.

[0521] Generation AI analysis means

[0522] The server inputs the cloned source code into a generative AI model (e.g., GPT-4) and performs analysis using the following prompt: "Analyze the source code below to identify the programming languages, libraries, and components used. Then, suggest the optimal deployment platform. Enter your source code here: <source code>." The generative AI model returns the analysis results to the server.

[0523] How to select the optimal deployment platform

[0524] The server evaluates multiple deployment platforms from an internal database based on the analysis results of the generative AI model. Evaluation criteria include cost-effectiveness, supported programming languages, and ease of deployment. For example, if the source code is composed of Node.js and React, Netlify or Vercel may be deemed suitable. If MongoDB is used as the backend, MongoDB Atlas and other platforms may also be considered.

[0525] Automated deployment methods

[0526] The server automatically deploys to the selected deployment platform, specifically by building and deploying the source code using the selected platform's API (e.g., Netlify API or Vercel API).

[0527] Deployment result notification method

[0528] The server will notify the user once the deployment is complete, either by email or via an alert on the website dashboard.

[0529] After receiving the notification, the user can access the provided URL to check the deployed web service or web tool.

[0530] Specific operation example

[0531] 1. A user develops a web tool on the educational platform and pushes the code to a GitHub repository.

[0532] 2. The user visits the system's website, authenticates with their GitHub account, and selects the repository they want to clone.

[0533] 3. The server clones the repository and analyzes the source code using a generative AI model such as GPT-4.

[0534] 4. Based on the analysis results, the server selects the optimal deployment platform (e.g., Netlify).

[0535] 5. The server performs the deployment using the Netlify API.

[0536] 6. The server notifies the user after the deployment is complete, and the user can access the provided URL to check the deployment results.

[0537] The system automates the deployment process of web services and web tools, ensuring efficient and high-performance deployment.

[0538] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0539] Step 1: Authentication

[0540] Input: The user visits the system's website in a browser and clicks the "Log in with GitHub" button.

[0541] How it works: When a user clicks the "Log in with GitHub" button, the system redirects the user to GitHub's authentication page using the OAuth 2.0 protocol.

[0542] Data processing: After the user enters their authentication information, GitHub returns an authentication token.

[0543] Output: An authentication token is sent to the server and a session is established for the user.

[0544] Step 2: Get the source code

[0545] Input: After the user has authenticated, they select the repository they want to deploy to.

[0546] What it does: The server uses the authentication token to clone the specified GitHub repository via the API.

[0547] Data processing: The source code is obtained through the GitHub API and temporarily stored on the server.

[0548] Output: A temporarily saved source code is prepared.

[0549] Step 3: Analyzing the source code

[0550] Input: Source code stored on the server.

[0551] How it works: The server inputs the source code into a generative AI model (e.g., GPT-4) and performs the analysis.

[0552] Data processing: Use the following prompt: "Analyze the source code below to identify the programming languages, libraries, and components used. Then, suggest the best deployment platform. Enter the source code here: <source code>". The generative AI model performs the analysis.

[0553] Output: The analysis results provide information about the programming languages, libraries, and components used.

[0554] Step 4: Select the optimal deployment platform

[0555] Input: Analysis results from the generative AI model.

[0556] How it works: Based on the analysis results, the server evaluates multiple deployment platforms from an internal database.

[0557] Data processing: Evaluation criteria include cost-effectiveness, supported programming languages, and ease of deployment.

[0558] Output: The optimal deployment platform (e.g. Netlify) is selected.

[0559] Step 5: Automated deployment

[0560] Input: Selected deployment platform information and source code.

[0561] How it works: The server uses the API of the platform of your choice (e.g., Netlify) to build and deploy the source code.

[0562] Data processing: Build and deploy source code through the Netlify API.

[0563] Output: Deployment is complete and a URL for the published web service is generated.

[0564] Step 6: Notification of deployment results

[0565] Input: Deployment completion information and the URL of the published web service.

[0566] Action: The server sends a notification to the user that the deployment is complete.

[0567] Data processing: Generate emails and dashboard alerts as notification methods.

[0568] Output: The user will be notified and can check the deployment result via the provided URL.

[0569] In this way, the processing flow of the entire system is completed, allowing users to achieve efficient and high-performance deployment.

[0570] (Application example 1)

[0571] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0572] In modern logistics centers, software updates and deployments for inventory management systems, delivery management systems, and other systems are complicated and require a large amount of resources and time. This has created a need for automating the software deployment process to make it efficient and fast. Furthermore, selecting the optimal deployment platform for the programming languages ​​and libraries used requires specialized knowledge and is not a straightforward task. Solving these challenges and improving the operational efficiency of logistics centers is essential.

[0573] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0574] In this invention, the server includes means for linking a repository in which a user stores source code with authentication means, means for acquiring the source code from the repository using the authentication means, means for analyzing the acquired source code and identifying the programming language and library used, means for analyzing the source code of software used in an inventory management system or a delivery management system in a logistics center and selecting an optimal deployment platform, and means for automatically deploying the software to the selected deployment platform. This enables the software deployment process to be automated, efficiently, and quickly.

[0575] A "repository" is a data storage for storing and versioning source code and other development files.

[0576] An "authentication method" is a mechanism for verifying a user's identity and securely accessing a repository, such as OAuth 2.0.

[0577] "Source code acquisition means" is a mechanism for acquiring source code from a repository through authentication means.

[0578] The "source code analysis means" is a mechanism for analyzing acquired source code and identifying the programming language and library used.

[0579] A "deployment platform" is an infrastructure service for deploying source code into an execution environment.

[0580] The "optimal deployment platform selection means" is a mechanism for selecting the optimal deployment platform based on the results of source code analysis.

[0581] An "automated deployment mechanism" is a mechanism for automatically building and deploying source code to a selected deployment platform.

[0582] A "logistics center" is a facility that manages inventory and delivery of goods.

[0583] An "inventory management system" is a system that manages the receipt, storage, and shipping of inventory at a logistics center.

[0584] A "delivery management system" is a system that plans and tracks product deliveries at a logistics center.

[0585] The present invention relates to a system for automating the software deployment process for inventory management systems and delivery management systems in a logistics center. This system consists of the following main components:

[0586] System configuration

[0587] Authentication method: Users access the system's website from a browser or device. They authenticate using OAuth 2.0 with their repository management system account. This authentication allows them to access the repository securely.

[0588] Source code acquisition method: The server clones the source code from the specified repository using the authenticated token. The cloned source code is temporarily stored on the server side and becomes the target of analysis.

[0589] Generative AI analysis method: The server analyzes the cloned source code. The generative AI identifies the programming languages, libraries, and components (front-end, back-end, database, etc.) in the source code.

[0590] Optimal deployment platform selection method: The server selects the optimal deployment platform based on the analysis results. Evaluation criteria include cost performance, supported languages, ease of deployment, etc.

[0591] Automatic deployment method: The server automatically builds and deploys the source code using the API of the selected deployment platform.

[0592] Notification of deployment results: Once the deployment is complete, the server will notify the user. Notification methods include email and dashboard alerts. The user can check the deployed system by accessing the provided URL.

[0593] Specific examples of embodiments

[0594] 1. Examples of authentication:

[0595] A user accesses the system's website from a browser and authenticates with an account in the repository management system using OAuth 2.0, which ensures security and grants access to the specified repository.

[0596] 2. Example of source code acquisition:

[0597] After authentication is complete, the server uses the OAuth 2.0 access token to clone the source code from the repository, using Git.

[0598] 3. Specific examples of analysis by generative AI:

[0599] The cloned source code is analyzed by generative AI, which identifies the programming languages ​​(e.g., Node.js, Python), libraries (e.g., React, Django), and other components used.

[0600] 4. Examples of choosing the best deployment platform:

[0601] The server then selects the appropriate deployment platform from its internal database based on the analysis results. For example, if the source code is written in Node.js and React, it may determine that Netlify or another front-end deployment platform is the best fit.

[0602] 5. Examples of automated deployment:

[0603] The server uses the API of the selected deployment platform to automatically build and deploy the source code. If the deployment is successful, a deployment URL is generated and notified to the user.

[0604] 6. Examples of deployment notifications:

[0605] After the deployment is complete, the server notifies the user via email or an alert function on the website dashboard, making it easy for users to check the deployed system.

[0606] Prompt Sentence Examples

[0607] Analyze the following source code, select the optimal deployment platform, and provide instructions for automated deployment.

[0608] Repository URL: https: / / github.com / user / repo

[0609] Language used: Node.js, React

[0610] Potential deployment platforms: Netlify, Heroku

[0611] As described above, the present invention is a system that automates the software deployment process in a logistics center, thereby realizing efficiency and speed, thereby enabling smooth business operations.

[0612] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0613] Step 1:

[0614] Performing authentication

[0615] Input: User's repository management system account information

[0616] How it works: A user accesses the system's website through a browser or device and authenticates using OAuth 2.0. If authentication is successful, an access token is issued.

[0617] Data processing / calculation: Obtaining and saving access tokens

[0618] Output: An access token with permissions to access the repository.

[0619] Step 2:

[0620] Get the source code

[0621] Input: Repository URL and Access Token

[0622] How it works: The server uses the access token to clone source code from the specified repository using a Git client, and the cloned source code is temporarily stored on the server.

[0623] Data processing / calculation: Download and save source code

[0624] Output: Source code stored on the server

[0625] Step 3:

[0626] Source code analysis

[0627] Input: Saved source code

[0628] How it works: The server uses a generative AI model to analyze the source code, which identifies the programming languages, libraries, and components used.

[0629] Data processing / computation: Identifying programming languages, libraries, and components

[0630] Output: Analysis results (list of languages ​​and libraries used)

[0631] Step 4:

[0632] Selecting the optimal deployment platform

[0633] Input: Source code analysis results

[0634] How it works: Based on the analysis results, the server compares and evaluates multiple deployment platforms from an internal database. Evaluation criteria include cost-effectiveness, supported languages, ease of deployment, etc. It then uses a generative AI model to select the most suitable deployment platform.

[0635] Data Processing / Computation: Evaluating and Selecting Deployment Platforms

[0636] Output: Selected deployment platform

[0637] Step 5:

[0638] Implementing automatic deployment

[0639] Input: Selected deployment platform and source code

[0640] How it works: The server builds and deploys the source code using the API of the chosen deployment platform, for example Netlify or other cloud deployment services.

[0641] Data processing / calculation: Building and deploying source code via API

[0642] Output: URL of the deployed system

[0643] Step 6:

[0644] Deployment status notifications

[0645] Input: Deploy result

[0646] How it works: After the deployment is complete, the server notifies the user of the deployment results, providing information to the user via email and dashboard alerts.

[0647] Data processing / calculation: Generation and transmission of notification data

[0648] Output: The deployment URL notified to the user

[0649] Through this procedure, the software deployment process at the logistics center is automated, efficient, and fast.

[0650] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0651] This invention is a system for efficient deployment of web services and web tools for individual developers and in educational settings, and is equipped with a function to recognize user emotions and suggest the optimal deployment platform based on those emotions. This system works in conjunction with a repository that stores source code, analyzes the source code, selects the optimal deployment platform, and automatically deploys the code. In addition, it incorporates an emotion engine that recognizes and analyzes user emotions.

[0652] System configuration

[0653] The system consists of the following main components:

[0654] 1. Authentication Methods

[0655] 2. How to obtain source code

[0656] 3. Generation AI analysis means

[0657] 4. How to Select the Optimal Deployment Platform

[0658] 5. Automated Deployment Methods

[0659] 6. Emotion Engine

[0660] Authentication Method

[0661] User: Accesses the system's website in a browser and authenticates with their GitHub account using OAuth 2.0. The user clicks the "Log in with GitHub" button and the GitHub authentication page is displayed. Next, the user enters their GitHub username and password to authenticate. If authentication is successful, the system obtains a GitHub authentication token.

[0662] How to get source code

[0663] Server: Using the obtained authentication token, the source code is cloned from the repository specified by the user. The cloned source code is temporarily stored on the server side for analysis.

[0664] Generation AI analysis means

[0665] Server: The generated AI analyzes the cloned source code, identifying the programming languages, libraries, and components (front-end, back-end, database, etc.) used within the source code.

[0666] How to select the optimal deployment platform

[0667] Server: Based on the analysis results, multiple deployment platforms are compared and evaluated from an internal database. Evaluation criteria include cost performance, supported languages, ease of deployment, etc. Finally, the most suitable platform is selected.

[0668] For example, if the source code is composed of Node.js and React, the server will determine that Netlify or Vercel is the best choice. Also, if MongoDB is used as the backend, it will consider using MongoDB Atlas or Heroku.

[0669] Automated deployment methods

[0670] Server: Automates deployment to the selected deployment platform. For example, if Netlify is selected, it uses the Netlify API to build and deploy the source code, sends the necessary configuration settings (environment variables, build commands, etc.), and starts the deployment process.

[0671] Emotion Engine

[0672] Server: Using an emotion engine, it recognizes the user's emotions and suggests the optimal deployment platform based on them. It analyzes the user's emotional state from their input and operation history. For example, if the user is feeling stressed, it simplifies the deployment procedure or provides gentle guidance.

[0673] Notification of deployment results

[0674] Server: Once the deployment is complete, the user is notified via email, dashboard notification, etc.

[0675] Users: They receive a notification and can visit the provided URL to view the deployed web service or web tool.

[0676] Specific operation example

[0677] 1. A user performs practical training on the engineering education platform and pushes the completed web tool to the repository.

[0678] 2. The user visits the system's website, authenticates with GitHub, and selects the repository to deploy to.

[0679] 3. The server retrieves the repository and the generative AI analyzes the source code.

[0680] 4. The emotion engine analyzes the user's emotional state and suggests a simplified deployment procedure if it determines that the user is feeling stressed.

[0681] 5. The server selects the optimal deployment platform and determines it to be Netlify.

[0682] 6. The server uses the Netlify API to automatically deploy the front-end app.

[0683] 7. The user receives a notification that the deployment is complete and can confirm by visiting the provided URL.

[0684] In this way, the present invention is a system that automates the deployment process of web services and web tools and provides support that is tailored to the user's emotional state, thereby achieving low-cost, high-performance deployment and reducing the burden on developers.

[0685] The processing flow will be explained below.

[0686] Step 1:

[0687] The user accesses the system's website in a browser and authenticates their GitHub account using OAuth 2.0. The user clicks the "Log in with GitHub" button and the GitHub authentication page is displayed. The user then enters their GitHub username and password to authenticate. If authentication is successful, the system obtains a GitHub authentication token.

[0688] Step 2:

[0689] The server uses the obtained authentication token to access the GitHub API and retrieve the user's repository list. The server sends a request to the GitHub API and receives the user's repository list in JSON format. The server parses this data and formats it into a format that can be displayed to the user.

[0690] Step 3:

[0691] The user selects the project they want to deploy from the displayed list of repositories. The user clicks the "Select repository to deploy to" button and enters the required configuration information (e.g., branch name, environment variables, etc.).

[0692] Step 4:

[0693] The server clones the selected repository. The server clones the repository locally using the Git command (e.g., git clone https: / / github.com / username / repositoryname.git). Once the clone is complete, the entire source code will be stored in the local directory.

[0694] Step 5:

[0695] The server uses generative AI to analyze the cloned source code. The generative AI analyzes the file structure of the source code and identifies the programming languages, libraries, and components (front-end, back-end, database, etc.) used. For example, it extracts dependencies from the package.json file and identifies the technology stack used.

[0696] Step 6:

[0697] The server uses an emotion engine to recognize the user's emotions, analyzes the user's emotional state based on their operation history and input, and determines whether they are feeling stressed or anxious.

[0698] Step 7:

[0699] The server selects the optimal deployment platform based on the analysis results of the Hikari technology stack and the user's emotional state. Multiple platform candidates are retrieved from an internal database and compared based on criteria such as cost performance, supported languages, and ease of deployment. For example, if the user is feeling stressed, a platform that simplifies the process will be prioritized.

[0700] Step 8:

[0701] The server automatically deploys to the selected deployment platform. For example, if Netlify is selected, it uses the Netlify API to build and deploy the source code, sends the necessary configuration settings (such as environment variables and build commands), and starts the deployment process.

[0702] Step 9:

[0703] The server notifies the user that the deployment is complete by sending an email, posting a notification on the dashboard, etc. The user receives the notification and accesses the provided URL to check the operation of the deployed web service or web tool.

[0704] In this way, the system can automatically deploy user-created web services and web tools with low cost and high performance, and also provide support that takes into account the user's emotional state.

[0705] Example 2

[0706] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0707] In modern web development, deployment tasks for individual developers and educational institutions are complex and involve many manual steps, making it difficult to perform efficiently. Furthermore, the selection of the optimal deployment platform does not take into account the emotional state of the user, which often leads to stress and reduced development productivity. Furthermore, there is a lack of a unified selection method for the platform that takes into account evaluation criteria such as cost performance and supported languages.

[0708] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0709] In this invention, the server includes means for linking a repository in which a user stores source code with authentication means, means for retrieving the source code from the repository using the authentication means, means for analyzing the retrieved source code and identifying the programming language and library used, means for selecting an optimal deployment platform based on the programming language and library, means for automatically deploying to the selected deployment platform, and an emotion engine for recognizing the emotional state of the user and suggesting the optimal deployment platform based on the emotional state. This makes the deployment of web services and web tools more efficient and provides support according to the user's emotional state, enabling low-stress, high-performance deployment.

[0710] A "repository" is a digital storage for storing and managing source code and related data.

[0711] "Authentication" refers to the processes and technologies used to verify a user's identity and grant appropriate access privileges.

[0712] A "server" is a computer system that provides data and services in response to requests from clients.

[0713] "Source code" is a set of instructions written in a programming language that defines the behavior of a piece of software or a web application.

[0714] "Generative AI" is a type of artificial intelligence that uses machine learning and deep learning techniques to extract patterns and information from data and provide new data and insights.

[0715] A "deployment platform" refers to the infrastructure and services that deploy software and web applications into an execution environment and make them accessible to users.

[0716] An "emotion engine" refers to technology and algorithms that analyze data such as user input and operation history to recognize the user's emotional state.

[0717] "Automated deployment" is the process of deploying software or web applications to a specified execution environment without manual intervention.

[0718] This invention is a system for efficient deployment of web services and web tools for individual developers and in educational settings, and in particular has the function of recognizing user emotions and proposing the optimal deployment platform based on that. This system works in conjunction with a repository that stores source code, analyzes the source code, selects the optimal deployment platform, and automatically deploys it. In addition, it incorporates an emotion engine that recognizes and analyzes user emotions.

[0719] System configuration

[0720] The system consists of the following main components:

[0721] 1. Authentication Methods

[0722] 2. How to obtain source code

[0723] 3. Generation AI analysis means

[0724] 4. How to Select the Optimal Deployment Platform

[0725] 5. Automated Deployment Methods

[0726] 6. Emotion Engine

[0727] Authentication Method

[0728] The authentication method involves the process where the user accesses the system's website in a browser and authenticates their repository service account (e.g., GitHub) using OAuth 2.0. The user clicks the "Log in with Repository Service" button, and an authentication page is displayed. The user then enters their repository service username and password to authenticate. If authentication is successful, the server obtains an authentication token for the repository service.

[0729] How to get source code

[0730] The source code acquisition means includes a process in which the server clones the source code from the repository specified by the user using the authentication token acquired by the server. The cloned source code is temporarily stored on the server side for analysis.

[0731] Generation AI analysis means

[0732] The generative AI analysis method involves a process in which the server uses generative AI to analyze the cloned source code, which identifies the programming languages, libraries, and components (front-end, back-end, database, etc.) used within the source code.

[0733] How to select the optimal deployment platform

[0734] The optimal deployment platform selection method involves the server comparing and evaluating multiple deployment platforms from an internal database based on the analysis results. Evaluation criteria include cost performance, supported languages, ease of deployment, etc. Finally, the most suitable platform is selected. For example, if the source code is composed of Node.js and React, the server will determine that Netlify or Vercel is optimal. Also, if a database service is used as the backend, the server will also consider the deployment platform appropriate for that service.

[0735] Automated deployment methods

[0736] The automated deployment process involves the server automatically deploying to the selected deployment platform. For example, if Netlify is selected, it uses the Netlify API to build and deploy the source code, sends the necessary configuration settings (such as environment variables and build commands), and starts the deployment process.

[0737] Emotion Engine

[0738] The emotion engine involves a process in which the server recognizes the user's emotional state and suggests the optimal deployment platform based on that. For example, if the user is feeling stressed, the server analyzes the user's emotional state from their input and operation history, and simplifies the deployment procedure or provides gentle guidance.

[0739] Specific examples

[0740] 1. Users practice on the learning platform and push the completed web tool to the repository.

[0741] 2. The user accesses the system website, authenticates with the repository service, and selects the repository to deploy to.

[0742] 3. The server retrieves the repository and the generative AI analyzes the source code.

[0743] 4. The emotion engine analyzes the user's emotional state and suggests a simplified deployment procedure if it determines that the user is feeling stressed.

[0744] 5. The server selects the optimal deployment platform, for example, Netlify.

[0745] 6. The server automatically deploys the web app using the Netlify API.

[0746] 7. The user receives a notification that the deployment is complete and can confirm by accessing the provided URL.

[0747] Prompt Sentence Examples

[0748] "I'd like to deploy a web app built with Node.js and React. Can you briefly explain the automated deployment process? Also, could you suggest the best deployment platform to use?"

[0749] In this way, this invention is a system that automates the deployment process of web services and web tools and provides support that is tailored to the user's emotional state, thereby achieving efficient and stress-free deployment.

[0750] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0751] Step 1:

[0752] A user accesses the system's website in a browser and clicks the "Log in with Repository Service" button. The input is the user's click action, and the output is the display of an authentication page. As a specific operation, the browser redirects the user to the authentication page of the repository service.

[0753] Step 2:

[0754] The user enters a username and password on the repository service authentication page to perform authentication. The input is the user's authentication information, and the output is an authentication token. Specifically, the authentication process ends and the server receives the authentication token from the repository service.

[0755] Step 3:

[0756] The server uses the authentication token it has obtained to clone source code from the repository specified by the user. The input is the authentication token and repository specification, and the output is the cloned source code. Specifically, the server uses an API to obtain the contents of the repository and temporarily stores them.

[0757] Step 4:

[0758] The server analyzes the cloned source code using generative AI. The input is the cloned source code, and the output is the identification of programming languages, libraries, and components. Specifically, the generative AI analyzes the source code and identifies the languages, libraries, and system configurations used.

[0759] Step 5:

[0760] Based on the analysis results, the server compares and evaluates multiple deployment platforms from an internal database. The input is the analysis results, and the output is the selection of the optimal deployment platform. Specifically, the server evaluates the platforms based on cost performance, supported languages, and ease of deployment, and selects the appropriate platform.

[0761] Step 6:

[0762] The emotion engine analyzes the user's emotional state based on their input and operation history. The input is the user's operation history data, and the output is the evaluation result of the emotional state. Specifically, the emotion engine calculates stress levels and other emotional indicators and generates appropriate feedback.

[0763] Step 7:

[0764] Based on the evaluation result of the emotional state, the server may simplify the deployment procedure or guide the user with a gentle message. The input is the evaluation result of the emotional state and the deployment procedure, and the output is a notification to the user and a simplified deployment procedure. Specifically, the server sends an easy-to-understand message to the user.

[0765] Step 8:

[0766] The server automatically deploys to the optimal deployment platform. The input is the selected deployment platform and source code, and the output is a deployment completion notification. Specifically, the server automatically builds and deploys the source code using the API of the selected platform.

[0767] Step 9:

[0768] After the deployment is complete, the server notifies the user of the results. The input is the deployment completion status, and the output is the user notification. Specifically, the server notifies the user of the deployment results via email or a notification on the dashboard.

[0769] Step 10:

[0770] The user accesses the provided URL and checks the deployed web service or web tool. The input is the notified URL, and the output is confirmation of the deployment results. Specifically, the user accesses the URL in a browser and checks the actual operation of the service.

[0771] (Application example 2)

[0772] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0773] In traditional software development, developers must manually select an appropriate deployment platform and execute the deployment procedure, which is time-consuming, labor-intensive, and time-consuming. Furthermore, when developers feel stressed or impatient, the efficiency of the deployment process decreases and errors become more likely. To address these issues, a system is needed that can automatically adjust the deployment procedure and suggest the optimal deployment platform based on the user's emotional state.

[0774] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for authenticating a database in which a user stores source code, means for retrieving the source code from the database using the authentication means, means for analyzing the retrieved source code and identifying the programming language and library used, means for selecting an optimal deployment platform based on the programming language and library, means for automatically deploying on the selected deployment platform, means for analyzing the emotional state of the user, and means for adjusting the deployment procedure based on the emotional state. This automates the deployment process and makes it possible to provide optimal support according to the user's emotional state.

[0775] "Source code" means a description of the program used in the design and development of software.

[0776] A "database" is an information system that stores data in an organized manner so that it can be efficiently managed, searched, and retrieved.

[0777] "Authentication" is the process of verifying that a particular user or system is legitimate.

[0778] A "programming language" is an artificial language used to create software and applications.

[0779] A "library" is a collection of reusable routines or code in programming.

[0780] A "deployment platform" is an environment for running software applications.

[0781] "Analysis" is the process of examining and evaluating data or information in detail.

[0782] "Automated deployment" is the act of placing software or applications into a specified environment without manual intervention.

[0783] "Emotional state" refers to the user's mental and emotional state.

[0784] "Adjustment" is the act of changing settings or procedures to accommodate specific conditions.

[0785] An embodiment of the present invention will be described.

[0786] System Configuration

[0787] The system consists of the following main components:

[0788] 1. Authentication Methods

[0789] 2. How to obtain source code

[0790] 3. Generation AI analysis means

[0791] 4. How to Select the Optimal Deployment Platform

[0792] 5. Automated Deployment Methods

[0793] 6. Emotion Engine

[0794] Authentication Method

[0795] The user accesses the system's website in a browser and authenticates the GitHub account using OAuth 2.0. The user clicks the "Log in with GitHub" button, and the GitHub authentication page is displayed. The user then enters their GitHub username and password to authenticate. If authentication is successful, the server obtains a GitHub authentication token.

[0796] How to get source code

[0797] The server uses the obtained authentication token to clone the source code from the repository specified by the user. The cloned source code is temporarily stored on the server side for analysis.

[0798] Generation AI analysis means

[0799] The server then uses a generative AI model to analyze the cloned source code, which identifies the programming languages, libraries, and components (e.g., front-end, back-end, database, etc.) used within the source code.

[0800] How to select the optimal deployment platform

[0801] Based on the analysis results, the server compares and evaluates multiple deployment platforms from an internal database. Evaluation criteria include cost performance, supported languages, ease of deployment, etc., and ultimately selects the most suitable platform. For example, if the source code is composed of Python and Flask, the server will determine that Heroku is the best choice.

[0802] Automated deployment methods

[0803] The server automatically deploys to the selected deployment platform (for example, if Heroku is selected, it uses the Heroku API to build and deploy the source code, sends the necessary configuration settings (such as environment variables and build commands), and starts the deployment process.

[0804] Emotion Engine

[0805] The server uses an emotion engine to recognize the user's emotional state and propose the optimal deployment procedure based on that. It analyzes the user's emotional state from their input and operation history. For example, if the user is feeling stressed, it simplifies the deployment procedure or provides a gentle message.

[0806] Notification of deployment results

[0807] Once the deployment is complete, the server will notify the user via email, a notification on the dashboard, etc. Users can receive the notification and access the provided URL to check the deployed system and tools.

[0808] Specific operation example

[0809] 1. A user pushes a new robot control program to GitHub and deploys it using the RoboDeploy app.

[0810] 2. The user opens the app, authenticates with GitHub, and selects the repository to deploy.

[0811] 3. The server retrieves the repository and the generative AI analyzes the source code.

[0812] 4. The emotion engine analyzes the user's emotional state and, if it determines that the user is feeling stressed, suggests a simplified deployment procedure.

[0813] 5. The server selects the optimal deployment platform and determines it to be Heroku.

[0814] 6. The server automatically performs the deployment using the Heroku API.

[0815] 7. The user receives a notification that the deployment is complete and visits the provided URL to see the results.

[0816] Prompt Sentence Examples

[0817] An engineer deploying a new robot control program opens the RoboDeploy app. After successfully authenticating with GitHub and specifying the target repository, the app analyzes the code and automatically deploys it to the optimal Heroku repository. Describe the process of the emotion engine detecting the user's stress level and deploying it in a simplified manner.

[0818] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0819] Step 1: Authentication

[0820] The server authenticates a user's GitHub account when the user accesses the system's website in a browser. The user clicks the "Log in with GitHub" button, and the GitHub authentication page is displayed. Next, the user enters their GitHub username and password to authenticate. The input is the username and password, and the output is an authentication token. The server obtains this authentication token.

[0821] Step 2: Get the source code

[0822] The server uses the authentication token to clone the source code from the repository specified by the user. The input is the authentication token and the repository URL, and the output is the source code. The cloned source code is temporarily stored on the server side for analysis. Specifically, the server calls the GitHub API to retrieve the contents of the specified repository.

[0823] Step 3: Analysis by generative AI

[0824] The server analyzes the cloned source code using a generative AI model. The input is the source code, and the output is the programming language, library, and components used. The generative AI model performs data analysis to identify various elements in the source code (language, library, framework, etc.). Specifically, the server inputs the source code into the AI ​​model and obtains the analysis results.

[0825] Step 4: Select the optimal deployment platform

[0826] The server compares and evaluates multiple deployment platforms from an internal database based on the analysis results. The input is the analysis results, and the output is the optimal deployment platform. Evaluation criteria include cost performance, supported languages, ease of deployment, etc. Specifically, the server calculates an evaluation score for each platform based on the analysis results and selects the most suitable platform.

[0827] Step 5: Automatic deployment

[0828] The server automatically performs deployment to the selected deployment platform. The input is the selected deployment platform information and source code, and the output is the deployment result. For example, if Heroku is selected, the server will use the Heroku API to build and deploy the source code. Specifically, the server will call the API to instruct the build and deployment.

[0829] Step 6: Analyze emotional state

[0830] The server uses an emotion engine to analyze the user's emotional state. The input is the user's operation history and input data, and the output is the analyzed emotional state. Specifically, the server inputs operation logs and session data into the emotion engine to recognize the user's stress level and emotional state.

[0831] Step 7: Adjust the deployment procedure

[0832] The server adjusts the deployment procedure based on the analyzed emotional state. The input is the emotional state, and the output is the adjusted deployment procedure. For example, if the user is feeling stressed, the server simplifies the deployment procedure or guides the user with a gentle message. Specifically, the server generates a simplified procedure and presents it to the user.

[0833] Step 8: Notification

[0834] The server notifies the user when the deployment is complete. The input is the deployment completion status, and the output is a notification message. Notification methods include email and notifications on the dashboard. As a specific operation, the server generates a notification message and sends it to the user via the specified method.

[0835] The above is a specific description of each processing step in the deployment process.

[0836] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0837] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0838] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0839] [Third embodiment]

[0840] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0841] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0842] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0843] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0844] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0845] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0846] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0847] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0848] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0849] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0850] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0851] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0852] This invention is a system for efficient deployment of web services and web tools for individual developers and educational institutions. This system works in conjunction with a repository that stores source code, analyzes the source code, selects the optimal deployment platform, and automatically deploys the code.

[0853] System configuration

[0854] The system consists of the following main components:

[0855] 1. Authentication Methods

[0856] 2. How to obtain source code

[0857] 3. Generation AI analysis means

[0858] 4. How to Select the Optimal Deployment Platform

[0859] 5. Automated Deployment Methods

[0860] Authentication Method

[0861] User: Accesses the system's website in a browser. The user authenticates using an account in a version control system such as GitHub. OAuth 2.0 is used for authentication, which ensures secure authentication.

[0862] How to get source code

[0863] Server: Using the authentication token, the source code is cloned from the repository specified by the user. The cloned source code is temporarily stored on the server side for analysis.

[0864] Generation AI analysis means

[0865] Server: The generated AI analyzes the cloned source code, identifying the programming languages, libraries, and components (front-end, back-end, database, etc.) used within the source code.

[0866] How to select the optimal deployment platform

[0867] Server: Based on the analysis results, multiple deployment platforms are compared and evaluated from an internal database. Evaluation criteria include cost performance, supported languages, ease of deployment, etc. The optimal deployment platform is selected.

[0868] For example, if the source code is composed of Node.js and React, the server will determine that Netlify or Vercel is the best choice. Also, if MongoDB is used as the backend, it will consider using MongoDB Atlas or Heroku.

[0869] Automated deployment methods

[0870] Server: Automatically deploys to the selected deployment platform. The server uses the deployment platform's API to automatically build and deploy the source code.

[0871] Notification of deployment results

[0872] Server: Once the deployment is complete, the user is notified via email, dashboard alerts, etc.

[0873] Users: They receive a notification and can visit the provided URL to view the deployed web service or web tool.

[0874] Specific operation example

[0875] 1. A user performs practical training on the engineering education platform and pushes the completed web tool to the repository.

[0876] 2. The user visits the system's website, authenticates with GitHub, and selects the repository to deploy to.

[0877] 3. The server retrieves the repository and the generative AI analyzes the source code.

[0878] 4. The server selects the optimal deployment platform and determines it to be Netlify.

[0879] 5. The server uses the Netlify API to automatically deploy the front-end app.

[0880] 6. The user receives a notification that the deployment is complete and can confirm by visiting the provided URL.

[0881] In this way, the present invention is a system that automates the deployment process of web services and web tools, achieving low cost and high performance, thereby providing great convenience to individual developers and users in educational settings.

[0882] The processing flow will be explained below.

[0883] Step 1:

[0884] The user accesses the system's website in a browser and authenticates their GitHub account using OAuth 2.0. The user clicks the "Log in with GitHub" button and the GitHub authentication page is displayed. The user then enters their GitHub username and password to authenticate. If authentication is successful, the system obtains a GitHub authentication token.

[0885] Step 2:

[0886] The server uses the obtained authentication token to access the GitHub API and retrieve the user's repository list. The server sends a request to the GitHub API and receives the user's repository list in JSON format. The server parses this data and formats it into a format that can be displayed to the user.

[0887] Step 3:

[0888] The user selects the project they want to deploy from the displayed list of repositories. The user clicks the "Select repository to deploy to" button and enters the required configuration information (e.g., branch name, environment variables, etc.).

[0889] Step 4:

[0890] The server clones the selected repository. The server clones the repository locally using the Git command (e.g., git clone https: / / github.com / username / repositoryname.git). Once the clone is complete, the entire source code will be stored in the local directory.

[0891] Step 5:

[0892] The server uses generative AI to analyze the cloned source code. The generative AI analyzes the file structure of the source code and identifies the programming languages, libraries, and components (front-end, back-end, database, etc.) used. For example, it extracts dependencies from the package.json file and identifies the technology stack used.

[0893] Step 6:

[0894] The server selects the optimal deployment platform based on the analysis results. It retrieves multiple platform candidates from an internal database and compares them based on evaluation criteria such as cost performance, supported languages, and ease of deployment. Finally, it selects the most suitable platform.

[0895] Step 7:

[0896] The server automatically deploys to the selected deployment platform. For example, if Netlify is selected, it uses the Netlify API to build and deploy the source code, sends the necessary configuration settings (such as environment variables and build commands), and starts the deployment process.

[0897] Step 8:

[0898] The server notifies the user that the deployment is complete by sending an email, posting a notification on the dashboard, etc. The user receives the notification and accesses the provided URL to check the operation of the deployed web service or web tool.

[0899] In this way, the system provides an environment in which users can automatically deploy and easily publish web services and web tools they create at low cost and with high performance.

[0900] Example 1

[0901] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0902] Existing deployment methods for web services and web tools involve a lot of manual work, which is time-consuming and labor-intensive for individual developers and educational institutions. In addition, there is a lack of know-how to select the optimal deployment platform, which can result in high costs and low performance deployments.

[0903] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0904] In this invention, the server includes: means for linking a repository in which a user stores source code with authentication means; means for retrieving the source code from the repository using the authentication means; means for analyzing the retrieved source code using a generative AI model to identify the programming languages, libraries, and components used; means for selecting an optimal deployment platform based on the analysis results; means for automatically deploying to the selected deployment platform; and means for notifying the user of completion of deployment. This automates the deployment process for web services and web tools, enabling low-cost, high-performance deployment.

[0905] "Authentication method" refers to a technology that authenticates users accessing a website using a third-party authentication protocol (e.g., OAuth 2.0).

[0906] A "repository" is a place where source code and other related data are stored and managed in a version control system (e.g., GitHub).

[0907] "Source code" is a set of instructions written in a programming language that defines the behavior of a particular software or web application.

[0908] A "generative AI model" is a system that uses artificial intelligence to automatically generate and analyze text and code, and specific examples include natural language processing models (e.g., GPT-4).

[0909] "Analysis" is the process of examining source code in detail to identify the programming languages, libraries, and components used.

[0910] A "programming language" is an artificial language used to develop software and web applications, and examples include JavaScript, Python, and Java.

[0911] A "library" is a collection of reusable code that is used to easily access specific functionality or services.

[0912] "Components" are major parts of the source code, such as the front end, back end, and database.

[0913] A "deployment platform" is an online service for building and deploying source code into an executable form, and examples include Netlify, Vercel, and Heroku.

[0914] "Deployment" is the process of placing developed software in an executable environment and making it accessible to users.

[0915] "Notification" refers to the means by which users are notified that the deployment is complete, including, for example, email or dashboard alerts.

[0916] The present invention provides a system for efficiently deploying Web services and Web tools for individual developers and in educational settings. Detailed embodiments of the system will be described below.

[0917] System configuration

[0918] The system consists of the following main components:

[0919] 1. Authentication Methods

[0920] 2. How to obtain source code

[0921] 3. Generation AI analysis means

[0922] 4. How to Select the Optimal Deployment Platform

[0923] 5. Automated Deployment Methods

[0924] 6. Deployment result notification method

[0925] Authentication Method

[0926] A user accesses the system's website in a browser and authenticates with their GitHub account. OAuth 2.0 is used as the authentication method, which ensures secure authentication. Specifically, the user clicks the "Log in with GitHub" button and is redirected to the GitHub authentication page. GitHub then returns an authentication token, which the server receives and establishes a user session.

[0927] How to get source code

[0928] The server clones the repository specified by the user through the GitHub API using the obtained authentication token, and the source code is temporarily stored on the server for analysis.

[0929] Generation AI analysis means

[0930] The server inputs the cloned source code into a generative AI model (e.g., GPT-4) and performs analysis using the following prompt: "Analyze the source code below to identify the programming languages, libraries, and components used. Then, suggest the optimal deployment platform. Enter your source code here: <source code>." The generative AI model returns the analysis results to the server.

[0931] How to select the optimal deployment platform

[0932] The server evaluates multiple deployment platforms from an internal database based on the analysis results of the generative AI model. Evaluation criteria include cost-effectiveness, supported programming languages, and ease of deployment. For example, if the source code is composed of Node.js and React, Netlify or Vercel may be deemed suitable. If MongoDB is used as the backend, MongoDB Atlas and other platforms may also be considered.

[0933] Automated deployment methods

[0934] The server automatically deploys to the selected deployment platform, specifically by building and deploying the source code using the selected platform's API (e.g., Netlify API or Vercel API).

[0935] Deployment result notification method

[0936] The server will notify the user once the deployment is complete, either by email or via an alert on the website dashboard.

[0937] After receiving the notification, the user can access the provided URL to check the deployed web service or web tool.

[0938] Specific operation example

[0939] 1. A user develops a web tool on the educational platform and pushes the code to a GitHub repository.

[0940] 2. The user visits the system's website, authenticates with their GitHub account, and selects the repository they want to clone.

[0941] 3. The server clones the repository and analyzes the source code using a generative AI model such as GPT-4.

[0942] 4. Based on the analysis results, the server selects the optimal deployment platform (e.g., Netlify).

[0943] 5. The server performs the deployment using the Netlify API.

[0944] 6. The server notifies the user after the deployment is complete, and the user can access the provided URL to check the deployment results.

[0945] The system automates the deployment process of web services and web tools, ensuring efficient and high-performance deployment.

[0946] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0947] Step 1: Authentication

[0948] Input: The user visits the system's website in a browser and clicks the "Log in with GitHub" button.

[0949] How it works: When a user clicks the "Log in with GitHub" button, the system redirects the user to GitHub's authentication page using the OAuth 2.0 protocol.

[0950] Data processing: After the user enters their authentication information, GitHub returns an authentication token.

[0951] Output: An authentication token is sent to the server and a session is established for the user.

[0952] Step 2: Get the source code

[0953] Input: After the user has authenticated, they select the repository they want to deploy to.

[0954] What it does: The server uses the authentication token to clone the specified GitHub repository via the API.

[0955] Data processing: The source code is obtained through the GitHub API and temporarily stored on the server.

[0956] Output: A temporarily saved source code is prepared.

[0957] Step 3: Analyzing the source code

[0958] Input: Source code stored on the server.

[0959] How it works: The server inputs the source code into a generative AI model (e.g., GPT-4) and performs the analysis.

[0960] Data processing: Use the following prompt: "Analyze the source code below to identify the programming languages, libraries, and components used. Then, suggest the best deployment platform. Enter the source code here: <source code>". The generative AI model performs the analysis.

[0961] Output: The analysis results provide information about the programming languages, libraries, and components used.

[0962] Step 4: Select the optimal deployment platform

[0963] Input: Analysis results from the generative AI model.

[0964] How it works: Based on the analysis results, the server evaluates multiple deployment platforms from an internal database.

[0965] Data processing: Evaluation criteria include cost-effectiveness, supported programming languages, and ease of deployment.

[0966] Output: The optimal deployment platform (e.g. Netlify) is selected.

[0967] Step 5: Automated deployment

[0968] Input: Selected deployment platform information and source code.

[0969] How it works: The server uses the API of the platform of your choice (e.g., Netlify) to build and deploy the source code.

[0970] Data processing: Build and deploy source code through the Netlify API.

[0971] Output: Deployment is complete and a URL for the published web service is generated.

[0972] Step 6: Notification of deployment results

[0973] Input: Deployment completion information and the URL of the published web service.

[0974] Action: The server sends a notification to the user that the deployment is complete.

[0975] Data processing: Generate emails and dashboard alerts as notification methods.

[0976] Output: The user will be notified and can check the deployment result via the provided URL.

[0977] In this way, the processing flow of the entire system is completed, allowing users to achieve efficient and high-performance deployment.

[0978] (Application example 1)

[0979] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0980] In modern logistics centers, software updates and deployments for inventory management systems, delivery management systems, and other systems are complicated and require a large amount of resources and time. This has created a need for automating the software deployment process to make it efficient and fast. Furthermore, selecting the optimal deployment platform for the programming languages ​​and libraries used requires specialized knowledge and is not a straightforward task. Solving these challenges and improving the operational efficiency of logistics centers is essential.

[0981] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0982] In this invention, the server includes means for linking a repository in which a user stores source code with authentication means, means for acquiring the source code from the repository using the authentication means, means for analyzing the acquired source code and identifying the programming language and library used, means for analyzing the source code of software used in an inventory management system or a delivery management system in a logistics center and selecting an optimal deployment platform, and means for automatically deploying the software to the selected deployment platform. This enables the software deployment process to be automated, efficiently, and quickly.

[0983] A "repository" is a data storage for storing and versioning source code and other development files.

[0984] An "authentication method" is a mechanism for verifying a user's identity and securely accessing a repository, such as OAuth 2.0.

[0985] "Source code acquisition means" is a mechanism for acquiring source code from a repository through authentication means.

[0986] The "source code analysis means" is a mechanism for analyzing acquired source code and identifying the programming language and library used.

[0987] A "deployment platform" is an infrastructure service for deploying source code into an execution environment.

[0988] The "optimal deployment platform selection means" is a mechanism for selecting the optimal deployment platform based on the results of source code analysis.

[0989] An "automated deployment mechanism" is a mechanism for automatically building and deploying source code to a selected deployment platform.

[0990] A "logistics center" is a facility that manages inventory and delivery of goods.

[0991] An "inventory management system" is a system that manages the receipt, storage, and shipping of inventory at a logistics center.

[0992] A "delivery management system" is a system that plans and tracks product deliveries at a logistics center.

[0993] The present invention relates to a system for automating the software deployment process for inventory management systems and delivery management systems in a logistics center. This system consists of the following main components:

[0994] System configuration

[0995] Authentication method: Users access the system's website from a browser or device. They authenticate using OAuth 2.0 with their repository management system account. This authentication allows them to access the repository securely.

[0996] Source code acquisition method: The server clones the source code from the specified repository using the authenticated token. The cloned source code is temporarily stored on the server side and becomes the target of analysis.

[0997] Generative AI analysis method: The server analyzes the cloned source code. The generative AI identifies the programming languages, libraries, and components (front-end, back-end, database, etc.) in the source code.

[0998] Optimal deployment platform selection method: The server selects the optimal deployment platform based on the analysis results. Evaluation criteria include cost performance, supported languages, ease of deployment, etc.

[0999] Automatic deployment method: The server automatically builds and deploys the source code using the API of the selected deployment platform.

[1000] Notification of deployment results: Once the deployment is complete, the server will notify the user. Notification methods include email and dashboard alerts. The user can check the deployed system by accessing the provided URL.

[1001] Specific examples of embodiments

[1002] 1. Examples of authentication:

[1003] A user accesses the system's website from a browser and authenticates with an account in the repository management system using OAuth 2.0, which ensures security and grants access to the specified repository.

[1004] 2. Example of source code acquisition:

[1005] After authentication is complete, the server uses the OAuth 2.0 access token to clone the source code from the repository, using Git.

[1006] 3. Specific examples of analysis by generative AI:

[1007] The cloned source code is analyzed by generative AI, which identifies the programming languages ​​(e.g., Node.js, Python), libraries (e.g., React, Django), and other components used.

[1008] 4. Examples of choosing the best deployment platform:

[1009] The server then selects the appropriate deployment platform from its internal database based on the analysis results. For example, if the source code is written in Node.js and React, it may determine that Netlify or another front-end deployment platform is the best fit.

[1010] 5. Examples of automated deployment:

[1011] The server uses the API of the selected deployment platform to automatically build and deploy the source code. If the deployment is successful, a deployment URL is generated and notified to the user.

[1012] 6. Examples of deployment notifications:

[1013] After the deployment is complete, the server notifies the user via email or an alert function on the website dashboard, making it easy for users to check the deployed system.

[1014] Prompt Sentence Examples

[1015] Analyze the following source code, select the optimal deployment platform, and provide instructions for automated deployment.

[1016] Repository URL: https: / / github.com / user / repo

[1017] Language used: Node.js, React

[1018] Potential deployment platforms: Netlify, Heroku

[1019] As described above, the present invention is a system that automates the software deployment process in a logistics center, thereby realizing efficiency and speed, thereby enabling smooth business operations.

[1020] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1021] Step 1:

[1022] Performing authentication

[1023] Input: User's repository management system account information

[1024] How it works: A user accesses the system's website through a browser or device and authenticates using OAuth 2.0. If authentication is successful, an access token is issued.

[1025] Data processing / calculation: Obtaining and saving access tokens

[1026] Output: An access token with permissions to access the repository.

[1027] Step 2:

[1028] Get the source code

[1029] Input: Repository URL and Access Token

[1030] How it works: The server uses the access token to clone source code from the specified repository using a Git client, and the cloned source code is temporarily stored on the server.

[1031] Data processing / calculation: Download and save source code

[1032] Output: Source code stored on the server

[1033] Step 3:

[1034] Source code analysis

[1035] Input: Saved source code

[1036] How it works: The server uses a generative AI model to analyze the source code, which identifies the programming languages, libraries, and components used.

[1037] Data processing / computation: Identifying programming languages, libraries, and components

[1038] Output: Analysis results (list of languages ​​and libraries used)

[1039] Step 4:

[1040] Selecting the optimal deployment platform

[1041] Input: Source code analysis results

[1042] How it works: Based on the analysis results, the server compares and evaluates multiple deployment platforms from an internal database. Evaluation criteria include cost-effectiveness, supported languages, ease of deployment, etc. It then uses a generative AI model to select the most suitable deployment platform.

[1043] Data Processing / Computation: Evaluating and Selecting Deployment Platforms

[1044] Output: Selected deployment platform

[1045] Step 5:

[1046] Implementing automatic deployment

[1047] Input: Selected deployment platform and source code

[1048] How it works: The server builds and deploys the source code using the API of the chosen deployment platform, for example Netlify or other cloud deployment services.

[1049] Data processing / calculation: Building and deploying source code via API

[1050] Output: URL of the deployed system

[1051] Step 6:

[1052] Deployment status notifications

[1053] Input: Deploy result

[1054] How it works: After the deployment is complete, the server notifies the user of the deployment results, providing information to the user via email and dashboard alerts.

[1055] Data processing / calculation: Generation and transmission of notification data

[1056] Output: The deployment URL notified to the user

[1057] Through this procedure, the software deployment process at the logistics center is automated, efficient, and fast.

[1058] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1059] This invention is a system for efficient deployment of web services and web tools for individual developers and in educational settings, and is equipped with a function to recognize user emotions and suggest the optimal deployment platform based on those emotions. This system works in conjunction with a repository that stores source code, analyzes the source code, selects the optimal deployment platform, and automatically deploys the code. In addition, it incorporates an emotion engine that recognizes and analyzes user emotions.

[1060] System configuration

[1061] The system consists of the following main components:

[1062] 1. Authentication Methods

[1063] 2. How to obtain source code

[1064] 3. Generation AI analysis means

[1065] 4. How to Select the Optimal Deployment Platform

[1066] 5. Automated Deployment Methods

[1067] 6. Emotion Engine

[1068] Authentication Method

[1069] User: Accesses the system's website in a browser and authenticates with their GitHub account using OAuth 2.0. The user clicks the "Log in with GitHub" button and the GitHub authentication page is displayed. Next, the user enters their GitHub username and password to authenticate. If authentication is successful, the system obtains a GitHub authentication token.

[1070] How to get source code

[1071] Server: Using the obtained authentication token, the source code is cloned from the repository specified by the user. The cloned source code is temporarily stored on the server side for analysis.

[1072] Generation AI analysis means

[1073] Server: The generated AI analyzes the cloned source code, identifying the programming languages, libraries, and components (front-end, back-end, database, etc.) used within the source code.

[1074] How to select the optimal deployment platform

[1075] Server: Based on the analysis results, multiple deployment platforms are compared and evaluated from an internal database. Evaluation criteria include cost performance, supported languages, ease of deployment, etc. Finally, the most suitable platform is selected.

[1076] For example, if the source code is composed of Node.js and React, the server will determine that Netlify or Vercel is the best choice. Also, if MongoDB is used as the backend, it will consider using MongoDB Atlas or Heroku.

[1077] Automated deployment methods

[1078] Server: Automates deployment to the selected deployment platform. For example, if Netlify is selected, it uses the Netlify API to build and deploy the source code, sends the necessary configuration settings (environment variables, build commands, etc.), and starts the deployment process.

[1079] Emotion Engine

[1080] Server: Using an emotion engine, it recognizes the user's emotions and suggests the optimal deployment platform based on them. It analyzes the user's emotional state from their input and operation history. For example, if the user is feeling stressed, it simplifies the deployment procedure or provides gentle guidance.

[1081] Notification of deployment results

[1082] Server: Once the deployment is complete, the user is notified via email, dashboard notification, etc.

[1083] Users: They receive a notification and can visit the provided URL to view the deployed web service or web tool.

[1084] Specific operation example

[1085] 1. A user performs practical training on the engineering education platform and pushes the completed web tool to the repository.

[1086] 2. The user visits the system's website, authenticates with GitHub, and selects the repository to deploy to.

[1087] 3. The server retrieves the repository and the generative AI analyzes the source code.

[1088] 4. The emotion engine analyzes the user's emotional state and suggests a simplified deployment procedure if it determines that the user is feeling stressed.

[1089] 5. The server selects the optimal deployment platform and determines it to be Netlify.

[1090] 6. The server uses the Netlify API to automatically deploy the front-end app.

[1091] 7. The user receives a notification that the deployment is complete and can confirm by visiting the provided URL.

[1092] In this way, the present invention is a system that automates the deployment process of web services and web tools and provides support that is tailored to the user's emotional state, thereby achieving low-cost, high-performance deployment and reducing the burden on developers.

[1093] The processing flow will be explained below.

[1094] Step 1:

[1095] The user accesses the system's website in a browser and authenticates their GitHub account using OAuth 2.0. The user clicks the "Log in with GitHub" button and the GitHub authentication page is displayed. The user then enters their GitHub username and password to authenticate. If authentication is successful, the system obtains a GitHub authentication token.

[1096] Step 2:

[1097] The server uses the obtained authentication token to access the GitHub API and retrieve the user's repository list. The server sends a request to the GitHub API and receives the user's repository list in JSON format. The server parses this data and formats it into a format that can be displayed to the user.

[1098] Step 3:

[1099] The user selects the project they want to deploy from the displayed list of repositories. The user clicks the "Select repository to deploy to" button and enters the required configuration information (e.g., branch name, environment variables, etc.).

[1100] Step 4:

[1101] The server clones the selected repository. The server clones the repository locally using the Git command (e.g., git clone https: / / github.com / username / repositoryname.git). Once the clone is complete, the entire source code will be stored in the local directory.

[1102] Step 5:

[1103] The server uses generative AI to analyze the cloned source code. The generative AI analyzes the file structure of the source code and identifies the programming languages, libraries, and components (front-end, back-end, database, etc.) used. For example, it extracts dependencies from the package.json file and identifies the technology stack used.

[1104] Step 6:

[1105] The server uses an emotion engine to recognize the user's emotions, analyzes the user's emotional state based on their operation history and input, and determines whether they are feeling stressed or anxious.

[1106] Step 7:

[1107] The server selects the optimal deployment platform based on the analysis results of the Hikari technology stack and the user's emotional state. Multiple platform candidates are retrieved from an internal database and compared based on criteria such as cost performance, supported languages, and ease of deployment. For example, if the user is feeling stressed, a platform that simplifies the process will be prioritized.

[1108] Step 8:

[1109] The server automatically deploys to the selected deployment platform. For example, if Netlify is selected, it uses the Netlify API to build and deploy the source code, sends the necessary configuration settings (such as environment variables and build commands), and starts the deployment process.

[1110] Step 9:

[1111] The server notifies the user that the deployment is complete by sending an email, posting a notification on the dashboard, etc. The user receives the notification and accesses the provided URL to check the operation of the deployed web service or web tool.

[1112] In this way, the system can automatically deploy user-created web services and web tools with low cost and high performance, and also provide support that takes into account the user's emotional state.

[1113] Example 2

[1114] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1115] In modern web development, deployment tasks for individual developers and educational institutions are complex and involve many manual steps, making it difficult to perform efficiently. Furthermore, the selection of the optimal deployment platform does not take into account the emotional state of the user, which often leads to stress and reduced development productivity. Furthermore, there is a lack of a unified selection method for the platform that takes into account evaluation criteria such as cost performance and supported languages.

[1116] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1117] In this invention, the server includes means for linking a repository in which a user stores source code with authentication means, means for retrieving the source code from the repository using the authentication means, means for analyzing the retrieved source code and identifying the programming language and library used, means for selecting an optimal deployment platform based on the programming language and library, means for automatically deploying to the selected deployment platform, and an emotion engine for recognizing the emotional state of the user and suggesting the optimal deployment platform based on the emotional state. This makes the deployment of web services and web tools more efficient and provides support according to the user's emotional state, enabling low-stress, high-performance deployment.

[1118] A "repository" is a digital storage for storing and managing source code and related data.

[1119] "Authentication" refers to the processes and technologies used to verify a user's identity and grant appropriate access privileges.

[1120] A "server" is a computer system that provides data and services in response to requests from clients.

[1121] "Source code" is a set of instructions written in a programming language that defines the behavior of a piece of software or a web application.

[1122] "Generative AI" is a type of artificial intelligence that uses machine learning and deep learning techniques to extract patterns and information from data and provide new data and insights.

[1123] A "deployment platform" refers to the infrastructure and services that deploy software and web applications into an execution environment and make them accessible to users.

[1124] An "emotion engine" refers to technology and algorithms that analyze data such as user input and operation history to recognize the user's emotional state.

[1125] "Automated deployment" is the process of deploying software or web applications to a specified execution environment without manual intervention.

[1126] This invention is a system for efficient deployment of web services and web tools for individual developers and in educational settings, and in particular has the function of recognizing user emotions and proposing the optimal deployment platform based on that. This system works in conjunction with a repository that stores source code, analyzes the source code, selects the optimal deployment platform, and automatically deploys it. In addition, it incorporates an emotion engine that recognizes and analyzes user emotions.

[1127] System configuration

[1128] The system consists of the following main components:

[1129] 1. Authentication Methods

[1130] 2. How to obtain source code

[1131] 3. Generation AI analysis means

[1132] 4. How to Select the Optimal Deployment Platform

[1133] 5. Automated Deployment Methods

[1134] 6. Emotion Engine

[1135] Authentication Method

[1136] The authentication method involves the process where the user accesses the system's website in a browser and authenticates their repository service account (e.g., GitHub) using OAuth 2.0. The user clicks the "Log in with Repository Service" button, and an authentication page is displayed. The user then enters their repository service username and password to authenticate. If authentication is successful, the server obtains an authentication token for the repository service.

[1137] How to get source code

[1138] The source code acquisition means includes a process in which the server clones the source code from the repository specified by the user using the authentication token acquired by the server. The cloned source code is temporarily stored on the server side for analysis.

[1139] Generation AI analysis means

[1140] The generative AI analysis method involves a process in which the server uses generative AI to analyze the cloned source code, which identifies the programming languages, libraries, and components (front-end, back-end, database, etc.) used within the source code.

[1141] How to select the optimal deployment platform

[1142] The optimal deployment platform selection method involves the server comparing and evaluating multiple deployment platforms from an internal database based on the analysis results. Evaluation criteria include cost performance, supported languages, ease of deployment, etc. Finally, the most suitable platform is selected. For example, if the source code is composed of Node.js and React, the server will determine that Netlify or Vercel is optimal. Also, if a database service is used as the backend, the server will also consider the deployment platform appropriate for that service.

[1143] Automated deployment methods

[1144] The automated deployment process involves the server automatically deploying to the selected deployment platform. For example, if Netlify is selected, it uses the Netlify API to build and deploy the source code, sends the necessary configuration settings (such as environment variables and build commands), and starts the deployment process.

[1145] Emotion Engine

[1146] The emotion engine involves a process in which the server recognizes the user's emotional state and suggests the optimal deployment platform based on that. For example, if the user is feeling stressed, the server analyzes the user's emotional state from their input and operation history, and simplifies the deployment procedure or provides gentle guidance.

[1147] Specific examples

[1148] 1. Users practice on the learning platform and push the completed web tool to the repository.

[1149] 2. The user accesses the system website, authenticates with the repository service, and selects the repository to deploy to.

[1150] 3. The server retrieves the repository and the generative AI analyzes the source code.

[1151] 4. The emotion engine analyzes the user's emotional state and suggests a simplified deployment procedure if it determines that the user is feeling stressed.

[1152] 5. The server selects the optimal deployment platform, for example, Netlify.

[1153] 6. The server automatically deploys the web app using the Netlify API.

[1154] 7. The user receives a notification that the deployment is complete and can confirm by accessing the provided URL.

[1155] Prompt Sentence Examples

[1156] "I'd like to deploy a web app built with Node.js and React. Can you briefly explain the automated deployment process? Also, could you suggest the best deployment platform to use?"

[1157] In this way, this invention is a system that automates the deployment process of web services and web tools and provides support that is tailored to the user's emotional state, thereby achieving efficient and stress-free deployment.

[1158] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1159] Step 1:

[1160] A user accesses the system's website in a browser and clicks the "Log in with Repository Service" button. The input is the user's click action, and the output is the display of an authentication page. As a specific operation, the browser redirects the user to the authentication page of the repository service.

[1161] Step 2:

[1162] The user enters a username and password on the repository service authentication page to perform authentication. The input is the user's authentication information, and the output is an authentication token. Specifically, the authentication process ends and the server receives the authentication token from the repository service.

[1163] Step 3:

[1164] The server uses the authentication token it has obtained to clone source code from the repository specified by the user. The input is the authentication token and repository specification, and the output is the cloned source code. Specifically, the server uses an API to obtain the contents of the repository and temporarily stores them.

[1165] Step 4:

[1166] The server analyzes the cloned source code using generative AI. The input is the cloned source code, and the output is the identification of programming languages, libraries, and components. Specifically, the generative AI analyzes the source code and identifies the languages, libraries, and system configurations used.

[1167] Step 5:

[1168] Based on the analysis results, the server compares and evaluates multiple deployment platforms from an internal database. The input is the analysis results, and the output is the selection of the optimal deployment platform. Specifically, the server evaluates the platforms based on cost performance, supported languages, and ease of deployment, and selects the appropriate platform.

[1169] Step 6:

[1170] The emotion engine analyzes the user's emotional state based on their input and operation history. The input is the user's operation history data, and the output is the evaluation result of the emotional state. Specifically, the emotion engine calculates stress levels and other emotional indicators and generates appropriate feedback.

[1171] Step 7:

[1172] Based on the evaluation result of the emotional state, the server may simplify the deployment procedure or guide the user with a gentle message. The input is the evaluation result of the emotional state and the deployment procedure, and the output is a notification to the user and a simplified deployment procedure. Specifically, the server sends an easy-to-understand message to the user.

[1173] Step 8:

[1174] The server automatically deploys to the optimal deployment platform. The input is the selected deployment platform and source code, and the output is a deployment completion notification. Specifically, the server automatically builds and deploys the source code using the API of the selected platform.

[1175] Step 9:

[1176] After the deployment is complete, the server notifies the user of the results. The input is the deployment completion status, and the output is the user notification. Specifically, the server notifies the user of the deployment results via email or a notification on the dashboard.

[1177] Step 10:

[1178] The user accesses the provided URL and checks the deployed web service or web tool. The input is the notified URL, and the output is confirmation of the deployment results. Specifically, the user accesses the URL in a browser and checks the actual operation of the service.

[1179] (Application example 2)

[1180] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1181] In traditional software development, developers must manually select an appropriate deployment platform and execute the deployment procedure, which is time-consuming, labor-intensive, and time-consuming. Furthermore, when developers feel stressed or impatient, the efficiency of the deployment process decreases and errors become more likely. To address these issues, a system is needed that can automatically adjust the deployment procedure and suggest the optimal deployment platform based on the user's emotional state.

[1182] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for authenticating a database in which a user stores source code, means for retrieving the source code from the database using the authentication means, means for analyzing the retrieved source code and identifying the programming language and library used, means for selecting an optimal deployment platform based on the programming language and library, means for automatically deploying on the selected deployment platform, means for analyzing the emotional state of the user, and means for adjusting the deployment procedure based on the emotional state. This automates the deployment process and makes it possible to provide optimal support according to the user's emotional state.

[1183] "Source code" means a description of the program used in the design and development of software.

[1184] A "database" is an information system that stores data in an organized manner so that it can be efficiently managed, searched, and retrieved.

[1185] "Authentication" is the process of verifying that a particular user or system is legitimate.

[1186] A "programming language" is an artificial language used to create software and applications.

[1187] A "library" is a collection of reusable routines or code in programming.

[1188] A "deployment platform" is an environment for running software applications.

[1189] "Analysis" is the process of examining and evaluating data or information in detail.

[1190] "Automated deployment" is the act of placing software or applications into a specified environment without manual intervention.

[1191] "Emotional state" refers to the user's mental and emotional state.

[1192] "Adjustment" is the act of changing settings or procedures to accommodate specific conditions.

[1193] An embodiment of the present invention will be described.

[1194] System Configuration

[1195] The system consists of the following main components:

[1196] 1. Authentication Methods

[1197] 2. How to obtain source code

[1198] 3. Generation AI analysis means

[1199] 4. How to Select the Optimal Deployment Platform

[1200] 5. Automated Deployment Methods

[1201] 6. Emotion Engine

[1202] Authentication Method

[1203] The user accesses the system's website in a browser and authenticates the GitHub account using OAuth 2.0. The user clicks the "Log in with GitHub" button, and the GitHub authentication page is displayed. The user then enters their GitHub username and password to authenticate. If authentication is successful, the server obtains a GitHub authentication token.

[1204] How to get source code

[1205] The server uses the obtained authentication token to clone the source code from the repository specified by the user. The cloned source code is temporarily stored on the server side for analysis.

[1206] Generation AI analysis means

[1207] The server then uses a generative AI model to analyze the cloned source code, which identifies the programming languages, libraries, and components (e.g., front-end, back-end, database, etc.) used within the source code.

[1208] How to select the optimal deployment platform

[1209] Based on the analysis results, the server compares and evaluates multiple deployment platforms from an internal database. Evaluation criteria include cost performance, supported languages, ease of deployment, etc., and ultimately selects the most suitable platform. For example, if the source code is composed of Python and Flask, the server will determine that Heroku is the best choice.

[1210] Automated deployment methods

[1211] The server automatically deploys to the selected deployment platform (for example, if Heroku is selected, it uses the Heroku API to build and deploy the source code, sends the necessary configuration settings (such as environment variables and build commands), and starts the deployment process.

[1212] Emotion Engine

[1213] The server uses an emotion engine to recognize the user's emotional state and propose the optimal deployment procedure based on that. It analyzes the user's emotional state from their input and operation history. For example, if the user is feeling stressed, it simplifies the deployment procedure or provides a gentle message.

[1214] Notification of deployment results

[1215] Once the deployment is complete, the server will notify the user via email, a notification on the dashboard, etc. Users can receive the notification and access the provided URL to check the deployed system and tools.

[1216] Specific operation example

[1217] 1. A user pushes a new robot control program to GitHub and deploys it using the RoboDeploy app.

[1218] 2. The user opens the app, authenticates with GitHub, and selects the repository to deploy.

[1219] 3. The server retrieves the repository and the generative AI analyzes the source code.

[1220] 4. The emotion engine analyzes the user's emotional state and, if it determines that the user is feeling stressed, suggests a simplified deployment procedure.

[1221] 5. The server selects the optimal deployment platform and determines it to be Heroku.

[1222] 6. The server automatically performs the deployment using the Heroku API.

[1223] 7. The user receives a notification that the deployment is complete and visits the provided URL to see the results.

[1224] Prompt Sentence Examples

[1225] An engineer deploying a new robot control program opens the RoboDeploy app. After successfully authenticating with GitHub and specifying the target repository, the app analyzes the code and automatically deploys it to the optimal Heroku repository. Describe the process of the emotion engine detecting the user's stress level and deploying it in a simplified manner.

[1226] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1227] Step 1: Authentication

[1228] The server authenticates a user's GitHub account when the user accesses the system's website in a browser. The user clicks the "Log in with GitHub" button, and the GitHub authentication page is displayed. Next, the user enters their GitHub username and password to authenticate. The input is the username and password, and the output is an authentication token. The server obtains this authentication token.

[1229] Step 2: Get the source code

[1230] The server uses the authentication token to clone the source code from the repository specified by the user. The input is the authentication token and the repository URL, and the output is the source code. The cloned source code is temporarily stored on the server side for analysis. Specifically, the server calls the GitHub API to retrieve the contents of the specified repository.

[1231] Step 3: Analysis by generative AI

[1232] The server analyzes the cloned source code using a generative AI model. The input is the source code, and the output is the programming language, library, and components used. The generative AI model performs data analysis to identify various elements in the source code (language, library, framework, etc.). Specifically, the server inputs the source code into the AI ​​model and obtains the analysis results.

[1233] Step 4: Select the optimal deployment platform

[1234] The server compares and evaluates multiple deployment platforms from an internal database based on the analysis results. The input is the analysis results, and the output is the optimal deployment platform. Evaluation criteria include cost performance, supported languages, ease of deployment, etc. Specifically, the server calculates an evaluation score for each platform based on the analysis results and selects the most suitable platform.

[1235] Step 5: Automatic deployment

[1236] The server automatically performs deployment to the selected deployment platform. The input is the selected deployment platform information and source code, and the output is the deployment result. For example, if Heroku is selected, the server will use the Heroku API to build and deploy the source code. Specifically, the server will call the API to instruct the build and deployment.

[1237] Step 6: Analyze emotional state

[1238] The server uses an emotion engine to analyze the user's emotional state. The input is the user's operation history and input data, and the output is the analyzed emotional state. Specifically, the server inputs operation logs and session data into the emotion engine to recognize the user's stress level and emotional state.

[1239] Step 7: Adjust the deployment procedure

[1240] The server adjusts the deployment procedure based on the analyzed emotional state. The input is the emotional state, and the output is the adjusted deployment procedure. For example, if the user is feeling stressed, the server simplifies the deployment procedure or guides the user with a gentle message. Specifically, the server generates a simplified procedure and presents it to the user.

[1241] Step 8: Notification

[1242] The server notifies the user when the deployment is complete. The input is the deployment completion status, and the output is a notification message. Notification methods include email and notifications on the dashboard. As a specific operation, the server generates a notification message and sends it to the user via the specified method.

[1243] The above is a specific description of each processing step in the deployment process.

[1244] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1245] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1246] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1247] [Fourth embodiment]

[1248] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1249] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1250] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1251] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1252] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1253] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1254] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1255] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1256] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1257] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1258] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1259] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1260] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1261] This invention is a system for efficient deployment of web services and web tools for individual developers and educational institutions. This system works in conjunction with a repository that stores source code, analyzes the source code, selects the optimal deployment platform, and automatically deploys the code.

[1262] System configuration

[1263] The system consists of the following main components:

[1264] 1. Authentication Methods

[1265] 2. How to obtain source code

[1266] 3. Generation AI analysis means

[1267] 4. How to Select the Optimal Deployment Platform

[1268] 5. Automated Deployment Methods

[1269] Authentication Method

[1270] User: Accesses the system's website in a browser. The user authenticates using an account in a version control system such as GitHub. OAuth 2.0 is used for authentication, which ensures secure authentication.

[1271] How to get source code

[1272] Server: Using the authentication token, the source code is cloned from the repository specified by the user. The cloned source code is temporarily stored on the server side for analysis.

[1273] Generation AI analysis means

[1274] Server: The generated AI analyzes the cloned source code, identifying the programming languages, libraries, and components (front-end, back-end, database, etc.) used within the source code.

[1275] How to select the optimal deployment platform

[1276] Server: Based on the analysis results, multiple deployment platforms are compared and evaluated from an internal database. Evaluation criteria include cost performance, supported languages, ease of deployment, etc. The optimal deployment platform is selected.

[1277] For example, if the source code is composed of Node.js and React, the server will determine that Netlify or Vercel is the best choice. Also, if MongoDB is used as the backend, it will consider using MongoDB Atlas or Heroku.

[1278] Automated deployment methods

[1279] Server: Automatically deploys to the selected deployment platform. The server uses the deployment platform's API to automatically build and deploy the source code.

[1280] Notification of deployment results

[1281] Server: Once the deployment is complete, the user is notified via email, dashboard alerts, etc.

[1282] Users: They receive a notification and can visit the provided URL to view the deployed web service or web tool.

[1283] Specific operation example

[1284] 1. A user performs practical training on the engineering education platform and pushes the completed web tool to the repository.

[1285] 2. The user visits the system's website, authenticates with GitHub, and selects the repository to deploy to.

[1286] 3. The server retrieves the repository and the generative AI analyzes the source code.

[1287] 4. The server selects the optimal deployment platform and determines it to be Netlify.

[1288] 5. The server uses the Netlify API to automatically deploy the front-end app.

[1289] 6. The user receives a notification that the deployment is complete and can confirm by visiting the provided URL.

[1290] In this way, the present invention is a system that automates the deployment process of web services and web tools, achieving low cost and high performance, thereby providing great convenience to individual developers and users in educational settings.

[1291] The processing flow will be explained below.

[1292] Step 1:

[1293] The user accesses the system's website in a browser and authenticates their GitHub account using OAuth 2.0. The user clicks the "Log in with GitHub" button and the GitHub authentication page is displayed. The user then enters their GitHub username and password to authenticate. If authentication is successful, the system obtains a GitHub authentication token.

[1294] Step 2:

[1295] The server uses the obtained authentication token to access the GitHub API and retrieve the user's repository list. The server sends a request to the GitHub API and receives the user's repository list in JSON format. The server parses this data and formats it into a format that can be displayed to the user.

[1296] Step 3:

[1297] The user selects the project they want to deploy from the displayed list of repositories. The user clicks the "Select repository to deploy to" button and enters the required configuration information (e.g., branch name, environment variables, etc.).

[1298] Step 4:

[1299] The server clones the selected repository. The server clones the repository locally using the Git command (e.g., git clone https: / / github.com / username / repositoryname.git). Once the clone is complete, the entire source code will be stored in the local directory.

[1300] Step 5:

[1301] The server uses generative AI to analyze the cloned source code. The generative AI analyzes the file structure of the source code and identifies the programming languages, libraries, and components (front-end, back-end, database, etc.) used. For example, it extracts dependencies from the package.json file and identifies the technology stack used.

[1302] Step 6:

[1303] The server selects the optimal deployment platform based on the analysis results. It retrieves multiple platform candidates from an internal database and compares them based on evaluation criteria such as cost performance, supported languages, and ease of deployment. Finally, it selects the most suitable platform.

[1304] Step 7:

[1305] The server automatically deploys to the selected deployment platform. For example, if Netlify is selected, it uses the Netlify API to build and deploy the source code, sends the necessary configuration settings (such as environment variables and build commands), and starts the deployment process.

[1306] Step 8:

[1307] The server notifies the user that the deployment is complete by sending an email, posting a notification on the dashboard, etc. The user receives the notification and accesses the provided URL to check the operation of the deployed web service or web tool.

[1308] In this way, the system provides an environment in which users can automatically deploy and easily publish web services and web tools they create at low cost and with high performance.

[1309] Example 1

[1310] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1311] Existing deployment methods for web services and web tools involve a lot of manual work, which is time-consuming and labor-intensive for individual developers and educational institutions. In addition, there is a lack of know-how to select the optimal deployment platform, which can result in high costs and low performance deployments.

[1312] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1313] In this invention, the server includes: means for linking a repository in which a user stores source code with authentication means; means for retrieving the source code from the repository using the authentication means; means for analyzing the retrieved source code using a generative AI model to identify the programming languages, libraries, and components used; means for selecting an optimal deployment platform based on the analysis results; means for automatically deploying to the selected deployment platform; and means for notifying the user of completion of deployment. This automates the deployment process for web services and web tools, enabling low-cost, high-performance deployment.

[1314] "Authentication method" refers to a technology that authenticates users accessing a website using a third-party authentication protocol (e.g., OAuth 2.0).

[1315] A "repository" is a place where source code and other related data are stored and managed in a version control system (e.g., GitHub).

[1316] "Source code" is a set of instructions written in a programming language that defines the behavior of a particular software or web application.

[1317] A "generative AI model" is a system that uses artificial intelligence to automatically generate and analyze text and code, and specific examples include natural language processing models (e.g., GPT-4).

[1318] "Analysis" is the process of examining source code in detail to identify the programming languages, libraries, and components used.

[1319] A "programming language" is an artificial language used to develop software and web applications, and examples include JavaScript, Python, and Java.

[1320] A "library" is a collection of reusable code that is used to easily access specific functionality or services.

[1321] "Components" are major parts of the source code, such as the front end, back end, and database.

[1322] A "deployment platform" is an online service for building and deploying source code into an executable form, and examples include Netlify, Vercel, and Heroku.

[1323] "Deployment" is the process of placing developed software in an executable environment and making it accessible to users.

[1324] "Notification" refers to the means by which users are notified that the deployment is complete, including, for example, email or dashboard alerts.

[1325] The present invention provides a system for efficiently deploying Web services and Web tools for individual developers and in educational settings. Detailed embodiments of the system will be described below.

[1326] System configuration

[1327] The system consists of the following main components:

[1328] 1. Authentication Methods

[1329] 2. How to obtain source code

[1330] 3. Generation AI analysis means

[1331] 4. How to Select the Optimal Deployment Platform

[1332] 5. Automated Deployment Methods

[1333] 6. Deployment result notification method

[1334] Authentication Method

[1335] A user accesses the system's website in a browser and authenticates with their GitHub account. OAuth 2.0 is used as the authentication method, which ensures secure authentication. Specifically, the user clicks the "Log in with GitHub" button and is redirected to the GitHub authentication page. GitHub then returns an authentication token, which the server receives and establishes a user session.

[1336] How to get source code

[1337] The server clones the repository specified by the user through the GitHub API using the obtained authentication token, and the source code is temporarily stored on the server for analysis.

[1338] Generation AI analysis means

[1339] The server inputs the cloned source code into a generative AI model (e.g., GPT-4) and performs analysis using the following prompt: "Analyze the source code below to identify the programming languages, libraries, and components used. Then, suggest the optimal deployment platform. Enter your source code here: <source code>." The generative AI model returns the analysis results to the server.

[1340] How to select the optimal deployment platform

[1341] The server evaluates multiple deployment platforms from an internal database based on the analysis results of the generative AI model. Evaluation criteria include cost-effectiveness, supported programming languages, and ease of deployment. For example, if the source code is composed of Node.js and React, Netlify or Vercel may be deemed suitable. If MongoDB is used as the backend, MongoDB Atlas and other platforms may also be considered.

[1342] Automated deployment methods

[1343] The server automatically deploys to the selected deployment platform, specifically by building and deploying the source code using the selected platform's API (e.g., Netlify API or Vercel API).

[1344] Deployment result notification method

[1345] The server will notify the user once the deployment is complete, either by email or via an alert on the website dashboard.

[1346] After receiving the notification, the user can access the provided URL to check the deployed web service or web tool.

[1347] Specific operation example

[1348] 1. A user develops a web tool on the educational platform and pushes the code to a GitHub repository.

[1349] 2. The user visits the system's website, authenticates with their GitHub account, and selects the repository they want to clone.

[1350] 3. The server clones the repository and analyzes the source code using a generative AI model such as GPT-4.

[1351] 4. Based on the analysis results, the server selects the optimal deployment platform (e.g., Netlify).

[1352] 5. The server performs the deployment using the Netlify API.

[1353] 6. The server notifies the user after the deployment is complete, and the user can access the provided URL to check the deployment results.

[1354] The system automates the deployment process of web services and web tools, ensuring efficient and high-performance deployment.

[1355] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1356] Step 1: Authentication

[1357] Input: The user visits the system's website in a browser and clicks the "Log in with GitHub" button.

[1358] How it works: When a user clicks the "Log in with GitHub" button, the system redirects the user to GitHub's authentication page using the OAuth 2.0 protocol.

[1359] Data processing: After the user enters their authentication information, GitHub returns an authentication token.

[1360] Output: An authentication token is sent to the server and a session is established for the user.

[1361] Step 2: Get the source code

[1362] Input: After the user has authenticated, they select the repository they want to deploy to.

[1363] What it does: The server uses the authentication token to clone the specified GitHub repository via the API.

[1364] Data processing: The source code is obtained through the GitHub API and temporarily stored on the server.

[1365] Output: A temporarily saved source code is prepared.

[1366] Step 3: Analyzing the source code

[1367] Input: Source code stored on the server.

[1368] How it works: The server inputs the source code into a generative AI model (e.g., GPT-4) and performs the analysis.

[1369] Data processing: Use the following prompt: "Analyze the source code below to identify the programming languages, libraries, and components used. Then, suggest the best deployment platform. Enter the source code here: <source code>". The generative AI model performs the analysis.

[1370] Output: The analysis results provide information about the programming languages, libraries, and components used.

[1371] Step 4: Select the optimal deployment platform

[1372] Input: Analysis results from the generative AI model.

[1373] How it works: Based on the analysis results, the server evaluates multiple deployment platforms from an internal database.

[1374] Data processing: Evaluation criteria include cost-effectiveness, supported programming languages, and ease of deployment.

[1375] Output: The optimal deployment platform (e.g. Netlify) is selected.

[1376] Step 5: Automated deployment

[1377] Input: Selected deployment platform information and source code.

[1378] How it works: The server uses the API of the platform of your choice (e.g., Netlify) to build and deploy the source code.

[1379] Data processing: Build and deploy source code through the Netlify API.

[1380] Output: Deployment is complete and a URL for the published web service is generated.

[1381] Step 6: Notification of deployment results

[1382] Input: Deployment completion information and the URL of the published web service.

[1383] Action: The server sends a notification to the user that the deployment is complete.

[1384] Data processing: Generate emails and dashboard alerts as notification methods.

[1385] Output: The user will be notified and can check the deployment result via the provided URL.

[1386] In this way, the processing flow of the entire system is completed, allowing users to achieve efficient and high-performance deployment.

[1387] (Application example 1)

[1388] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1389] In modern logistics centers, software updates and deployments for inventory management systems, delivery management systems, and other systems are complicated and require a large amount of resources and time. This has created a need for automating the software deployment process to make it efficient and fast. Furthermore, selecting the optimal deployment platform for the programming languages ​​and libraries used requires specialized knowledge and is not a straightforward task. Solving these challenges and improving the operational efficiency of logistics centers is essential.

[1390] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1391] In this invention, the server includes means for linking a repository in which a user stores source code with authentication means, means for acquiring the source code from the repository using the authentication means, means for analyzing the acquired source code and identifying the programming language and library used, means for analyzing the source code of software used in an inventory management system or a delivery management system in a logistics center and selecting an optimal deployment platform, and means for automatically deploying the software to the selected deployment platform. This enables the software deployment process to be automated, efficiently, and quickly.

[1392] A "repository" is a data storage for storing and versioning source code and other development files.

[1393] An "authentication method" is a mechanism for verifying a user's identity and securely accessing a repository, such as OAuth 2.0.

[1394] "Source code acquisition means" is a mechanism for acquiring source code from a repository through authentication means.

[1395] The "source code analysis means" is a mechanism for analyzing acquired source code and identifying the programming language and library used.

[1396] A "deployment platform" is an infrastructure service for deploying source code into an execution environment.

[1397] The "optimal deployment platform selection means" is a mechanism for selecting the optimal deployment platform based on the results of source code analysis.

[1398] An "automated deployment mechanism" is a mechanism for automatically building and deploying source code to a selected deployment platform.

[1399] A "logistics center" is a facility that manages inventory and delivery of goods.

[1400] An "inventory management system" is a system that manages the receipt, storage, and shipping of inventory at a logistics center.

[1401] A "delivery management system" is a system that plans and tracks product deliveries at a logistics center.

[1402] The present invention relates to a system for automating the software deployment process for inventory management systems and delivery management systems in a logistics center. This system consists of the following main components:

[1403] System configuration

[1404] Authentication method: Users access the system's website from a browser or device. They authenticate using OAuth 2.0 with their repository management system account. This authentication allows them to access the repository securely.

[1405] Source code acquisition method: The server clones the source code from the specified repository using the authenticated token. The cloned source code is temporarily stored on the server side and becomes the target of analysis.

[1406] Generative AI analysis method: The server analyzes the cloned source code. The generative AI identifies the programming languages, libraries, and components (front-end, back-end, database, etc.) in the source code.

[1407] Optimal deployment platform selection method: The server selects the optimal deployment platform based on the analysis results. Evaluation criteria include cost performance, supported languages, ease of deployment, etc.

[1408] Automatic deployment method: The server automatically builds and deploys the source code using the API of the selected deployment platform.

[1409] Notification of deployment results: Once the deployment is complete, the server will notify the user. Notification methods include email and dashboard alerts. The user can check the deployed system by accessing the provided URL.

[1410] Specific examples of embodiments

[1411] 1. Examples of authentication:

[1412] A user accesses the system's website from a browser and authenticates with an account in the repository management system using OAuth 2.0, which ensures security and grants access to the specified repository.

[1413] 2. Example of source code acquisition:

[1414] After authentication is complete, the server uses the OAuth 2.0 access token to clone the source code from the repository, using Git.

[1415] 3. Specific examples of analysis by generative AI:

[1416] The cloned source code is analyzed by generative AI, which identifies the programming languages ​​(e.g., Node.js, Python), libraries (e.g., React, Django), and other components used.

[1417] 4. Examples of choosing the best deployment platform:

[1418] The server then selects the appropriate deployment platform from its internal database based on the analysis results. For example, if the source code is written in Node.js and React, it may determine that Netlify or another front-end deployment platform is the best fit.

[1419] 5. Examples of automated deployment:

[1420] The server uses the API of the selected deployment platform to automatically build and deploy the source code. If the deployment is successful, a deployment URL is generated and notified to the user.

[1421] 6. Examples of deployment notifications:

[1422] After the deployment is complete, the server notifies the user via email or an alert function on the website dashboard, making it easy for users to check the deployed system.

[1423] Prompt Sentence Examples

[1424] Analyze the following source code, select the optimal deployment platform, and provide instructions for automated deployment.

[1425] Repository URL: https: / / github.com / user / repo

[1426] Language used: Node.js, React

[1427] Potential deployment platforms: Netlify, Heroku

[1428] As described above, the present invention is a system that automates the software deployment process in a logistics center, thereby realizing efficiency and speed, thereby enabling smooth business operations.

[1429] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1430] Step 1:

[1431] Performing authentication

[1432] Input: User's repository management system account information

[1433] How it works: A user accesses the system's website through a browser or device and authenticates using OAuth 2.0. If authentication is successful, an access token is issued.

[1434] Data processing / calculation: Obtaining and saving access tokens

[1435] Output: An access token with permissions to access the repository.

[1436] Step 2:

[1437] Get the source code

[1438] Input: Repository URL and Access Token

[1439] How it works: The server uses the access token to clone source code from the specified repository using a Git client, and the cloned source code is temporarily stored on the server.

[1440] Data processing / calculation: Download and save source code

[1441] Output: Source code stored on the server

[1442] Step 3:

[1443] Source code analysis

[1444] Input: Saved source code

[1445] How it works: The server uses a generative AI model to analyze the source code, which identifies the programming languages, libraries, and components used.

[1446] Data processing / computation: Identifying programming languages, libraries, and components

[1447] Output: Analysis results (list of languages ​​and libraries used)

[1448] Step 4:

[1449] Selecting the optimal deployment platform

[1450] Input: Source code analysis results

[1451] How it works: Based on the analysis results, the server compares and evaluates multiple deployment platforms from an internal database. Evaluation criteria include cost-effectiveness, supported languages, ease of deployment, etc. It then uses a generative AI model to select the most suitable deployment platform.

[1452] Data Processing / Computation: Evaluating and Selecting Deployment Platforms

[1453] Output: Selected deployment platform

[1454] Step 5:

[1455] Implementing automatic deployment

[1456] Input: Selected deployment platform and source code

[1457] How it works: The server builds and deploys the source code using the API of the chosen deployment platform, for example Netlify or other cloud deployment services.

[1458] Data processing / calculation: Building and deploying source code via API

[1459] Output: URL of the deployed system

[1460] Step 6:

[1461] Deployment status notifications

[1462] Input: Deploy result

[1463] How it works: After the deployment is complete, the server notifies the user of the deployment results, providing information to the user via email and dashboard alerts.

[1464] Data processing / calculation: Generation and transmission of notification data

[1465] Output: The deployment URL notified to the user

[1466] Through this procedure, the software deployment process at the logistics center is automated, efficient, and fast.

[1467] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1468] This invention is a system for efficient deployment of web services and web tools for individual developers and in educational settings, and is equipped with a function to recognize user emotions and suggest the optimal deployment platform based on those emotions. This system works in conjunction with a repository that stores source code, analyzes the source code, selects the optimal deployment platform, and automatically deploys the code. In addition, it incorporates an emotion engine that recognizes and analyzes user emotions.

[1469] System configuration

[1470] The system consists of the following main components:

[1471] 1. Authentication Methods

[1472] 2. How to obtain source code

[1473] 3. Generation AI analysis means

[1474] 4. How to Select the Optimal Deployment Platform

[1475] 5. Automated Deployment Methods

[1476] 6. Emotion Engine

[1477] Authentication Method

[1478] User: Accesses the system's website in a browser and authenticates with their GitHub account using OAuth 2.0. The user clicks the "Log in with GitHub" button and the GitHub authentication page is displayed. Next, the user enters their GitHub username and password to authenticate. If authentication is successful, the system obtains a GitHub authentication token.

[1479] How to get source code

[1480] Server: Using the obtained authentication token, the source code is cloned from the repository specified by the user. The cloned source code is temporarily stored on the server side for analysis.

[1481] Generation AI analysis means

[1482] Server: The generated AI analyzes the cloned source code, identifying the programming languages, libraries, and components (front-end, back-end, database, etc.) used within the source code.

[1483] How to select the optimal deployment platform

[1484] Server: Based on the analysis results, multiple deployment platforms are compared and evaluated from an internal database. Evaluation criteria include cost performance, supported languages, ease of deployment, etc. Finally, the most suitable platform is selected.

[1485] For example, if the source code is composed of Node.js and React, the server will determine that Netlify or Vercel is the best choice. Also, if MongoDB is used as the backend, it will consider using MongoDB Atlas or Heroku.

[1486] Automated deployment methods

[1487] Server: Automates deployment to the selected deployment platform. For example, if Netlify is selected, it uses the Netlify API to build and deploy the source code, sends the necessary configuration settings (environment variables, build commands, etc.), and starts the deployment process.

[1488] Emotion Engine

[1489] Server: Using an emotion engine, it recognizes the user's emotions and suggests the optimal deployment platform based on them. It analyzes the user's emotional state from their input and operation history. For example, if the user is feeling stressed, it simplifies the deployment procedure or provides gentle guidance.

[1490] Notification of deployment results

[1491] Server: Once the deployment is complete, the user is notified via email, dashboard notification, etc.

[1492] Users: They receive a notification and can visit the provided URL to view the deployed web service or web tool.

[1493] Specific operation example

[1494] 1. A user performs practical training on the engineering education platform and pushes the completed web tool to the repository.

[1495] 2. The user visits the system's website, authenticates with GitHub, and selects the repository to deploy to.

[1496] 3. The server retrieves the repository and the generative AI analyzes the source code.

[1497] 4. The emotion engine analyzes the user's emotional state and suggests a simplified deployment procedure if it determines that the user is feeling stressed.

[1498] 5. The server selects the optimal deployment platform and determines it to be Netlify.

[1499] 6. The server uses the Netlify API to automatically deploy the front-end app.

[1500] 7. The user receives a notification that the deployment is complete and can confirm by visiting the provided URL.

[1501] In this way, the present invention is a system that automates the deployment process of web services and web tools and provides support that is tailored to the user's emotional state, thereby achieving low-cost, high-performance deployment and reducing the burden on developers.

[1502] The processing flow will be explained below.

[1503] Step 1:

[1504] The user accesses the system's website in a browser and authenticates their GitHub account using OAuth 2.0. The user clicks the "Log in with GitHub" button and the GitHub authentication page is displayed. The user then enters their GitHub username and password to authenticate. If authentication is successful, the system obtains a GitHub authentication token.

[1505] Step 2:

[1506] The server uses the obtained authentication token to access the GitHub API and retrieve the user's repository list. The server sends a request to the GitHub API and receives the user's repository list in JSON format. The server parses this data and formats it into a format that can be displayed to the user.

[1507] Step 3:

[1508] The user selects the project they want to deploy from the displayed list of repositories. The user clicks the "Select repository to deploy to" button and enters the required configuration information (e.g., branch name, environment variables, etc.).

[1509] Step 4:

[1510] The server clones the selected repository. The server clones the repository locally using the Git command (e.g., git clone https: / / github.com / username / repositoryname.git). Once the clone is complete, the entire source code will be stored in the local directory.

[1511] Step 5:

[1512] The server uses generative AI to analyze the cloned source code. The generative AI analyzes the file structure of the source code and identifies the programming languages, libraries, and components (front-end, back-end, database, etc.) used. For example, it extracts dependencies from the package.json file and identifies the technology stack used.

[1513] Step 6:

[1514] The server uses an emotion engine to recognize the user's emotions, analyzes the user's emotional state based on their operation history and input, and determines whether they are feeling stressed or anxious.

[1515] Step 7:

[1516] The server selects the optimal deployment platform based on the analysis results of the Hikari technology stack and the user's emotional state. Multiple platform candidates are retrieved from an internal database and compared based on criteria such as cost performance, supported languages, and ease of deployment. For example, if the user is feeling stressed, a platform that simplifies the process will be prioritized.

[1517] Step 8:

[1518] The server automatically deploys to the selected deployment platform. For example, if Netlify is selected, it uses the Netlify API to build and deploy the source code, sends the necessary configuration settings (such as environment variables and build commands), and starts the deployment process.

[1519] Step 9:

[1520] The server notifies the user that the deployment is complete by sending an email, posting a notification on the dashboard, etc. The user receives the notification and accesses the provided URL to check the operation of the deployed web service or web tool.

[1521] In this way, the system can automatically deploy user-created web services and web tools with low cost and high performance, and also provide support that takes into account the user's emotional state.

[1522] Example 2

[1523] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1524] In modern web development, deployment tasks for individual developers and educational institutions are complex and involve many manual steps, making it difficult to perform efficiently. Furthermore, the selection of the optimal deployment platform does not take into account the emotional state of the user, which often leads to stress and reduced development productivity. Furthermore, there is a lack of a unified selection method for the platform that takes into account evaluation criteria such as cost performance and supported languages.

[1525] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1526] In this invention, the server includes means for linking a repository in which a user stores source code with authentication means, means for retrieving the source code from the repository using the authentication means, means for analyzing the retrieved source code and identifying the programming language and library used, means for selecting an optimal deployment platform based on the programming language and library, means for automatically deploying to the selected deployment platform, and an emotion engine for recognizing the emotional state of the user and suggesting the optimal deployment platform based on the emotional state. This makes the deployment of web services and web tools more efficient and provides support according to the user's emotional state, enabling low-stress, high-performance deployment.

[1527] A "repository" is a digital storage for storing and managing source code and related data.

[1528] "Authentication" refers to the processes and technologies used to verify a user's identity and grant appropriate access privileges.

[1529] A "server" is a computer system that provides data and services in response to requests from clients.

[1530] "Source code" is a set of instructions written in a programming language that defines the behavior of a piece of software or a web application.

[1531] "Generative AI" is a type of artificial intelligence that uses machine learning and deep learning techniques to extract patterns and information from data and provide new data and insights.

[1532] A "deployment platform" refers to the infrastructure and services that deploy software and web applications into an execution environment and make them accessible to users.

[1533] An "emotion engine" refers to technology and algorithms that analyze data such as user input and operation history to recognize the user's emotional state.

[1534] "Automated deployment" is the process of deploying software or web applications to a specified execution environment without manual intervention.

[1535] This invention is a system for efficient deployment of web services and web tools for individual developers and in educational settings, and in particular has the function of recognizing user emotions and proposing the optimal deployment platform based on that. This system works in conjunction with a repository that stores source code, analyzes the source code, selects the optimal deployment platform, and automatically deploys it. In addition, it incorporates an emotion engine that recognizes and analyzes user emotions.

[1536] System configuration

[1537] The system consists of the following main components:

[1538] 1. Authentication Methods

[1539] 2. How to obtain source code

[1540] 3. Generation AI analysis means

[1541] 4. How to Select the Optimal Deployment Platform

[1542] 5. Automated Deployment Methods

[1543] 6. Emotion Engine

[1544] Authentication Method

[1545] The authentication method involves the process where the user accesses the system's website in a browser and authenticates their repository service account (e.g., GitHub) using OAuth 2.0. The user clicks the "Log in with Repository Service" button, and an authentication page is displayed. The user then enters their repository service username and password to authenticate. If authentication is successful, the server obtains an authentication token for the repository service.

[1546] How to get source code

[1547] The source code acquisition means includes a process in which the server clones the source code from the repository specified by the user using the authentication token acquired by the server. The cloned source code is temporarily stored on the server side for analysis.

[1548] Generation AI analysis means

[1549] The generative AI analysis method involves a process in which the server uses generative AI to analyze the cloned source code, which identifies the programming languages, libraries, and components (front-end, back-end, database, etc.) used within the source code.

[1550] How to select the optimal deployment platform

[1551] The optimal deployment platform selection method involves the server comparing and evaluating multiple deployment platforms from an internal database based on the analysis results. Evaluation criteria include cost performance, supported languages, ease of deployment, etc. Finally, the most suitable platform is selected. For example, if the source code is composed of Node.js and React, the server will determine that Netlify or Vercel is optimal. Also, if a database service is used as the backend, the server will also consider the deployment platform appropriate for that service.

[1552] Automated deployment methods

[1553] The automated deployment process involves the server automatically deploying to the selected deployment platform. For example, if Netlify is selected, it uses the Netlify API to build and deploy the source code, sends the necessary configuration settings (such as environment variables and build commands), and starts the deployment process.

[1554] Emotion Engine

[1555] The emotion engine involves a process in which the server recognizes the user's emotional state and suggests the optimal deployment platform based on that. For example, if the user is feeling stressed, the server analyzes the user's emotional state from their input and operation history, and simplifies the deployment procedure or provides gentle guidance.

[1556] Specific examples

[1557] 1. Users practice on the learning platform and push the completed web tool to the repository.

[1558] 2. The user accesses the system website, authenticates with the repository service, and selects the repository to deploy to.

[1559] 3. The server retrieves the repository and the generative AI analyzes the source code.

[1560] 4. The emotion engine analyzes the user's emotional state and suggests a simplified deployment procedure if it determines that the user is feeling stressed.

[1561] 5. The server selects the optimal deployment platform, for example, Netlify.

[1562] 6. The server automatically deploys the web app using the Netlify API.

[1563] 7. The user receives a notification that the deployment is complete and can confirm by accessing the provided URL.

[1564] Prompt Sentence Examples

[1565] "I'd like to deploy a web app built with Node.js and React. Can you briefly explain the automated deployment process? Also, could you suggest the best deployment platform to use?"

[1566] In this way, this invention is a system that automates the deployment process of web services and web tools and provides support that is tailored to the user's emotional state, thereby achieving efficient and stress-free deployment.

[1567] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1568] Step 1:

[1569] A user accesses the system's website in a browser and clicks the "Log in with Repository Service" button. The input is the user's click action, and the output is the display of an authentication page. As a specific operation, the browser redirects the user to the authentication page of the repository service.

[1570] Step 2:

[1571] The user enters a username and password on the repository service authentication page to perform authentication. The input is the user's authentication information, and the output is an authentication token. Specifically, the authentication process ends and the server receives the authentication token from the repository service.

[1572] Step 3:

[1573] The server uses the authentication token it has obtained to clone source code from the repository specified by the user. The input is the authentication token and repository specification, and the output is the cloned source code. Specifically, the server uses an API to obtain the contents of the repository and temporarily stores them.

[1574] Step 4:

[1575] The server analyzes the cloned source code using generative AI. The input is the cloned source code, and the output is the identification of programming languages, libraries, and components. Specifically, the generative AI analyzes the source code and identifies the languages, libraries, and system configurations used.

[1576] Step 5:

[1577] Based on the analysis results, the server compares and evaluates multiple deployment platforms from an internal database. The input is the analysis results, and the output is the selection of the optimal deployment platform. Specifically, the server evaluates the platforms based on cost performance, supported languages, and ease of deployment, and selects the appropriate platform.

[1578] Step 6:

[1579] The emotion engine analyzes the user's emotional state based on their input and operation history. The input is the user's operation history data, and the output is the evaluation result of the emotional state. Specifically, the emotion engine calculates stress levels and other emotional indicators and generates appropriate feedback.

[1580] Step 7:

[1581] Based on the evaluation result of the emotional state, the server may simplify the deployment procedure or guide the user with a gentle message. The input is the evaluation result of the emotional state and the deployment procedure, and the output is a notification to the user and a simplified deployment procedure. Specifically, the server sends an easy-to-understand message to the user.

[1582] Step 8:

[1583] The server automatically deploys to the optimal deployment platform. The input is the selected deployment platform and source code, and the output is a deployment completion notification. Specifically, the server automatically builds and deploys the source code using the API of the selected platform.

[1584] Step 9:

[1585] After the deployment is complete, the server notifies the user of the results. The input is the deployment completion status, and the output is the user notification. Specifically, the server notifies the user of the deployment results via email or a notification on the dashboard.

[1586] Step 10:

[1587] The user accesses the provided URL and checks the deployed web service or web tool. The input is the notified URL, and the output is confirmation of the deployment results. Specifically, the user accesses the URL in a browser and checks the actual operation of the service.

[1588] (Application example 2)

[1589] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1590] In traditional software development, developers must manually select an appropriate deployment platform and execute the deployment procedure, which is time-consuming, labor-intensive, and time-consuming. Furthermore, when developers feel stressed or impatient, the efficiency of the deployment process decreases and errors become more likely. To address these issues, a system is needed that can automatically adjust the deployment procedure and suggest the optimal deployment platform based on the user's emotional state.

[1591] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for authenticating a database in which a user stores source code, means for retrieving the source code from the database using the authentication means, means for analyzing the retrieved source code and identifying the programming language and library used, means for selecting an optimal deployment platform based on the programming language and library, means for automatically deploying on the selected deployment platform, means for analyzing the emotional state of the user, and means for adjusting the deployment procedure based on the emotional state. This automates the deployment process and makes it possible to provide optimal support according to the user's emotional state.

[1592] "Source code" means a description of the program used in the design and development of software.

[1593] A "database" is an information system that stores data in an organized manner so that it can be efficiently managed, searched, and retrieved.

[1594] "Authentication" is the process of verifying that a particular user or system is legitimate.

[1595] A "programming language" is an artificial language used to create software and applications.

[1596] A "library" is a collection of reusable routines or code in programming.

[1597] A "deployment platform" is an environment for running software applications.

[1598] "Analysis" is the process of examining and evaluating data or information in detail.

[1599] "Automated deployment" is the act of placing software or applications into a specified environment without manual intervention.

[1600] "Emotional state" refers to the user's mental and emotional state.

[1601] "Adjustment" is the act of changing settings or procedures to accommodate specific conditions.

[1602] An embodiment of the present invention will be described.

[1603] System Configuration

[1604] The system consists of the following main components:

[1605] 1. Authentication Methods

[1606] 2. How to obtain source code

[1607] 3. Generation AI analysis means

[1608] 4. How to Select the Optimal Deployment Platform

[1609] 5. Automated Deployment Methods

[1610] 6. Emotion Engine

[1611] Authentication Method

[1612] The user accesses the system's website in a browser and authenticates the GitHub account using OAuth 2.0. The user clicks the "Log in with GitHub" button, and the GitHub authentication page is displayed. The user then enters their GitHub username and password to authenticate. If authentication is successful, the server obtains a GitHub authentication token.

[1613] How to get source code

[1614] The server uses the obtained authentication token to clone the source code from the repository specified by the user. The cloned source code is temporarily stored on the server side for analysis.

[1615] Generation AI analysis means

[1616] The server then uses a generative AI model to analyze the cloned source code, which identifies the programming languages, libraries, and components (e.g., front-end, back-end, database, etc.) used within the source code.

[1617] How to select the optimal deployment platform

[1618] Based on the analysis results, the server compares and evaluates multiple deployment platforms from an internal database. Evaluation criteria include cost performance, supported languages, ease of deployment, etc., and ultimately selects the most suitable platform. For example, if the source code is composed of Python and Flask, the server will determine that Heroku is the best choice.

[1619] Automated deployment methods

[1620] The server automatically deploys to the selected deployment platform (for example, if Heroku is selected, it uses the Heroku API to build and deploy the source code, sends the necessary configuration settings (such as environment variables and build commands), and starts the deployment process.

[1621] Emotion Engine

[1622] The server uses an emotion engine to recognize the user's emotional state and propose the optimal deployment procedure based on that. It analyzes the user's emotional state from their input and operation history. For example, if the user is feeling stressed, it simplifies the deployment procedure or provides a gentle message.

[1623] Notification of deployment results

[1624] Once the deployment is complete, the server will notify the user via email, a notification on the dashboard, etc. Users can receive the notification and access the provided URL to check the deployed system and tools.

[1625] Specific operation example

[1626] 1. A user pushes a new robot control program to GitHub and deploys it using the RoboDeploy app.

[1627] 2. The user opens the app, authenticates with GitHub, and selects the repository to deploy.

[1628] 3. The server retrieves the repository and the generative AI analyzes the source code.

[1629] 4. The emotion engine analyzes the user's emotional state and, if it determines that the user is feeling stressed, suggests a simplified deployment procedure.

[1630] 5. The server selects the optimal deployment platform and determines it to be Heroku.

[1631] 6. The server automatically performs the deployment using the Heroku API.

[1632] 7. The user receives a notification that the deployment is complete and visits the provided URL to see the results.

[1633] Prompt Sentence Examples

[1634] An engineer deploying a new robot control program opens the RoboDeploy app. After successfully authenticating with GitHub and specifying the target repository, the app analyzes the code and automatically deploys it to the optimal Heroku repository. Describe the process of the emotion engine detecting the user's stress level and deploying it in a simplified manner.

[1635] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1636] Step 1: Authentication

[1637] The server authenticates a user's GitHub account when the user accesses the system's website in a browser. The user clicks the "Log in with GitHub" button, and the GitHub authentication page is displayed. Next, the user enters their GitHub username and password to authenticate. The input is the username and password, and the output is an authentication token. The server obtains this authentication token.

[1638] Step 2: Get the source code

[1639] The server uses the authentication token to clone the source code from the repository specified by the user. The input is the authentication token and the repository URL, and the output is the source code. The cloned source code is temporarily stored on the server side for analysis. Specifically, the server calls the GitHub API to retrieve the contents of the specified repository.

[1640] Step 3: Analysis by generative AI

[1641] The server analyzes the cloned source code using a generative AI model. The input is the source code, and the output is the programming language, library, and components used. The generative AI model performs data analysis to identify various elements in the source code (language, library, framework, etc.). Specifically, the server inputs the source code into the AI ​​model and obtains the analysis results.

[1642] Step 4: Select the optimal deployment platform

[1643] The server compares and evaluates multiple deployment platforms from an internal database based on the analysis results. The input is the analysis results, and the output is the optimal deployment platform. Evaluation criteria include cost performance, supported languages, ease of deployment, etc. Specifically, the server calculates an evaluation score for each platform based on the analysis results and selects the most suitable platform.

[1644] Step 5: Automatic deployment

[1645] The server automatically performs deployment to the selected deployment platform. The input is the selected deployment platform information and source code, and the output is the deployment result. For example, if Heroku is selected, the server will use the Heroku API to build and deploy the source code. Specifically, the server will call the API to instruct the build and deployment.

[1646] Step 6: Analyze emotional state

[1647] The server uses an emotion engine to analyze the user's emotional state. The input is the user's operation history and input data, and the output is the analyzed emotional state. Specifically, the server inputs operation logs and session data into the emotion engine to recognize the user's stress level and emotional state.

[1648] Step 7: Adjust the deployment procedure

[1649] The server adjusts the deployment procedure based on the analyzed emotional state. The input is the emotional state, and the output is the adjusted deployment procedure. For example, if the user is feeling stressed, the server simplifies the deployment procedure or guides the user with a gentle message. Specifically, the server generates a simplified procedure and presents it to the user.

[1650] Step 8: Notification

[1651] The server notifies the user when the deployment is complete. The input is the deployment completion status, and the output is a notification message. Notification methods include email and notifications on the dashboard. As a specific operation, the server generates a notification message and sends it to the user via the specified method.

[1652] The above is a specific description of each processing step in the deployment process.

[1653] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1654] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1655] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1656] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1657] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1658] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1659] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1660] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1661] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1662] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1663] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1664] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1665] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1666] 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.

[1667] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1668] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1669] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1670] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1671] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1672] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1673] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1674] The following is further disclosed regarding the above embodiment.

[1675] (Claim 1)

[1676] a means for associating a repository where a user stores source code with an authentication means;

[1677] means for obtaining source code from a repository using said authentication means;

[1678] A means for analyzing the acquired source code and identifying the programming languages ​​and libraries used;

[1679] A means for selecting an optimal deployment platform based on the programming language and library;

[1680] A system including a means for automated deployment to a selected deployment platform.

[1681] (Claim 2)

[1682] 2. The system according to claim 1, wherein the means for selecting the optimal deployment platform evaluates information from a plurality of platforms and selects the deployment platform with the highest cost performance.

[1683] (Claim 3)

[1684] 2. The system according to claim 1, wherein the means for linking the repository with the authentication means performs user authentication using OAuth 2.0.

[1685] "Example 1"

[1686] (Claim 1)

[1687] a means for associating a repository where a user stores source code with an authentication means;

[1688] means for obtaining source code from a repository using said authentication means;

[1689] a means for analyzing the acquired source code using a generative AI model to identify the programming languages, libraries, and components used;

[1690] A means for selecting an optimal deployment platform based on the analysis results;

[1691] a means for automated deployment to the selected deployment platform;

[1692] A system that includes a means to notify the consumer when the deployment is complete.

[1693] (Claim 2)

[1694] 2. The system according to claim 1, wherein the means for selecting the optimal deployment platform evaluates information from a plurality of platforms and selects the deployment platform with the highest cost performance.

[1695] (Claim 3)

[1696] 2. The system according to claim 1, wherein the means for linking the repository with the authentication means performs user authentication using OAuth 2.0.

[1697] "Application Example 1"

[1698] (Claim 1)

[1699] a means for associating a repository where a user stores source code with an authentication means;

[1700] means for obtaining source code from a repository using said authentication means;

[1701] A means for analyzing the acquired source code and identifying the programming languages ​​and libraries used;

[1702] A means for selecting an optimal deployment platform based on the programming language and library;

[1703] A method for analyzing the source code of software used in inventory management systems and delivery management systems at logistics centers and selecting the optimal deployment platform;

[1704] A system including a means for automated deployment to a selected deployment platform.

[1705] (Claim 2)

[1706] 2. The system according to claim 1, wherein the means for selecting the optimal deployment platform evaluates information from a plurality of platforms and selects the deployment platform with the highest cost performance.

[1707] (Claim 3)

[1708] 2. The system according to claim 1, wherein the means for linking the repository with the authentication means performs user authentication using OAuth 2.0.

[1709] "Example 2: Combining Emotion Engines"

[1710] (Claim 1)

[1711] a means for associating a repository where a user stores source code with an authentication means;

[1712] means for obtaining source code from a repository using said authentication means;

[1713] A means for analyzing the acquired source code and identifying the programming languages ​​and libraries used;

[1714] A means for selecting an optimal deployment platform based on the programming language and library;

[1715] a means for automated deployment to the selected deployment platform;

[1716] The system includes an emotion engine that recognizes the user's emotional state and suggests the optimal deployment platform based on the emotional state.

[1717] (Claim 2)

[1718] 2. The system according to claim 1, wherein the means for selecting the optimal deployment platform evaluates information from a plurality of platforms and selects the deployment platform with the highest cost performance.

[1719] (Claim 3)

[1720] 2. The system according to claim 1, wherein the means for linking the repository with the authentication means performs user authentication using an authentication protocol.

[1721] "Application example 2 when combining emotion engines"

[1722] (Claim 1)

[1723] a means for a user to authenticate a database storing source code;

[1724] means for acquiring source code from a database using the authentication means;

[1725] A means for analyzing the acquired source code and identifying the programming languages ​​and libraries used;

[1726] means for selecting an optimal deployment platform based on said programming language and library;

[1727] a means for automated deployment to a selected deployment platform;

[1728] means for analyzing the emotional state of a user;

[1729] means for adjusting a deployment procedure based on said emotional state;

[1730] Including system.

[1731] (Claim 2)

[1732] 2. The system of claim 1, wherein the means for selecting the optimal deployment platform evaluates information from multiple platforms and selects the most cost-effective deployment platform.

[1733] (Claim 3)

[1734] 2. The system according to claim 1, wherein the means for authenticating the database performs user authentication using OAuth 2.0. [Explanation of symbols]

[1735] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for associating a repository where a user stores source code with an authentication means; means for obtaining source code from a repository using said authentication means; A means for analyzing the acquired source code and identifying the programming languages ​​and libraries used; A means for selecting an optimal deployment platform based on the programming language and library; A system including a means for automated deployment to a selected deployment platform.

2. 2. The system according to claim 1, wherein the means for selecting the optimal deployment platform evaluates information from a plurality of platforms and selects the deployment platform with the highest cost performance.

3. 2. The system according to claim 1, wherein the means for linking the repository with the authentication means performs user authentication using OAuth 2.0.

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