system

Abstract

We provide the system. [Solution] A device for acquiring user interests and goals, A device that transmits user history and personal information, A device that analyzes received information and recommends the optimal payment technology and resources, A device that collects relevant information and generates specific procedures, A device that presents the generated procedure to the user, A device that tracks the user's progress and provides evaluations, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] There are individuals and small business operators who want to challenge new projects but find it difficult to continue because information collection and execution are time-consuming and laborious, and it is difficult to select appropriate tools and manuals. In such a situation, there is a need for support to enable users to start and continue projects efficiently and effectively.

Means for Solving the Problems

[0005] To address this challenge, the present invention provides a system that includes means for acquiring user interests and goals, transmits user history and personal data to a server, analyzes the received data, and recommends optimal tools and resources. It also includes means for collecting relevant information and generating specific execution procedures, and presents these procedures to the user to support the start of their project. Furthermore, it includes means for tracking user progress and providing feedback, thereby enabling continuous support in project execution.

[0006] A "user" is an individual or small business that uses the system to try out a new project.

[0007] "Interest" refers to the fields or areas that users are interested in and want to engage with as learning opportunities or projects.

[0008] "Goals" refer to the specific results or outcomes that users are trying to achieve through a project.

[0009] "History" refers to information about projects and activities that the user has participated in in the past.

[0010] "Personal data" refers to information about an individual user's attributes, preferences, skill set, etc.

[0011] "Tools" refer to the software, platforms, and equipment available to users to carry out a project.

[0012] "Resources" refer to the information, materials, and support that users need for a project.

[0013] "Recommendation" refers to the process of suggesting the most suitable tools and resources based on the user's specific needs.

[0014] "Procedure" refers to the specific action steps necessary to start and continue a project.

[0015] "Progress" refers to the state indicating the extent to which the user has completed the work in the project.

[0016] "Feedback" refers to the evaluation, comments, and instructions regarding the next steps provided to the user.

Brief Description of the Drawings

[0017] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12]It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] First, the language used in the following description will be explained.

[0020] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0021] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0023] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0030] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0037] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0038] One embodiment of the present invention begins with the user inputting their interests and goals for a new project. In this process, the terminal collects the user's interests and goals, as well as their past project history and personal data. For example, consider the case where the user is interested in "opening an online store."

[0039] The device sends the collected information to the server. The server uses machine learning algorithms to analyze this information and recommend the most suitable tools and resources for the user's project. Specifically, if the user wants to open an online store, the server will list available e-commerce platforms and related tools (e.g., product management software).

[0040] Next, the server automatically collects information related to the user's goals from the external network and generates specific execution steps. These steps are designed to guide the project step by step and are displayed on the terminal. For example, it provides detailed instructions for the steps from domain acquisition to site design and product registration.

[0041] After the terminal presents the user with instructions, the user can proceed with the project by following this guide. Progress is updated in real time via the terminal, and the server analyzes this progress data to generate feedback. By providing users with evaluations based on their progress and advice for the next steps, they can effectively complete the project.

[0042] In this way, this system significantly reduces the user's workload and can provide comprehensive support from the start to the completion of a project.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] Users input the project's purpose and goals using their devices. Specifically, users enter the specific results they want to achieve through the project and their areas of interest into a form.

[0046] Step 2:

[0047] The terminal sends user input data to the server. This data includes the user's past project history and personal data.

[0048] Step 3:

[0049] The server analyzes the user data it receives. This process uses machine learning algorithms to generate a list of optimal tools and resources tailored to the user's needs.

[0050] Step 4:

[0051] The server collects relevant information from the external network. This information includes the latest technical information and market trends related to the user's goals.

[0052] Step 5:

[0053] Based on the information collected by the server, the specific execution procedures for the project are designed. These procedures comprise the concrete steps necessary for the user to advance their project.

[0054] Step 6:

[0055] The terminal presents the user with the generated execution steps. The user can then proceed with the project while reviewing these steps.

[0056] Step 7:

[0057] Users update project progress using their devices. They input feedback on completed steps and register progress in real time.

[0058] Step 8:

[0059] The server analyzes progress data and generates feedback for the user. This feedback includes progress-based evaluations and advice for the next steps.

[0060] Step 9:

[0061] The terminal displays feedback from the server to the user. Based on this feedback, the user can decide on the next steps in carrying out the project.

[0062] (Example 1)

[0063] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0064] In modern information processing systems, a challenge exists in that it is difficult for users to quickly find appropriate resources and procedures when starting a new project. In particular, there is a need to efficiently collect relevant information from vast amounts of data and provide feedback tailored to the individual needs of the user.

[0065] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0066] In this invention, the server includes a device for acquiring user requests and objectives, a device for analyzing the received data and recommending the optimal devices and resources, and a device for collecting relevant information from external resources and generating specific operating procedures. This allows users to obtain specific execution procedures for their projects, thereby improving the efficiency of project progress.

[0067] A "user" is an individual or group that uses an information processing system to input their requests and goals and to receive relevant information and resources.

[0068] A "requirement" is a specific need or desire that a user wants to achieve or that is necessary for the progress of a project.

[0069] A "goal" refers to the specific results or direction that the user wants to achieve, and represents the ultimate objective of the project.

[0070] A "device" is a hardware or software component designed to perform a specific function within an information processing system.

[0071] "Analysis" is the process of analyzing obtained data through machine learning algorithms to extract useful patterns and insights.

[0072] "Recommendation" means suggesting the most appropriate resources or equipment based on the user's needs and history.

[0073] "Resources" is a comprehensive concept encompassing the tools, data, and human resources necessary to advance a user's project.

[0074] "External resources" refer to data sources and information provision services that exist outside of the information processing system and are used to collect project-related information.

[0075] "Operating procedures" are guidelines that show, in chronological order, the specific steps and methods that users need to effectively manage a project.

[0076] This invention relates to a comprehensive information processing system for improving the efficiency of users in starting new projects. Users input their interests and goals through a terminal. For example, if a user is considering opening an online store, they input this into the terminal. The terminal sends the collected information to a server. The server analyzes the data using machine learning algorithms with programming languages ​​such as Python and R. Specifically, it uses libraries such as scikit-learn and TENSORFLOW® to recommend the most suitable resources and tools for the user. The server also automatically collects relevant information from the internet using external APIs and web crawling technologies and generates specific operating procedures. These procedures are displayed on the user's terminal to assist in project implementation.

[0077] The server tracks the user's project progress in real time via the terminal and provides feedback based on that progress. This allows the user to effectively advance their project based on the feedback. For example, if the user enters the prompt, "I'm thinking of opening an online store. Please tell me the best platform and procedure," the server will provide a list of the best e-commerce platforms and related tools, and present specific steps. In this way, the present invention fully supports the user's project execution.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The user enters their interests and goals for a new project into the terminal. For example, if they are considering "opening an online store," they would write that down. This input includes prompts and keywords. The terminal receives this input, combines it with the user's past project history and personal data, and prepares to send it to the server.

[0081] Step 2:

[0082] The terminal sends user-obtained interests and goals, along with related historical data, to the server. The input data includes specific requirements and goals related to the project, and the output becomes integrated data sent to the server. This integrated data is then prepared for subsequent analysis.

[0083] Step 3:

[0084] The server launches Python or R programs to analyze the received data and applies machine learning algorithms. The input is an integrated dataset, and the output generates a list of the most suitable resources and tools for the user. Specifically, it uses scikit-learn and TensorFlow to compare current data with historical data and recommend the best options.

[0085] Step 4:

[0086] The server uses external APIs and web crawling technologies to collect up-to-date information related to the user's goals. Input includes keywords and requests related to the user's goals, and output is a list of specific execution steps. This generates the specific steps required for the user's project.

[0087] Step 5:

[0088] The project procedures generated by the server are sent to the terminal and visually presented to the user. The terminal displays the procedures in a user-friendly format, allowing the user to follow the steps. The input is the generated procedure data, and the output is the presentation to the user.

[0089] Step 6:

[0090] The user follows the provided instructions to advance the project. Progress is recorded on the device and sent to the server in real time. As a result, the user's progress data is sent to the server as input data.

[0091] Step 7:

[0092] The server analyzes progress data and generates feedback and advice for the next steps for the user. It utilizes machine learning models to provide efficient support. The output includes specific feedback for the user, effectively supporting project management.

[0093] (Application Example 1)

[0094] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0095] In recent years, many individuals and small businesses have been looking to open online stores, but they lack support in selecting the optimal electronic payment technology and implementing it effectively. As a result, incorrect selection or configuration of electronic payment systems can significantly impact business operations and security. This invention aims to solve this problem by providing a system that supports users in selecting appropriate payment technology and smoothly implementing and operating it.

[0096] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0097] In this invention, the server includes a device for acquiring the user's interests and goals, a device for transmitting the user's history and personal information, and a device for analyzing the received information and recommending the most suitable payment technology and resources. This enables the user to efficiently and effectively select and implement a payment system.

[0098] A "user" refers to an individual or business that uses the system and is interested in opening or operating an online store.

[0099] "Interests and goals" refer to what the user wants to achieve or the project themes they are interested in.

[0100] "Device" refers to a hardware or software configuration for acquiring, transmitting, and analyzing user data.

[0101] "History and personal information" refers to data that shows a user's past activities and profile, including information related to the execution of projects.

[0102] "Analyzing information" refers to the process of applying data analysis and machine learning techniques to identify the optimal payment technologies and resources based on collected data.

[0103] "Payment technologies and resources" refers to the technical means and available systems for conducting online payments.

[0104] "Information network" refers to the network infrastructure and communication technologies necessary to acquire information from external sources.

[0105] A "program interface" refers to protocols and APIs used to interact with external information networks and acquire data.

[0106] "Data collection technology" refers to technologies that automatically acquire necessary information from external sources using web crawling or APIs.

[0107] To implement this invention, it is necessary for a user's device to work in conjunction with a server in the cloud. First, the user uses their device to input their interests and goals regarding the opening of an online store. At the same time, the device also collects the user's past history and personal information.

[0108] Upon receiving this data, the server runs machine learning algorithms to recommend the optimal payment technology and resources. Specifically, the server analyzes the data using libraries such as TensorFlow and scikit-learn. The recommended payment technology includes specific steps to guide users through its implementation process. This allows users to proceed with the implementation efficiently.

[0109] In this process, the server automatically retrieves relevant information from external information networks. This process utilizes APIs and web crawling, employing programmatic interfaces and data collection technologies. This allows the server to reflect the latest information in real time and provide users with optimal advice.

[0110] Furthermore, user progress is tracked in real time by the server, and feedback is provided as needed. This feedback includes suggestions for improvement to the ongoing project and advice for the next steps. For example, it helps resolve issues by providing specific steps to fix error messages pointed out by the user.

[0111] A concrete example of this system would be a user who wants to "open a small online accessories shop." In this case, the server would suggest appropriate payment technologies and clearly outline the implementation procedures for each. It would also provide advice on security and operational optimization after implementation.

[0112] An example of a prompt for a generated AI model would be, "I'm thinking of opening an online accessory shop. Could you recommend an electronic payment system and explain the setup procedure?"

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The device receives the user's interests and goals. At the same time, it collects past project history and personal information as input. This data is organized in text format and prepared for transmission to the server.

[0116] Step 2:

[0117] The server receives user data sent from the terminal. Based on this input data, it first analyzes the user's interests and goals. Machine learning algorithms are used for the analysis, such as TensorFlow or scikit-learn, to process the data and extract features. This allows the server to search the database for the most suitable payment technology for the user.

[0118] Step 3:

[0119] The server recommends the most suitable payment technology and resources based on the analysis results. In this recommendation process, the analyzed data is used as input, and the program generates a recommendation list. This list includes technical details and specific implementation instructions, which are then provided to the user.

[0120] Step 4:

[0121] The server retrieves relevant information from external information networks. This step involves a process of collecting the latest information using APIs and web crawling technologies. The retrieved data is then used to create suggestions for the user.

[0122] Step 5:

[0123] The terminal displays to the user a list and instructions regarding optimized payment technologies received from the server. The user then proceeds with setting up the online store based on the provided instructions.

[0124] Step 6:

[0125] The server monitors the user's progress in real time. In this step, the server analyzes the progress based on the user's operation history and input data. Progress data is generated as feedback and provided to the user as guidance for the next step.

[0126] Step 7:

[0127] Users make necessary adjustments and improvements based on feedback from the server. Each feedback includes specific advice and problem-solving methods to support users in efficiently advancing the project.

[0128] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0129] The embodiment of the present invention begins with an initial stage where the user inputs project interests and goals. The terminal provides the user with an interface for inputting project goals, and the user can input their interests and preferences in detail. The terminal also collects the user's past project history and personal data.

[0130] Next, the device sends the collected information to the server. The server uses machine learning algorithms to analyze this information and recommend the most suitable tools and resources for the user's project. For example, if the project is "creating an online course," the server may recommend video editing software and an LMS platform.

[0131] Next, the server gathers up-to-date information related to the project's objectives from the external network. This involves using APIs and web crawling technologies to collect the knowledge and technical documents the user needs. Based on the information collected by the server, it designs the specific execution steps for the project. This includes installation procedures for the software to be used and preparation steps for the necessary materials.

[0132] The generated steps are presented to the user via the device, and the user follows this guide to complete the project. The emotion engine analyzes the user's input, progress, and even emotions, and generates feedback and motivational messages tailored to the project's progress. For example, if the user is feeling stressed about the progress, the emotion engine provides advice and encouraging messages to help them relax.

[0133] Thus, this system has the functionality to comprehensively support the project process from start to finish, while also taking into account the user's emotional state. As a result, users can significantly reduce their workload and achieve their goals more efficiently.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] The user uses a terminal to input the project's purpose and goals. Specifically, the user provides the system with the project's theme and desired outcomes through a text input interface.

[0137] Step 2:

[0138] The terminal sends the user's input data to the server. At the same time, the terminal also sends past project history and personal data.

[0139] Step 3:

[0140] The server analyzes the information it receives. It then runs machine learning algorithms to recommend the best tools and resources for the user's project. This process identifies the best software and platform to meet the user's needs.

[0141] Step 4:

[0142] The server automatically collects relevant information from the external network. Using APIs and web crawling technologies, it obtains the technical information and latest trend information necessary for the project.

[0143] Step 5:

[0144] Based on the information collected by the server, specific execution procedures are designed. This includes clear guidelines for project progress, such as "step-by-step instructions" and "critical checkpoints."

[0145] Step 6:

[0146] The terminal presents the user with the execution steps received from the server. The user can then proceed with the project by referring to these steps.

[0147] Step 7:

[0148] The device activates an emotion engine to recognize the user's emotions. It analyzes the user's text input and progress to generate data about the user's psychological state.

[0149] Step 8:

[0150] Based on the results of the emotion engine, the server creates feedback and motivational messages tailored to the user's progress. If the user is feeling stressed, this includes suggestions for relaxation.

[0151] Step 9:

[0152] The device displays generated feedback and messages to the user. Based on this, the user can maintain motivation for the project and move on to the next step.

[0153] (Example 2)

[0154] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0155] Traditionally, when users individually planned and implemented projects, it not only required a great deal of time and effort, but also presented problems such as the inability to effectively utilize appropriate information and resources. Furthermore, maintaining motivation and managing emotions during project progress was difficult, potentially leading to a decrease in overall efficiency.

[0156] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0157] In this invention, the server includes means for acquiring the user's interests and goals, means for transmitting the user's history and personal data, means for analyzing the received data and recommending the most suitable tools and resources, means for collecting external information and designing specific execution procedures, and means for monitoring the user's progress and providing feedback that takes into account their emotional state. This allows the user to be efficiently supported from the start to the completion of a project, effectively consolidating necessary resources and knowledge while also receiving emotional support.

[0158] A "user" refers to an individual or organization that uses the system to input information related to project planning and implementation.

[0159] "Interests and goals" refers to information that indicates the content of the project the user wants to achieve and the desired results.

[0160] "History and personal data" refers to information related to a user's past project activities and data concerning the user's unique characteristics.

[0161] "Received data" refers to a collection of information, including interests, goals, history, and personal data, that is sent by the user.

[0162] "Optimal tools and resources" refers to the tools and resources recommended to support the user in carrying out their project.

[0163] "Analysis" refers to the process of examining received data in detail and determining the tools and resources best suited to the user's needs.

[0164] "Recommended methods" refer to the methods and techniques for identifying and suggesting the most suitable tools and resources for the user.

[0165] "External information" refers to data obtained from external sources that contains the latest knowledge and technical information related to the user's project.

[0166] "Execution procedure" refers to the specific steps and methods that users should follow when carrying out a project.

[0167] "Monitoring" refers to continuously observing the progress and work being done on a user's project.

[0168] "Feedback that takes emotional state into account" refers to information that provides encouragement and advice regarding the progress of a project, taking into account the user's emotional state.

[0169] This invention is a system that supports users in achieving their project goals through the cooperation of the user, terminal, and server.

[0170] The user first uses the terminal's interface to input their interests and goals regarding their project. In addition to the entered interests and goals, the terminal also collects the user's past project history and personal data. This information is sent to the server via HTTP or WebSocket, using a secure communication protocol that includes error checking.

[0171] The server performs analysis using machine learning algorithms based on the received data. Python's scikit-learn or similar data processing libraries can be used for this analysis. This analysis identifies the most suitable tools and resources for the user's project.

[0172] Subsequently, the server utilizes APIs and web crawling technologies to collect relevant information from external networks. This process uses technologies such as BeautifulSoup and OpenAI® API to retrieve data. Based on this information, the server designs specific execution steps, which are then generated in Markdown or other document formats.

[0173] The user is presented with the designed execution procedure via a terminal. As the user progresses through the project according to the procedure, an emotion engine installed on the server monitors the user's emotional state and project progress, generating and providing feedback and encouraging messages as needed. This utilizes natural language processing with advanced text analysis techniques.

[0174] As a concrete example, let's consider a scenario where the user's goal is to "create an online educational course." In this case, the server would recommend candidate video editing software and learning management systems (LMS), collect information on relevant educational methods and materials, and present the user with the necessary steps.

[0175] Examples of prompt statements include the following:

[0176] "Please enter the goals for your new project and tell us the steps needed to achieve them. The following information is available…"

[0177] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0178] Step 1:

[0179] The user uses the terminal's interface to input their project interests and goals. They can also enter specific keywords and expected outcomes. The user's project interests and goals are sent to the terminal as input data. The terminal receives this data and displays a confirmation message to the user to indicate completion of the input.

[0180] Step 2:

[0181] The device collects user interests and goals, as well as the user's past project history and personal data. This data is organized based on the user's ID and sent to the server. Specifically, the device displays a message indicating that it is sending data to the server and sends the data using HTTP or WebSocket.

[0182] Step 3:

[0183] The server receives data sent from the terminal and analyzes it using machine learning algorithms. The analysis applies clustering and recommendation algorithms to identify the most suitable tools and resources for the user's project. As output, a list of appropriate tools and resources is generated and sent to the terminal.

[0184] Step 4:

[0185] The server collects relevant information from external networks using APIs and web crawling technologies. Keywords related to the user's interests and goals are used as input. The server uses BeautifulSoup and OpenAI APIs to collect the latest technical information and materials, analyzes them, and generates foundational data for execution procedures.

[0186] Step 5:

[0187] Based on the collected information, the server designs specific execution steps to help the user achieve their project goals. The procedure manual is created using a document format such as Markdown. The designed procedure manual is then sent to the terminal as output. The server sends a confirmation message to the terminal stating, "Execution steps have been generated."

[0188] Step 6:

[0189] The terminal presents the user with the execution steps sent from the server. The user proceeds with the project by following these steps. The terminal displays each step of the procedure and prompts the user to take the next action accordingly. Specifically, it displays prompts such as, "Are you ready to proceed to the next step?"

[0190] Step 7:

[0191] The server monitors the user's emotional state and progress throughout the project using an emotion engine. Inputs include user progress data and entered emotional states. The server analyzes this data and, if necessary, generates encouraging or feedback messages, which are then presented to the user via the terminal. For example, it might send a message like, "Great job! Progress is on track."

[0192] (Application Example 2)

[0193] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0194] Currently, in project execution, users spend a lot of time selecting necessary information and tools, making it difficult to manage progress and maintain motivation. This is especially true for personal projects within the home, where a lack of adequate support is particularly noticeable, often leading to a decline in individual motivation.

[0195] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0196] In this invention, the server includes means for acquiring user requests, means for transmitting user history information, means for analyzing received data and suggesting optimal equipment and resources, and means for evaluating the user's emotional state during project progress and generating appropriate messages. This enables the user to efficiently manage the project process from start to finish and to maintain high motivation throughout the process.

[0197] "Means for obtaining user requirements" refers to an interface that allows users to input their goals and desired outcomes for a project via digital devices and to accurately receive that information.

[0198] "Means for transmitting user history information" refers to a function for collecting data related to a user's past activities and projects and sending it to a server for analysis.

[0199] "A means of analyzing received data and proposing the optimal equipment and resources" refers to a process that uses algorithms based on collected information to present the tools and resources most suitable for the user's project execution.

[0200] "Means for evaluating the user's emotional state during project progress and generating appropriate messages" refers to a mechanism for analyzing the user's mental state according to the project's progress and providing encouragement or advice as needed.

[0201] The system for implementing this invention mainly consists of three components: a server, a terminal, and a user.

[0202] First, users provide information through an interface using their device to input project goals and interests. This interface is designed to allow users to easily input data. Similarly, user history information is also obtained from the device and sent to the server.

[0203] Next, the server analyzes the received data. This analysis utilizes a machine learning framework using Python (e.g., TensorFlow) to identify and suggest the most suitable equipment and resources for the user's project. Specifically, if a user wants to start a home garden, it will show the necessary tools, materials, and procedures. In this process, APIs and web crawling technologies are used to collect relevant information from external networks.

[0204] Furthermore, the server tracks user progress, assesses user emotional state, and generates messages to maintain project motivation. This process utilizes natural language processing libraries (e.g., NLTK). Specifically, if a user is feeling stressed about their progress, the server automatically provides relaxing advice and encouraging messages.

[0205] Ultimately, the generated procedures and messages are presented to the user through the device. This allows the user to carry out the project efficiently and effectively.

[0206] Examples of prompts include, "Please suggest the steps and tools needed to start a home garden project. Also, please create encouraging messages for the user as they progress." Based on such prompts, the AI ​​model generates appropriate output to meet the user's needs.

[0207] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0208] Step 1:

[0209] Users input project goals and interests using a device. This input is stored on the device as specific project goals and aspirations. This information specifically reflects the user's intentions and expected project outcomes.

[0210] Step 2:

[0211] The terminal collects historical information about past projects along with the user's input data. This includes information about the projects the user has worked on previously and the resources they used. This historical information is used to extract the user's project habits and preferences. The collected data is sent to the server.

[0212] Step 3:

[0213] The server analyzes the received user input data and historical information. This analysis uses a Python machine learning framework (e.g., TensorFlow) and algorithms to identify the most suitable equipment and resources for the user. The input to this analysis is the characteristics and historical data of the project the user is requesting, and the output is a list of suitable tool and resource candidates.

[0214] Step 4:

[0215] The server uses APIs and web crawling technologies to retrieve data from external networks to collect necessary information. This externally retrieved data includes the latest technical information and project-related documents. The input to this process is project-related keywords, and the output is relevant, detailed information.

[0216] Step 5:

[0217] The server designs specific execution procedures based on the analysis results and collected information. This includes installation instructions for the tools to be used and step-by-step guides for proceeding with the project. The input for this step is the analyzed information and acquired external data, and the output is the execution procedure for the user.

[0218] Step 6:

[0219] The terminal presents the user with the generated execution procedure. This procedure is a detailed guide to support the project from start to finish. The user can then complete the project by following this procedure.

[0220] Step 7:

[0221] The server analyzes user progress data and emotional state during project execution and generates appropriate messages. It uses a natural language processing library (e.g., NLTK) to provide encouragement and suggestions tailored to the user's state. The input for this analysis is progress data and user psychological feedback, and the output is a personalized message of encouragement.

[0222] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0223] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0224] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0225] [Second Embodiment]

[0226] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0227] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0228] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0229] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0230] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0231] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0232] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0233] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0234] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0235] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0236] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0237] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0238] One embodiment of the present invention begins with the user inputting their interests and goals for a new project. In this process, the terminal collects the user's interests and goals, as well as their past project history and personal data. For example, consider the case where the user is interested in "opening an online store."

[0239] The device sends the collected information to the server. The server uses machine learning algorithms to analyze this information and recommend the most suitable tools and resources for the user's project. Specifically, if the user wants to open an online store, the server will list available e-commerce platforms and related tools (e.g., product management software).

[0240] Next, the server automatically collects information related to the user's goals from the external network and generates specific execution steps. These steps are designed to guide the project step by step and are displayed on the terminal. For example, it provides detailed instructions for the steps from domain acquisition to site design and product registration.

[0241] After the terminal presents the user with instructions, the user can proceed with the project by following this guide. Progress is updated in real time via the terminal, and the server analyzes this progress data to generate feedback. By providing users with evaluations based on their progress and advice for the next steps, they can effectively complete the project.

[0242] In this way, this system significantly reduces the user's workload and can provide comprehensive support from the start to the completion of a project.

[0243] The following describes the processing flow.

[0244] Step 1:

[0245] Users input the project's purpose and goals using their devices. Specifically, users enter the specific results they want to achieve through the project and their areas of interest into a form.

[0246] Step 2:

[0247] The terminal sends user input data to the server. This data includes the user's past project history and personal data.

[0248] Step 3:

[0249] The server analyzes the user data it receives. This process uses machine learning algorithms to generate a list of optimal tools and resources tailored to the user's needs.

[0250] Step 4:

[0251] The server collects relevant information from the external network. This information includes the latest technical information and market trends related to the user's goals.

[0252] Step 5:

[0253] Based on the information collected by the server, the specific execution procedures for the project are designed. These procedures comprise the concrete steps necessary for the user to advance their project.

[0254] Step 6:

[0255] The terminal presents the user with the generated execution steps. The user can then proceed with the project while reviewing these steps.

[0256] Step 7:

[0257] Users update project progress using their devices. They input feedback on completed steps and register progress in real time.

[0258] Step 8:

[0259] The server analyzes progress data and generates feedback for the user. This feedback includes progress-based evaluations and advice for the next steps.

[0260] Step 9:

[0261] The terminal displays feedback from the server to the user. Based on this feedback, the user can decide on the next steps in carrying out the project.

[0262] (Example 1)

[0263] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0264] In modern information processing systems, a challenge exists in that it is difficult for users to quickly find appropriate resources and procedures when starting a new project. In particular, there is a need to efficiently collect relevant information from vast amounts of data and provide feedback tailored to the individual needs of the user.

[0265] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0266] In this invention, the server includes a device for acquiring user requests and objectives, a device for analyzing the received data and recommending the optimal devices and resources, and a device for collecting relevant information from external resources and generating specific operating procedures. This allows users to obtain specific execution procedures for their projects, thereby improving the efficiency of project progress.

[0267] A "user" is an individual or group that uses an information processing system to input their requests and goals and to receive relevant information and resources.

[0268] A "requirement" is a specific need or desire that a user wants to achieve or that is necessary for the progress of a project.

[0269] A "goal" refers to the specific results or direction that the user wants to achieve, and represents the ultimate objective of the project.

[0270] A "device" is a hardware or software component designed to perform a specific function within an information processing system.

[0271] "Analysis" is the process of analyzing obtained data through machine learning algorithms to extract useful patterns and insights.

[0272] "Recommendation" means suggesting the most appropriate resources or equipment based on the user's needs and history.

[0273] "Resources" is a comprehensive concept encompassing the tools, data, and human resources necessary to advance a user's project.

[0274] "External resources" refer to data sources and information provision services that exist outside of the information processing system and are used to collect project-related information.

[0275] "Operating procedures" are guidelines that show, in chronological order, the specific steps and methods that users need to effectively manage a project.

[0276] This invention relates to a comprehensive information processing system for improving user efficiency in starting new projects. The user inputs their interests and goals through a terminal. For example, if a user is considering opening an online store, they input this into the terminal. The terminal sends the collected information to a server. The server analyzes the data using machine learning algorithms with programming languages ​​such as Python and R. Specifically, it uses libraries such as scikit-learn and TensorFlow to recommend the most suitable resources and tools for the user. The server also automatically collects relevant information from the internet using external APIs and web crawling technologies and generates specific operating procedures. These procedures are displayed on the user's terminal to assist in project implementation.

[0277] The server tracks the user's project progress in real time via the terminal and provides feedback based on that progress. This allows the user to effectively advance their project based on the feedback. For example, if the user enters the prompt, "I'm thinking of opening an online store. Please tell me the best platform and procedure," the server will provide a list of the best e-commerce platforms and related tools, and present specific steps. In this way, the present invention fully supports the user's project execution.

[0278] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0279] Step 1:

[0280] The user enters their interests and goals for a new project into the terminal. For example, if they are considering "opening an online store," they would write that down. This input includes prompts and keywords. The terminal receives this input, combines it with the user's past project history and personal data, and prepares to send it to the server.

[0281] Step 2:

[0282] The terminal sends user-obtained interests and goals, along with related historical data, to the server. The input data includes specific requirements and goals related to the project, and the output becomes integrated data sent to the server. This integrated data is then prepared for subsequent analysis.

[0283] Step 3:

[0284] The server starts programs in Python or R and applies machine learning algorithms to analyze the received data. The input is the integrated dataset, and the output is a list of resources and tools optimal for the user. Specifically, using scikit-learn and TensorFlow, it compares with past data and recommends the optimal options.

[0285] Step 4:

[0286] The server uses external APIs and web crawling technologies to collect the latest information related to the user's goals. The input includes keywords and requirements related to the user's goals, and the output is a list of specific implementation procedures. This generates the specific procedures required for the user's project.

[0287] Step 5:

[0288] The project procedures generated by the server are sent to the terminal and visually presented to the user. The terminal displays the procedures in a form easy for the user to understand and is executable according to the steps. The input is the generated procedure data, and the output is the presentation to the user.

[0289] Step 6:

[0290] The user progresses the project according to the presented procedures. The progress status is recorded on the terminal and sent to the server in real time. This sends the user's progress data to the server as input data.

[0291] Step 7:

[0292] The server analyzes the progress data and generates feedback and advice for the next steps for the user. It utilizes a machine learning model to provide efficient support. The output is specific feedback to the user, effectively supporting project management.

[0293] (Application Example 1)

[0294] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0295] In recent years, many individuals and small businesses have been looking to open online stores, but they lack support in selecting the optimal electronic payment technology and implementing it effectively. As a result, incorrect selection or configuration of electronic payment systems can significantly impact business operations and security. This invention aims to solve this problem by providing a system that supports users in selecting appropriate payment technology and smoothly implementing and operating it.

[0296] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0297] In this invention, the server includes a device for acquiring the user's interests and goals, a device for transmitting the user's history and personal information, and a device for analyzing the received information and recommending the most suitable payment technology and resources. This enables the user to efficiently and effectively select and implement a payment system.

[0298] A "user" refers to an individual or business that uses the system and is interested in opening or operating an online store.

[0299] "Interests and goals" refer to what the user wants to achieve or the project themes they are interested in.

[0300] "Device" refers to a hardware or software configuration for acquiring, transmitting, and analyzing user data.

[0301] "History and personal information" refers to data that shows a user's past activities and profile, including information related to the execution of projects.

[0302] "Analyzing information" refers to the process of applying data analysis and machine learning techniques to identify optimal payment technologies and resources based on the collected data.

[0303] "Payment technologies and resources" refer to the technical means and available systems for conducting online payments.

[0304] "Information network" refers to the network infrastructure and communication technologies necessary to obtain information from external sources.

[0305] "Program interface" refers to the protocols and APIs for collaborating with external information networks to obtain data.

[0306] "Data collection technology" refers to the technology for automatically obtaining necessary information from external sources using web crawling or APIs.

[0307] To implement this invention, it is required that the user's terminal and the server on the cloud cooperate. First, the user uses the terminal to input their interests and goals regarding opening an online store. At this time, the user's past history and personal information are also collected by the terminal.

[0308] When the server receives this data, it executes a machine learning algorithm to recommend optimal payment technologies and resources. Specifically, the server uses libraries such as TensorFlow and scikit-learn to analyze the data. The recommended payment technologies include specific steps for guiding the introduction procedure step by step. This enables the user to proceed with the introduction work efficiently.

[0309] In this process, the server automatically retrieves relevant information from external information networks. This process utilizes APIs and web crawling, employing programmatic interfaces and data collection technologies. This allows the server to reflect the latest information in real time and provide users with optimal advice.

[0310] Furthermore, user progress is tracked in real time by the server, and feedback is provided as needed. This feedback includes suggestions for improvement to the ongoing project and advice for the next steps. For example, it helps resolve issues by providing specific steps to fix error messages pointed out by the user.

[0311] A concrete example of this system would be a user who wants to "open a small online accessories shop." In this case, the server would suggest appropriate payment technologies and clearly outline the implementation procedures for each. It would also provide advice on security and operational optimization after implementation.

[0312] An example of a prompt for a generated AI model would be, "I'm thinking of opening an online accessory shop. Could you recommend an electronic payment system and explain the setup procedure?"

[0313] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0314] Step 1:

[0315] The device receives the user's interests and goals. At the same time, it collects past project history and personal information as input. This data is organized in text format and prepared for transmission to the server.

[0316] Step 2:

[0317] The server receives user data sent from the terminal. Based on this input data, it first analyzes the user's interests and goals. Machine learning algorithms are used for the analysis, such as TensorFlow or scikit-learn, to process the data and extract features. This allows the server to search the database for the most suitable payment technology for the user.

[0318] Step 3:

[0319] The server recommends the most suitable payment technology and resources based on the analysis results. In this recommendation process, the analyzed data is used as input, and the program generates a recommendation list. This list includes technical details and specific implementation instructions, which are then provided to the user.

[0320] Step 4:

[0321] The server retrieves relevant information from external information networks. This step involves a process of collecting the latest information using APIs and web crawling technologies. The retrieved data is then used to create suggestions for the user.

[0322] Step 5:

[0323] The terminal displays to the user a list and instructions regarding optimized payment technologies received from the server. The user then proceeds with setting up the online store based on the provided instructions.

[0324] Step 6:

[0325] The server monitors the user's progress in real time. In this step, the server analyzes the progress based on the user's operation history and input data. Progress data is generated as feedback and provided to the user as guidance for the next step.

[0326] Step 7:

[0327] Users make necessary adjustments and improvements based on feedback from the server. Each feedback includes specific advice and problem-solving methods to support users in efficiently advancing the project.

[0328] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0329] The embodiment of the present invention begins with an initial stage where the user inputs project interests and goals. The terminal provides the user with an interface for inputting project goals, and the user can input their interests and preferences in detail. The terminal also collects the user's past project history and personal data.

[0330] Next, the device sends the collected information to the server. The server uses machine learning algorithms to analyze this information and recommend the most suitable tools and resources for the user's project. For example, if the project is "creating an online course," the server may recommend video editing software and an LMS platform.

[0331] Next, the server gathers up-to-date information related to the project's objectives from the external network. This involves using APIs and web crawling technologies to collect the knowledge and technical documents the user needs. Based on the information collected by the server, it designs the specific execution steps for the project. This includes installation procedures for the software to be used and preparation steps for the necessary materials.

[0332] The generated steps are presented to the user via the device, and the user follows this guide to complete the project. The emotion engine analyzes the user's input, progress, and even emotions, and generates feedback and motivational messages tailored to the project's progress. For example, if the user is feeling stressed about the progress, the emotion engine provides advice and encouraging messages to help them relax.

[0333] Thus, this system has the functionality to comprehensively support the project process from start to finish, while also taking into account the user's emotional state. As a result, users can significantly reduce their workload and achieve their goals more efficiently.

[0334] The following describes the processing flow.

[0335] Step 1:

[0336] The user uses a terminal to input the project's purpose and goals. Specifically, the user provides the system with the project's theme and desired outcomes through a text input interface.

[0337] Step 2:

[0338] The terminal sends the user's input data to the server. At the same time, the terminal also sends past project history and personal data.

[0339] Step 3:

[0340] The server analyzes the information it receives. It then runs machine learning algorithms to recommend the best tools and resources for the user's project. This process identifies the best software and platform to meet the user's needs.

[0341] Step 4:

[0342] The server automatically collects relevant information from the external network. Using APIs and web crawling technologies, it obtains the technical information and latest trend information necessary for the project.

[0343] Step 5:

[0344] Based on the information collected by the server, specific execution procedures are designed. This includes clear guidelines for project progress, such as "step-by-step instructions" and "critical checkpoints."

[0345] Step 6:

[0346] The terminal presents the user with the execution steps received from the server. The user can then proceed with the project by referring to these steps.

[0347] Step 7:

[0348] The device activates an emotion engine to recognize the user's emotions. It analyzes the user's text input and progress to generate data about the user's psychological state.

[0349] Step 8:

[0350] Based on the results of the emotion engine, the server creates feedback and motivational messages tailored to the user's progress. If the user is feeling stressed, this includes suggestions for relaxation.

[0351] Step 9:

[0352] The device displays generated feedback and messages to the user. Based on this, the user can maintain motivation for the project and move on to the next step.

[0353] (Example 2)

[0354] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0355] Traditionally, when users individually planned and implemented projects, it not only required a great deal of time and effort, but also presented problems such as the inability to effectively utilize appropriate information and resources. Furthermore, maintaining motivation and managing emotions during project progress was difficult, potentially leading to a decrease in overall efficiency.

[0356] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0357] In this invention, the server includes means for acquiring the user's interests and goals, means for transmitting the user's history and personal data, means for analyzing the received data and recommending the most suitable tools and resources, means for collecting external information and designing specific execution procedures, and means for monitoring the user's progress and providing feedback that takes into account their emotional state. This allows the user to be efficiently supported from the start to the completion of a project, effectively consolidating necessary resources and knowledge while also receiving emotional support.

[0358] A "user" refers to an individual or organization that uses the system to input information related to project planning and implementation.

[0359] "Interests and goals" refers to information that indicates the content of the project the user wants to achieve and the desired results.

[0360] "History and personal data" refers to information related to a user's past project activities and data concerning the user's unique characteristics.

[0361] "Received data" refers to a collection of information, including interests, goals, history, and personal data, that is sent by the user.

[0362] "Optimal tools and resources" refers to the tools and resources recommended to support the user in carrying out their project.

[0363] "Analysis" refers to the process of examining received data in detail and determining the tools and resources best suited to the user's needs.

[0364] "Recommended methods" refer to the methods and techniques for identifying and suggesting the most suitable tools and resources for the user.

[0365] "External information" refers to data obtained from external sources that contains the latest knowledge and technical information related to the user's project.

[0366] "Execution procedure" refers to the specific steps and methods that users should follow when carrying out a project.

[0367] "Monitoring" refers to continuously observing the progress and work being done on a user's project.

[0368] "Feedback that takes emotional state into account" refers to information that provides encouragement and advice regarding the progress of a project, taking into account the user's emotional state.

[0369] This invention is a system that supports users in achieving their project goals through the cooperation of the user, terminal, and server.

[0370] The user first uses the terminal's interface to input their interests and goals regarding their project. In addition to the entered interests and goals, the terminal also collects the user's past project history and personal data. This information is sent to the server via HTTP or WebSocket, using a secure communication protocol that includes error checking.

[0371] The server performs analysis using machine learning algorithms based on the received data. Python's scikit-learn or similar data processing libraries can be used for this analysis. This analysis identifies the most suitable tools and resources for the user's project.

[0372] Subsequently, the server utilizes APIs and web crawling technologies to collect relevant information from external networks. This process uses technologies such as BeautifulSoup and OpenAI APIs to retrieve data. Based on this information, the server designs specific execution steps, which are then generated in Markdown or other document formats.

[0373] The user is presented with the designed execution procedure via a terminal. As the user progresses through the project according to the procedure, an emotion engine installed on the server monitors the user's emotional state and project progress, generating and providing feedback and encouraging messages as needed. This utilizes natural language processing with advanced text analysis techniques.

[0374] As a concrete example, let's consider a scenario where the user's goal is to "create an online educational course." In this case, the server would recommend candidate video editing software and learning management systems (LMS), collect information on relevant educational methods and materials, and present the user with the necessary steps.

[0375] Examples of prompt statements include the following:

[0376] "Please enter the goals for your new project and tell us the steps needed to achieve them. The following information is available…"

[0377] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0378] Step 1:

[0379] The user uses the terminal's interface to input their project interests and goals. They can also enter specific keywords and expected outcomes. The user's project interests and goals are sent to the terminal as input data. The terminal receives this data and displays a confirmation message to the user to indicate completion of the input.

[0380] Step 2:

[0381] The device collects user interests and goals, as well as the user's past project history and personal data. This data is organized based on the user's ID and sent to the server. Specifically, the device displays a message indicating that it is sending data to the server and sends the data using HTTP or WebSocket.

[0382] Step 3:

[0383] The server receives data sent from the terminal and analyzes it using machine learning algorithms. The analysis applies clustering and recommendation algorithms to identify the most suitable tools and resources for the user's project. As output, a list of appropriate tools and resources is generated and sent to the terminal.

[0384] Step 4:

[0385] The server collects relevant information from external networks using APIs and web crawling technologies. Keywords related to the user's interests and goals are used as input. The server uses BeautifulSoup and OpenAI APIs to collect the latest technical information and materials, analyzes them, and generates foundational data for execution procedures.

[0386] Step 5:

[0387] Based on the collected information, the server designs specific execution steps to help the user achieve their project goals. The procedure manual is created using a document format such as Markdown. The designed procedure manual is then sent to the terminal as output. The server sends a confirmation message to the terminal stating, "Execution steps have been generated."

[0388] Step 6:

[0389] The terminal presents the user with the execution steps sent from the server. The user proceeds with the project by following these steps. The terminal displays each step of the procedure and prompts the user to take the next action accordingly. Specifically, it displays prompts such as, "Are you ready to proceed to the next step?"

[0390] Step 7:

[0391] The server monitors the user's emotional state and progress throughout the project using an emotion engine. Inputs include user progress data and entered emotional states. The server analyzes this data and, if necessary, generates encouraging or feedback messages, which are then presented to the user via the terminal. For example, it might send a message like, "Great job! Progress is on track."

[0392] (Application Example 2)

[0393] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0394] Currently, in project execution, users spend a lot of time selecting necessary information and tools, making it difficult to manage progress and maintain motivation. This is especially true for personal projects within the home, where a lack of adequate support is particularly noticeable, often leading to a decline in individual motivation.

[0395] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0396] In this invention, the server includes means for acquiring user requests, means for transmitting user history information, means for analyzing received data and suggesting optimal equipment and resources, and means for evaluating the user's emotional state during project progress and generating appropriate messages. This enables the user to efficiently manage the project process from start to finish and to maintain high motivation throughout the process.

[0397] "Means for obtaining user requirements" refers to an interface that allows users to input their goals and desired outcomes for a project via digital devices and to accurately receive that information.

[0398] "Means for transmitting user history information" refers to a function for collecting data related to a user's past activities and projects and sending it to a server for analysis.

[0399] "A means of analyzing received data and proposing the optimal equipment and resources" refers to a process that uses algorithms based on collected information to present the tools and resources most suitable for the user's project execution.

[0400] "Means for evaluating the user's emotional state during project progress and generating appropriate messages" refers to a mechanism for analyzing the user's mental state according to the project's progress and providing encouragement or advice as needed.

[0401] The system for implementing this invention mainly consists of three components: a server, a terminal, and a user.

[0402] First, users provide information through an interface using their device to input project goals and interests. This interface is designed to allow users to easily input data. Similarly, user history information is also obtained from the device and sent to the server.

[0403] Next, the server analyzes the received data. This analysis utilizes a machine learning framework using Python (e.g., TensorFlow) to identify and suggest the most suitable equipment and resources for the user's project. Specifically, if a user wants to start a home garden, it will show the necessary tools, materials, and procedures. In this process, APIs and web crawling technologies are used to collect relevant information from external networks.

[0404] Furthermore, the server tracks user progress, assesses user emotional state, and generates messages to maintain project motivation. This process utilizes natural language processing libraries (e.g., NLTK). Specifically, if a user is feeling stressed about their progress, the server automatically provides relaxing advice and encouraging messages.

[0405] Ultimately, the generated procedures and messages are presented to the user through the device. This allows the user to carry out the project efficiently and effectively.

[0406] Examples of prompts include, "Please suggest the steps and tools needed to start a home garden project. Also, please create encouraging messages for the user as they progress." Based on such prompts, the AI ​​model generates appropriate output to meet the user's needs.

[0407] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0408] Step 1:

[0409] Users input project goals and interests using a device. This input is stored on the device as specific project goals and aspirations. This information specifically reflects the user's intentions and expected project outcomes.

[0410] Step 2:

[0411] The terminal collects historical information about past projects along with the user's input data. This includes information about the projects the user has worked on previously and the resources they used. This historical information is used to extract the user's project habits and preferences. The collected data is sent to the server.

[0412] Step 3:

[0413] The server analyzes the received user input data and historical information. This analysis uses a Python machine learning framework (e.g., TensorFlow) and algorithms to identify the most suitable equipment and resources for the user. The input to this analysis is the characteristics and historical data of the project the user is requesting, and the output is a list of suitable tool and resource candidates.

[0414] Step 4:

[0415] The server uses APIs and web crawling technologies to retrieve data from external networks to collect necessary information. This externally retrieved data includes the latest technical information and project-related documents. The input to this process is project-related keywords, and the output is relevant, detailed information.

[0416] Step 5:

[0417] The server designs specific execution procedures based on the analysis results and collected information. This includes installation instructions for the tools to be used and step-by-step guides for proceeding with the project. The input for this step is the analyzed information and acquired external data, and the output is the execution procedure for the user.

[0418] Step 6:

[0419] The terminal presents the user with the generated execution procedure. This procedure is a detailed guide to support the project from start to finish. The user can then complete the project by following this procedure.

[0420] Step 7:

[0421] The server analyzes user progress data and emotional state during project execution and generates appropriate messages. It uses a natural language processing library (e.g., NLTK) to provide encouragement and suggestions tailored to the user's state. The input for this analysis is progress data and user psychological feedback, and the output is a personalized message of encouragement.

[0422] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0423] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0424] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0425] [Third Embodiment]

[0426] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0427] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0428] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0429] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0430] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0431] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0432] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0433] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0434] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0435] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0436] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0437] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0438] One embodiment of the present invention begins with the user inputting their interests and goals for a new project. In this process, the terminal collects the user's interests and goals, as well as their past project history and personal data. For example, consider the case where the user is interested in "opening an online store."

[0439] The device sends the collected information to the server. The server uses machine learning algorithms to analyze this information and recommend the most suitable tools and resources for the user's project. Specifically, if the user wants to open an online store, the server will list available e-commerce platforms and related tools (e.g., product management software).

[0440] Next, the server automatically collects information related to the user's goals from the external network and generates specific execution steps. These steps are designed to guide the project step by step and are displayed on the terminal. For example, it provides detailed instructions for the steps from domain acquisition to site design and product registration.

[0441] After the terminal presents the user with instructions, the user can proceed with the project by following this guide. Progress is updated in real time via the terminal, and the server analyzes this progress data to generate feedback. By providing users with evaluations based on their progress and advice for the next steps, they can effectively complete the project.

[0442] In this way, this system significantly reduces the user's workload and can provide comprehensive support from the start to the completion of a project.

[0443] The following describes the processing flow.

[0444] Step 1:

[0445] Users input the project's purpose and goals using their devices. Specifically, users enter the specific results they want to achieve through the project and their areas of interest into a form.

[0446] Step 2:

[0447] The terminal sends user input data to the server. This data includes the user's past project history and personal data.

[0448] Step 3:

[0449] The server analyzes the user data it receives. This process uses machine learning algorithms to generate a list of optimal tools and resources tailored to the user's needs.

[0450] Step 4:

[0451] The server collects relevant information from the external network. This information includes the latest technical information and market trends related to the user's goals.

[0452] Step 5:

[0453] Based on the information collected by the server, the specific execution procedures for the project are designed. These procedures comprise the concrete steps necessary for the user to advance their project.

[0454] Step 6:

[0455] The terminal presents the user with the generated execution steps. The user can then proceed with the project while reviewing these steps.

[0456] Step 7:

[0457] Users update project progress using their devices. They input feedback on completed steps and register progress in real time.

[0458] Step 8:

[0459] The server analyzes progress data and generates feedback for the user. This feedback includes progress-based evaluations and advice for the next steps.

[0460] Step 9:

[0461] The terminal displays feedback from the server to the user. Based on this feedback, the user can decide on the next steps in carrying out the project.

[0462] (Example 1)

[0463] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0464] In modern information processing systems, a challenge exists in that it is difficult for users to quickly find appropriate resources and procedures when starting a new project. In particular, there is a need to efficiently collect relevant information from vast amounts of data and provide feedback tailored to the individual needs of the user.

[0465] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0466] In this invention, the server includes a device for acquiring user requests and objectives, a device for analyzing the received data and recommending the optimal devices and resources, and a device for collecting relevant information from external resources and generating specific operating procedures. This allows users to obtain specific execution procedures for their projects, thereby improving the efficiency of project progress.

[0467] A "user" is an individual or group that uses an information processing system to input their requests and goals and to receive relevant information and resources.

[0468] A "requirement" is a specific need or desire that a user wants to achieve or that is necessary for the progress of a project.

[0469] A "goal" refers to the specific results or direction that the user wants to achieve, and represents the ultimate objective of the project.

[0470] A "device" is a hardware or software component designed to perform a specific function within an information processing system.

[0471] "Analysis" is the process of analyzing obtained data through machine learning algorithms to extract useful patterns and insights.

[0472] "Recommendation" means suggesting the most appropriate resources or equipment based on the user's needs and history.

[0473] "Resources" is a comprehensive concept encompassing the tools, data, and human resources necessary to advance a user's project.

[0474] "External resources" refer to data sources and information provision services that exist outside of the information processing system and are used to collect project-related information.

[0475] "Operating procedures" are guidelines that show, in chronological order, the specific steps and methods that users need to effectively manage a project.

[0476] This invention relates to a comprehensive information processing system for improving user efficiency in starting new projects. The user inputs their interests and goals through a terminal. For example, if a user is considering opening an online store, they input this into the terminal. The terminal sends the collected information to a server. The server analyzes the data using machine learning algorithms with programming languages ​​such as Python and R. Specifically, it uses libraries such as scikit-learn and TensorFlow to recommend the most suitable resources and tools for the user. The server also automatically collects relevant information from the internet using external APIs and web crawling technologies and generates specific operating procedures. These procedures are displayed on the user's terminal to assist in project implementation.

[0477] The server tracks the user's project progress in real time via the terminal and provides feedback based on that progress. This allows the user to effectively advance their project based on the feedback. For example, if the user enters the prompt, "I'm thinking of opening an online store. Please tell me the best platform and procedure," the server will provide a list of the best e-commerce platforms and related tools, and present specific steps. In this way, the present invention fully supports the user's project execution.

[0478] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0479] Step 1:

[0480] The user enters their interests and goals for a new project into the terminal. For example, if they are considering "opening an online store," they would write that down. This input includes prompts and keywords. The terminal receives this input, combines it with the user's past project history and personal data, and prepares to send it to the server.

[0481] Step 2:

[0482] The terminal sends user-obtained interests and goals, along with related historical data, to the server. The input data includes specific requirements and goals related to the project, and the output becomes integrated data sent to the server. This integrated data is then prepared for subsequent analysis.

[0483] Step 3:

[0484] The server launches Python or R programs to analyze the received data and applies machine learning algorithms. The input is an integrated dataset, and the output generates a list of the most suitable resources and tools for the user. Specifically, it uses scikit-learn and TensorFlow to compare current data with historical data and recommend the best options.

[0485] Step 4:

[0486] The server uses external APIs and web crawling technologies to collect up-to-date information related to the user's goals. Input includes keywords and requests related to the user's goals, and output is a list of specific execution steps. This generates the specific steps required for the user's project.

[0487] Step 5:

[0488] The project procedures generated by the server are sent to the terminal and visually presented to the user. The terminal displays the procedures in a user-friendly format, allowing the user to follow the steps. The input is the generated procedure data, and the output is the presentation to the user.

[0489] Step 6:

[0490] The user follows the provided instructions to advance the project. Progress is recorded on the device and sent to the server in real time. As a result, the user's progress data is sent to the server as input data.

[0491] Step 7:

[0492] The server analyzes progress data and generates feedback and advice for the next steps for the user. It utilizes machine learning models to provide efficient support. The output includes specific feedback for the user, effectively supporting project management.

[0493] (Application Example 1)

[0494] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0495] In recent years, many individuals and small businesses have been looking to open online stores, but they lack support in selecting the optimal electronic payment technology and implementing it effectively. As a result, incorrect selection or configuration of electronic payment systems can significantly impact business operations and security. This invention aims to solve this problem by providing a system that supports users in selecting appropriate payment technology and smoothly implementing and operating it.

[0496] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0497] In this invention, the server includes a device for acquiring the user's interests and goals, a device for transmitting the user's history and personal information, and a device for analyzing the received information and recommending the most suitable payment technology and resources. This enables the user to efficiently and effectively select and implement a payment system.

[0498] A "user" refers to an individual or business that uses the system and is interested in opening or operating an online store.

[0499] "Interests and goals" refer to what the user wants to achieve or the project themes they are interested in.

[0500] "Device" refers to a hardware or software configuration for acquiring, transmitting, and analyzing user data.

[0501] "History and personal information" refers to data that shows a user's past activities and profile, including information related to the execution of projects.

[0502] "Analyzing information" refers to the process of applying data analysis and machine learning techniques to identify the optimal payment technologies and resources based on collected data.

[0503] "Payment technologies and resources" refers to the technical means and available systems for conducting online payments.

[0504] "Information network" refers to the network infrastructure and communication technologies necessary to acquire information from external sources.

[0505] A "program interface" refers to protocols and APIs used to interact with external information networks and acquire data.

[0506] "Data collection technology" refers to technologies that automatically acquire necessary information from external sources using web crawling or APIs.

[0507] To implement this invention, it is necessary for a user's device to work in conjunction with a server in the cloud. First, the user uses their device to input their interests and goals regarding the opening of an online store. At the same time, the device also collects the user's past history and personal information.

[0508] Upon receiving this data, the server runs machine learning algorithms to recommend the optimal payment technology and resources. Specifically, the server analyzes the data using libraries such as TensorFlow and scikit-learn. The recommended payment technology includes specific steps to guide users through its implementation process. This allows users to proceed with the implementation efficiently.

[0509] In this process, the server automatically retrieves relevant information from external information networks. This process utilizes APIs and web crawling, employing programmatic interfaces and data collection technologies. This allows the server to reflect the latest information in real time and provide users with optimal advice.

[0510] Furthermore, user progress is tracked in real time by the server, and feedback is provided as needed. This feedback includes suggestions for improvement to the ongoing project and advice for the next steps. For example, it helps resolve issues by providing specific steps to fix error messages pointed out by the user.

[0511] A concrete example of this system would be a user who wants to "open a small online accessories shop." In this case, the server would suggest appropriate payment technologies and clearly outline the implementation procedures for each. It would also provide advice on security and operational optimization after implementation.

[0512] An example of a prompt for a generated AI model would be, "I'm thinking of opening an online accessory shop. Could you recommend an electronic payment system and explain the setup procedure?"

[0513] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0514] Step 1:

[0515] The device receives the user's interests and goals. At the same time, it collects past project history and personal information as input. This data is organized in text format and prepared for transmission to the server.

[0516] Step 2:

[0517] The server receives user data sent from the terminal. Based on this input data, it first analyzes the user's interests and goals. Machine learning algorithms are used for the analysis, such as TensorFlow or scikit-learn, to process the data and extract features. This allows the server to search the database for the most suitable payment technology for the user.

[0518] Step 3:

[0519] The server recommends the most suitable payment technology and resources based on the analysis results. In this recommendation process, the analyzed data is used as input, and the program generates a recommendation list. This list includes technical details and specific implementation instructions, which are then provided to the user.

[0520] Step 4:

[0521] The server retrieves relevant information from external information networks. This step involves a process of collecting the latest information using APIs and web crawling technologies. The retrieved data is then used to create suggestions for the user.

[0522] Step 5:

[0523] The terminal displays to the user a list and instructions regarding optimized payment technologies received from the server. The user then proceeds with setting up the online store based on the provided instructions.

[0524] Step 6:

[0525] The server monitors the user's progress in real time. In this step, the server analyzes the progress based on the user's operation history and input data. Progress data is generated as feedback and provided to the user as guidance for the next step.

[0526] Step 7:

[0527] Users make necessary adjustments and improvements based on feedback from the server. Each feedback includes specific advice and problem-solving methods to support users in efficiently advancing the project.

[0528] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0529] The embodiment of the present invention begins with an initial stage where the user inputs project interests and goals. The terminal provides the user with an interface for inputting project goals, and the user can input their interests and preferences in detail. The terminal also collects the user's past project history and personal data.

[0530] Next, the device sends the collected information to the server. The server uses machine learning algorithms to analyze this information and recommend the most suitable tools and resources for the user's project. For example, if the project is "creating an online course," the server may recommend video editing software and an LMS platform.

[0531] Next, the server gathers up-to-date information related to the project's objectives from the external network. This involves using APIs and web crawling technologies to collect the knowledge and technical documents the user needs. Based on the information collected by the server, it designs the specific execution steps for the project. This includes installation procedures for the software to be used and preparation steps for the necessary materials.

[0532] The generated steps are presented to the user via the device, and the user follows this guide to complete the project. The emotion engine analyzes the user's input, progress, and even emotions, and generates feedback and motivational messages tailored to the project's progress. For example, if the user is feeling stressed about the progress, the emotion engine provides advice and encouraging messages to help them relax.

[0533] Thus, this system has the functionality to comprehensively support the project process from start to finish, while also taking into account the user's emotional state. As a result, users can significantly reduce their workload and achieve their goals more efficiently.

[0534] The following describes the processing flow.

[0535] Step 1:

[0536] The user uses a terminal to input the project's purpose and goals. Specifically, the user provides the system with the project's theme and desired outcomes through a text input interface.

[0537] Step 2:

[0538] The terminal sends the user's input data to the server. At the same time, the terminal also sends past project history and personal data.

[0539] Step 3:

[0540] The server analyzes the information it receives. It then runs machine learning algorithms to recommend the best tools and resources for the user's project. This process identifies the best software and platform to meet the user's needs.

[0541] Step 4:

[0542] The server automatically collects relevant information from the external network. Using APIs and web crawling technologies, it obtains the technical information and latest trend information necessary for the project.

[0543] Step 5:

[0544] Based on the information collected by the server, specific execution procedures are designed. This includes clear guidelines for project progress, such as "step-by-step instructions" and "critical checkpoints."

[0545] Step 6:

[0546] The terminal presents the user with the execution steps received from the server. The user can then proceed with the project by referring to these steps.

[0547] Step 7:

[0548] The device activates an emotion engine to recognize the user's emotions. It analyzes the user's text input and progress to generate data about the user's psychological state.

[0549] Step 8:

[0550] Based on the results of the emotion engine, the server creates feedback and motivational messages tailored to the user's progress. If the user is feeling stressed, this includes suggestions for relaxation.

[0551] Step 9:

[0552] The device displays generated feedback and messages to the user. Based on this, the user can maintain motivation for the project and move on to the next step.

[0553] (Example 2)

[0554] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0555] Traditionally, when users individually planned and implemented projects, it not only required a great deal of time and effort, but also presented problems such as the inability to effectively utilize appropriate information and resources. Furthermore, maintaining motivation and managing emotions during project progress was difficult, potentially leading to a decrease in overall efficiency.

[0556] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0557] In this invention, the server includes means for acquiring the user's interests and goals, means for transmitting the user's history and personal data, means for analyzing the received data and recommending the most suitable tools and resources, means for collecting external information and designing specific execution procedures, and means for monitoring the user's progress and providing feedback that takes into account their emotional state. This allows the user to be efficiently supported from the start to the completion of a project, effectively consolidating necessary resources and knowledge while also receiving emotional support.

[0558] A "user" refers to an individual or organization that uses the system to input information related to project planning and implementation.

[0559] "Interests and goals" refers to information that indicates the content of the project the user wants to achieve and the desired results.

[0560] "History and personal data" refers to information related to a user's past project activities and data concerning the user's unique characteristics.

[0561] "Received data" refers to a collection of information, including interests, goals, history, and personal data, that is sent by the user.

[0562] "Optimal tools and resources" refers to the tools and resources recommended to support the user in carrying out their project.

[0563] "Analysis" refers to the process of examining received data in detail and determining the tools and resources best suited to the user's needs.

[0564] "Recommended methods" refer to the methods and techniques for identifying and suggesting the most suitable tools and resources for the user.

[0565] "External information" refers to data obtained from external sources that contains the latest knowledge and technical information related to the user's project.

[0566] "Execution procedure" refers to the specific steps and methods that users should follow when carrying out a project.

[0567] "Monitoring" refers to continuously observing the progress and work being done on a user's project.

[0568] "Feedback that takes emotional state into account" refers to information that provides encouragement and advice regarding the progress of a project, taking into account the user's emotional state.

[0569] This invention is a system that supports users in achieving their project goals through the cooperation of the user, terminal, and server.

[0570] The user first uses the terminal's interface to input their interests and goals regarding their project. In addition to the entered interests and goals, the terminal also collects the user's past project history and personal data. This information is sent to the server via HTTP or WebSocket, using a secure communication protocol that includes error checking.

[0571] The server performs analysis using machine learning algorithms based on the received data. Python's scikit-learn or similar data processing libraries can be used for this analysis. This analysis identifies the most suitable tools and resources for the user's project.

[0572] Subsequently, the server utilizes APIs and web crawling technologies to collect relevant information from external networks. This process uses technologies such as BeautifulSoup and OpenAI APIs to retrieve data. Based on this information, the server designs specific execution steps, which are then generated in Markdown or other document formats.

[0573] The user is presented with the designed execution procedure via a terminal. As the user progresses through the project according to the procedure, an emotion engine installed on the server monitors the user's emotional state and project progress, generating and providing feedback and encouraging messages as needed. This utilizes natural language processing with advanced text analysis techniques.

[0574] As a concrete example, let's consider a scenario where the user's goal is to "create an online educational course." In this case, the server would recommend candidate video editing software and learning management systems (LMS), collect information on relevant educational methods and materials, and present the user with the necessary steps.

[0575] Examples of prompt statements include the following:

[0576] "Please enter the goals for your new project and tell us the steps needed to achieve them. The following information is available…"

[0577] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0578] Step 1:

[0579] The user uses the terminal's interface to input their project interests and goals. They can also enter specific keywords and expected outcomes. The user's project interests and goals are sent to the terminal as input data. The terminal receives this data and displays a confirmation message to the user to indicate completion of the input.

[0580] Step 2:

[0581] The device collects user interests and goals, as well as the user's past project history and personal data. This data is organized based on the user's ID and sent to the server. Specifically, the device displays a message indicating that it is sending data to the server and sends the data using HTTP or WebSocket.

[0582] Step 3:

[0583] The server receives data sent from the terminal and analyzes it using machine learning algorithms. The analysis applies clustering and recommendation algorithms to identify the most suitable tools and resources for the user's project. As output, a list of appropriate tools and resources is generated and sent to the terminal.

[0584] Step 4:

[0585] The server collects relevant information from external networks using APIs and web crawling technologies. Keywords related to the user's interests and goals are used as input. The server uses BeautifulSoup and OpenAI APIs to collect the latest technical information and materials, analyzes them, and generates foundational data for execution procedures.

[0586] Step 5:

[0587] Based on the collected information, the server designs specific execution steps to help the user achieve their project goals. The procedure manual is created using a document format such as Markdown. The designed procedure manual is then sent to the terminal as output. The server sends a confirmation message to the terminal stating, "Execution steps have been generated."

[0588] Step 6:

[0589] The terminal presents the user with the execution steps sent from the server. The user proceeds with the project by following these steps. The terminal displays each step of the procedure and prompts the user to take the next action accordingly. Specifically, it displays prompts such as, "Are you ready to proceed to the next step?"

[0590] Step 7:

[0591] The server monitors the user's emotional state and progress throughout the project using an emotion engine. Inputs include user progress data and entered emotional states. The server analyzes this data and, if necessary, generates encouraging or feedback messages, which are then presented to the user via the terminal. For example, it might send a message like, "Great job! Progress is on track."

[0592] (Application Example 2)

[0593] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0594] Currently, in project execution, users spend a lot of time selecting necessary information and tools, making it difficult to manage progress and maintain motivation. This is especially true for personal projects within the home, where a lack of adequate support is particularly noticeable, often leading to a decline in individual motivation.

[0595] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0596] In this invention, the server includes means for acquiring user requests, means for transmitting user history information, means for analyzing received data and suggesting optimal equipment and resources, and means for evaluating the user's emotional state during project progress and generating appropriate messages. This enables the user to efficiently manage the project process from start to finish and to maintain high motivation throughout the process.

[0597] "Means for obtaining user requirements" refers to an interface that allows users to input their goals and desired outcomes for a project via digital devices and to accurately receive that information.

[0598] "Means for transmitting user history information" refers to a function for collecting data related to a user's past activities and projects and sending it to a server for analysis.

[0599] "A means of analyzing received data and proposing the optimal equipment and resources" refers to a process that uses algorithms based on collected information to present the tools and resources most suitable for the user's project execution.

[0600] "Means for evaluating the user's emotional state during project progress and generating appropriate messages" refers to a mechanism for analyzing the user's mental state according to the project's progress and providing encouragement or advice as needed.

[0601] The system for implementing this invention mainly consists of three components: a server, a terminal, and a user.

[0602] First, users provide information through an interface using their device to input project goals and interests. This interface is designed to allow users to easily input data. Similarly, user history information is also obtained from the device and sent to the server.

[0603] Next, the server analyzes the received data. This analysis utilizes a machine learning framework using Python (e.g., TensorFlow) to identify and suggest the most suitable equipment and resources for the user's project. Specifically, if a user wants to start a home garden, it will show the necessary tools, materials, and procedures. In this process, APIs and web crawling technologies are used to collect relevant information from external networks.

[0604] Furthermore, the server tracks user progress, assesses user emotional state, and generates messages to maintain project motivation. This process utilizes natural language processing libraries (e.g., NLTK). Specifically, if a user is feeling stressed about their progress, the server automatically provides relaxing advice and encouraging messages.

[0605] Ultimately, the generated procedures and messages are presented to the user through the device. This allows the user to carry out the project efficiently and effectively.

[0606] Examples of prompts include, "Please suggest the steps and tools needed to start a home garden project. Also, please create encouraging messages for the user as they progress." Based on such prompts, the AI ​​model generates appropriate output to meet the user's needs.

[0607] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0608] Step 1:

[0609] Users input project goals and interests using a device. This input is stored on the device as specific project goals and aspirations. This information specifically reflects the user's intentions and expected project outcomes.

[0610] Step 2:

[0611] The terminal collects historical information about past projects along with the user's input data. This includes information about the projects the user has worked on previously and the resources they used. This historical information is used to extract the user's project habits and preferences. The collected data is sent to the server.

[0612] Step 3:

[0613] The server analyzes the received user input data and historical information. This analysis uses a Python machine learning framework (e.g., TensorFlow) and algorithms to identify the most suitable equipment and resources for the user. The input to this analysis is the characteristics and historical data of the project the user is requesting, and the output is a list of suitable tool and resource candidates.

[0614] Step 4:

[0615] The server uses APIs and web crawling technologies to retrieve data from external networks to collect necessary information. This externally retrieved data includes the latest technical information and project-related documents. The input to this process is project-related keywords, and the output is relevant, detailed information.

[0616] Step 5:

[0617] The server designs specific execution procedures based on the analysis results and collected information. This includes installation instructions for the tools to be used and step-by-step guides for proceeding with the project. The input for this step is the analyzed information and acquired external data, and the output is the execution procedure for the user.

[0618] Step 6:

[0619] The terminal presents the user with the generated execution procedure. This procedure is a detailed guide to support the project from start to finish. The user can then complete the project by following this procedure.

[0620] Step 7:

[0621] The server analyzes user progress data and emotional state during project execution and generates appropriate messages. It uses a natural language processing library (e.g., NLTK) to provide encouragement and suggestions tailored to the user's state. The input for this analysis is progress data and user psychological feedback, and the output is a personalized message of encouragement.

[0622] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0623] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0624] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0625] [Fourth Embodiment]

[0626] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0627] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0628] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0629] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0630] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0631] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0632] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0633] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0634] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0635] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0636] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0637] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0638] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0639] One embodiment of the present invention begins with the user inputting their interests and goals for a new project. In this process, the terminal collects the user's interests and goals, as well as their past project history and personal data. For example, consider the case where the user is interested in "opening an online store."

[0640] The device sends the collected information to the server. The server uses machine learning algorithms to analyze this information and recommend the most suitable tools and resources for the user's project. Specifically, if the user wants to open an online store, the server will list available e-commerce platforms and related tools (e.g., product management software).

[0641] Next, the server automatically collects information related to the user's goals from the external network and generates specific execution steps. These steps are designed to guide the project step by step and are displayed on the terminal. For example, it provides detailed instructions for the steps from domain acquisition to site design and product registration.

[0642] After the terminal presents the user with instructions, the user can proceed with the project by following this guide. Progress is updated in real time via the terminal, and the server analyzes this progress data to generate feedback. By providing users with evaluations based on their progress and advice for the next steps, they can effectively complete the project.

[0643] In this way, this system significantly reduces the user's workload and can provide comprehensive support from the start to the completion of a project.

[0644] The following describes the processing flow.

[0645] Step 1:

[0646] Users input the project's purpose and goals using their devices. Specifically, users enter the specific results they want to achieve through the project and their areas of interest into a form.

[0647] Step 2:

[0648] The terminal sends user input data to the server. This data includes the user's past project history and personal data.

[0649] Step 3:

[0650] The server analyzes the user data it receives. This process uses machine learning algorithms to generate a list of optimal tools and resources tailored to the user's needs.

[0651] Step 4:

[0652] The server collects relevant information from the external network. This information includes the latest technical information and market trends related to the user's goals.

[0653] Step 5:

[0654] Based on the information collected by the server, the specific execution procedures for the project are designed. These procedures comprise the concrete steps necessary for the user to advance their project.

[0655] Step 6:

[0656] The terminal presents the user with the generated execution steps. The user can then proceed with the project while reviewing these steps.

[0657] Step 7:

[0658] Users update project progress using their devices. They input feedback on completed steps and register progress in real time.

[0659] Step 8:

[0660] The server analyzes progress data and generates feedback for the user. This feedback includes progress-based evaluations and advice for the next steps.

[0661] Step 9:

[0662] The terminal displays feedback from the server to the user. Based on this feedback, the user can decide on the next steps in carrying out the project.

[0663] (Example 1)

[0664] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0665] In modern information processing systems, a challenge exists in that it is difficult for users to quickly find appropriate resources and procedures when starting a new project. In particular, there is a need to efficiently collect relevant information from vast amounts of data and provide feedback tailored to the individual needs of the user.

[0666] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0667] In this invention, the server includes a device for acquiring user requests and objectives, a device for analyzing the received data and recommending the optimal devices and resources, and a device for collecting relevant information from external resources and generating specific operating procedures. This allows users to obtain specific execution procedures for their projects, thereby improving the efficiency of project progress.

[0668] A "user" is an individual or group that uses an information processing system to input their requests and goals and to receive relevant information and resources.

[0669] A "requirement" is a specific need or desire that a user wants to achieve or that is necessary for the progress of a project.

[0670] A "goal" refers to the specific results or direction that the user wants to achieve, and represents the ultimate objective of the project.

[0671] A "device" is a hardware or software component designed to perform a specific function within an information processing system.

[0672] "Analysis" is the process of analyzing obtained data through machine learning algorithms to extract useful patterns and insights.

[0673] "Recommendation" means suggesting the most appropriate resources or equipment based on the user's needs and history.

[0674] "Resources" is a comprehensive concept encompassing the tools, data, and human resources necessary to advance a user's project.

[0675] "External resources" refer to data sources and information provision services that exist outside of the information processing system and are used to collect project-related information.

[0676] "Operating procedures" are guidelines that show, in chronological order, the specific steps and methods that users need to effectively manage a project.

[0677] This invention relates to a comprehensive information processing system for improving user efficiency in starting new projects. The user inputs their interests and goals through a terminal. For example, if a user is considering opening an online store, they input this into the terminal. The terminal sends the collected information to a server. The server analyzes the data using machine learning algorithms with programming languages ​​such as Python and R. Specifically, it uses libraries such as scikit-learn and TensorFlow to recommend the most suitable resources and tools for the user. The server also automatically collects relevant information from the internet using external APIs and web crawling technologies and generates specific operating procedures. These procedures are displayed on the user's terminal to assist in project implementation.

[0678] The server tracks the user's project progress in real time via the terminal and provides feedback based on that progress. This allows the user to effectively advance their project based on the feedback. For example, if the user enters the prompt, "I'm thinking of opening an online store. Please tell me the best platform and procedure," the server will provide a list of the best e-commerce platforms and related tools, and present specific steps. In this way, the present invention fully supports the user's project execution.

[0679] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0680] Step 1:

[0681] The user enters their interests and goals for a new project into the terminal. For example, if they are considering "opening an online store," they would write that down. This input includes prompts and keywords. The terminal receives this input, combines it with the user's past project history and personal data, and prepares to send it to the server.

[0682] Step 2:

[0683] The terminal sends user-obtained interests and goals, along with related historical data, to the server. The input data includes specific requirements and goals related to the project, and the output becomes integrated data sent to the server. This integrated data is then prepared for subsequent analysis.

[0684] Step 3:

[0685] The server launches Python or R programs to analyze the received data and applies machine learning algorithms. The input is an integrated dataset, and the output generates a list of the most suitable resources and tools for the user. Specifically, it uses scikit-learn and TensorFlow to compare current data with historical data and recommend the best options.

[0686] Step 4:

[0687] The server uses external APIs and web crawling technologies to collect up-to-date information related to the user's goals. Input includes keywords and requests related to the user's goals, and output is a list of specific execution steps. This generates the specific steps required for the user's project.

[0688] Step 5:

[0689] The project procedures generated by the server are sent to the terminal and visually presented to the user. The terminal displays the procedures in a user-friendly format, allowing the user to follow the steps. The input is the generated procedure data, and the output is the presentation to the user.

[0690] Step 6:

[0691] The user follows the provided instructions to advance the project. Progress is recorded on the device and sent to the server in real time. As a result, the user's progress data is sent to the server as input data.

[0692] Step 7:

[0693] The server analyzes progress data and generates feedback and advice for the next steps for the user. It utilizes machine learning models to provide efficient support. The output includes specific feedback for the user, effectively supporting project management.

[0694] (Application Example 1)

[0695] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0696] In recent years, many individuals and small businesses have been looking to open online stores, but they lack support in selecting the optimal electronic payment technology and implementing it effectively. As a result, incorrect selection or configuration of electronic payment systems can significantly impact business operations and security. This invention aims to solve this problem by providing a system that supports users in selecting appropriate payment technology and smoothly implementing and operating it.

[0697] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0698] In this invention, the server includes a device for acquiring the user's interests and goals, a device for transmitting the user's history and personal information, and a device for analyzing the received information and recommending the most suitable payment technology and resources. This enables the user to efficiently and effectively select and implement a payment system.

[0699] A "user" refers to an individual or business that uses the system and is interested in opening or operating an online store.

[0700] "Interests and goals" refer to what the user wants to achieve or the project themes they are interested in.

[0701] "Device" refers to a hardware or software configuration for acquiring, transmitting, and analyzing user data.

[0702] "History and personal information" refers to data that shows a user's past activities and profile, including information related to the execution of projects.

[0703] "Analyzing information" refers to the process of applying data analysis and machine learning techniques to identify the optimal payment technologies and resources based on collected data.

[0704] "Payment technologies and resources" refers to the technical means and available systems for conducting online payments.

[0705] "Information network" refers to the network infrastructure and communication technologies necessary to acquire information from external sources.

[0706] A "program interface" refers to protocols and APIs used to interact with external information networks and acquire data.

[0707] "Data collection technology" refers to technologies that automatically acquire necessary information from external sources using web crawling or APIs.

[0708] To implement this invention, it is necessary for a user's device to work in conjunction with a server in the cloud. First, the user uses their device to input their interests and goals regarding the opening of an online store. At the same time, the device also collects the user's past history and personal information.

[0709] Upon receiving this data, the server runs machine learning algorithms to recommend the optimal payment technology and resources. Specifically, the server analyzes the data using libraries such as TensorFlow and scikit-learn. The recommended payment technology includes specific steps to guide users through its implementation process. This allows users to proceed with the implementation efficiently.

[0710] In this process, the server automatically retrieves relevant information from external information networks. This process utilizes APIs and web crawling, employing programmatic interfaces and data collection technologies. This allows the server to reflect the latest information in real time and provide users with optimal advice.

[0711] Furthermore, user progress is tracked in real time by the server, and feedback is provided as needed. This feedback includes suggestions for improvement to the ongoing project and advice for the next steps. For example, it helps resolve issues by providing specific steps to fix error messages pointed out by the user.

[0712] A concrete example of this system would be a user who wants to "open a small online accessories shop." In this case, the server would suggest appropriate payment technologies and clearly outline the implementation procedures for each. It would also provide advice on security and operational optimization after implementation.

[0713] An example of a prompt for a generated AI model would be, "I'm thinking of opening an online accessory shop. Could you recommend an electronic payment system and explain the setup procedure?"

[0714] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0715] Step 1:

[0716] The device receives the user's interests and goals. At the same time, it collects past project history and personal information as input. This data is organized in text format and prepared for transmission to the server.

[0717] Step 2:

[0718] The server receives user data sent from the terminal. Based on this input data, it first analyzes the user's interests and goals. Machine learning algorithms are used for the analysis, such as TensorFlow or scikit-learn, to process the data and extract features. This allows the server to search the database for the most suitable payment technology for the user.

[0719] Step 3:

[0720] The server recommends the most suitable payment technology and resources based on the analysis results. In this recommendation process, the analyzed data is used as input, and the program generates a recommendation list. This list includes technical details and specific implementation instructions, which are then provided to the user.

[0721] Step 4:

[0722] The server retrieves relevant information from external information networks. This step involves a process of collecting the latest information using APIs and web crawling technologies. The retrieved data is then used to create suggestions for the user.

[0723] Step 5:

[0724] The terminal displays to the user a list and instructions regarding optimized payment technologies received from the server. The user then proceeds with setting up the online store based on the provided instructions.

[0725] Step 6:

[0726] The server monitors the user's progress in real time. In this step, the server analyzes the progress based on the user's operation history and input data. Progress data is generated as feedback and provided to the user as guidance for the next step.

[0727] Step 7:

[0728] Users make necessary adjustments and improvements based on feedback from the server. Each feedback includes specific advice and problem-solving methods to support users in efficiently advancing the project.

[0729] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0730] The embodiment of the present invention begins with an initial stage where the user inputs project interests and goals. The terminal provides the user with an interface for inputting project goals, and the user can input their interests and preferences in detail. The terminal also collects the user's past project history and personal data.

[0731] Next, the device sends the collected information to the server. The server uses machine learning algorithms to analyze this information and recommend the most suitable tools and resources for the user's project. For example, if the project is "creating an online course," the server may recommend video editing software and an LMS platform.

[0732] Next, the server gathers up-to-date information related to the project's objectives from the external network. This involves using APIs and web crawling technologies to collect the knowledge and technical documents the user needs. Based on the information collected by the server, it designs the specific execution steps for the project. This includes installation procedures for the software to be used and preparation steps for the necessary materials.

[0733] The generated steps are presented to the user via the device, and the user follows this guide to complete the project. The emotion engine analyzes the user's input, progress, and even emotions, and generates feedback and motivational messages tailored to the project's progress. For example, if the user is feeling stressed about the progress, the emotion engine provides advice and encouraging messages to help them relax.

[0734] Thus, this system has the functionality to comprehensively support the project process from start to finish, while also taking into account the user's emotional state. As a result, users can significantly reduce their workload and achieve their goals more efficiently.

[0735] The following describes the processing flow.

[0736] Step 1:

[0737] The user uses a terminal to input the project's purpose and goals. Specifically, the user provides the system with the project's theme and desired outcomes through a text input interface.

[0738] Step 2:

[0739] The terminal sends the user's input data to the server. At the same time, the terminal also sends past project history and personal data.

[0740] Step 3:

[0741] The server analyzes the information it receives. It then runs machine learning algorithms to recommend the best tools and resources for the user's project. This process identifies the best software and platform to meet the user's needs.

[0742] Step 4:

[0743] The server automatically collects relevant information from the external network. Using APIs and web crawling technologies, it obtains the technical information and latest trend information necessary for the project.

[0744] Step 5:

[0745] Based on the information collected by the server, specific execution procedures are designed. This includes clear guidelines for project progress, such as "step-by-step instructions" and "critical checkpoints."

[0746] Step 6:

[0747] The terminal presents the user with the execution steps received from the server. The user can then proceed with the project by referring to these steps.

[0748] Step 7:

[0749] The device activates an emotion engine to recognize the user's emotions. It analyzes the user's text input and progress to generate data about the user's psychological state.

[0750] Step 8:

[0751] Based on the results of the emotion engine, the server creates feedback and motivational messages tailored to the user's progress. If the user is feeling stressed, this includes suggestions for relaxation.

[0752] Step 9:

[0753] The device displays generated feedback and messages to the user. Based on this, the user can maintain motivation for the project and move on to the next step.

[0754] (Example 2)

[0755] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0756] Traditionally, when users individually planned and implemented projects, it not only required a great deal of time and effort, but also presented problems such as the inability to effectively utilize appropriate information and resources. Furthermore, maintaining motivation and managing emotions during project progress was difficult, potentially leading to a decrease in overall efficiency.

[0757] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0758] In this invention, the server includes means for acquiring the user's interests and goals, means for transmitting the user's history and personal data, means for analyzing the received data and recommending the most suitable tools and resources, means for collecting external information and designing specific execution procedures, and means for monitoring the user's progress and providing feedback that takes into account their emotional state. This allows the user to be efficiently supported from the start to the completion of a project, effectively consolidating necessary resources and knowledge while also receiving emotional support.

[0759] A "user" refers to an individual or organization that uses the system to input information related to project planning and implementation.

[0760] "Interests and goals" refers to information that indicates the content of the project the user wants to achieve and the desired results.

[0761] "History and personal data" refers to information related to a user's past project activities and data concerning the user's unique characteristics.

[0762] "Received data" refers to a collection of information, including interests, goals, history, and personal data, that is sent by the user.

[0763] "Optimal tools and resources" refers to the tools and resources recommended to support the user in carrying out their project.

[0764] "Analysis" refers to the process of examining received data in detail and determining the tools and resources best suited to the user's needs.

[0765] "Recommended methods" refer to the methods and techniques for identifying and suggesting the most suitable tools and resources for the user.

[0766] "External information" refers to data obtained from external sources that contains the latest knowledge and technical information related to the user's project.

[0767] "Execution procedure" refers to the specific steps and methods that users should follow when carrying out a project.

[0768] "Monitoring" refers to continuously observing the progress and work being done on a user's project.

[0769] "Feedback that takes emotional state into account" refers to information that provides encouragement and advice regarding the progress of a project, taking into account the user's emotional state.

[0770] This invention is a system that supports users in achieving their project goals through the cooperation of the user, terminal, and server.

[0771] The user first uses the terminal's interface to input their interests and goals regarding their project. In addition to the entered interests and goals, the terminal also collects the user's past project history and personal data. This information is sent to the server via HTTP or WebSocket, using a secure communication protocol that includes error checking.

[0772] The server performs analysis using machine learning algorithms based on the received data. Python's scikit-learn or similar data processing libraries can be used for this analysis. This analysis identifies the most suitable tools and resources for the user's project.

[0773] Subsequently, the server utilizes APIs and web crawling technologies to collect relevant information from external networks. This process uses technologies such as BeautifulSoup and OpenAI APIs to retrieve data. Based on this information, the server designs specific execution steps, which are then generated in Markdown or other document formats.

[0774] The user is presented with the designed execution procedure via a terminal. As the user progresses through the project according to the procedure, an emotion engine installed on the server monitors the user's emotional state and project progress, generating and providing feedback and encouraging messages as needed. This utilizes natural language processing with advanced text analysis techniques.

[0775] As a concrete example, let's consider a scenario where the user's goal is to "create an online educational course." In this case, the server would recommend candidate video editing software and learning management systems (LMS), collect information on relevant educational methods and materials, and present the user with the necessary steps.

[0776] Examples of prompt statements include the following:

[0777] "Please enter the goals for your new project and tell us the steps needed to achieve them. The following information is available…"

[0778] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0779] Step 1:

[0780] The user uses the terminal's interface to input their project interests and goals. They can also enter specific keywords and expected outcomes. The user's project interests and goals are sent to the terminal as input data. The terminal receives this data and displays a confirmation message to the user to indicate completion of the input.

[0781] Step 2:

[0782] The device collects user interests and goals, as well as the user's past project history and personal data. This data is organized based on the user's ID and sent to the server. Specifically, the device displays a message indicating that it is sending data to the server and sends the data using HTTP or WebSocket.

[0783] Step 3:

[0784] The server receives data sent from the terminal and analyzes it using machine learning algorithms. The analysis applies clustering and recommendation algorithms to identify the most suitable tools and resources for the user's project. As output, a list of appropriate tools and resources is generated and sent to the terminal.

[0785] Step 4:

[0786] The server collects relevant information from external networks using APIs and web crawling technologies. Keywords related to the user's interests and goals are used as input. The server uses BeautifulSoup and OpenAI APIs to collect the latest technical information and materials, analyzes them, and generates foundational data for execution procedures.

[0787] Step 5:

[0788] Based on the collected information, the server designs specific execution steps to help the user achieve their project goals. The procedure manual is created using a document format such as Markdown. The designed procedure manual is then sent to the terminal as output. The server sends a confirmation message to the terminal stating, "Execution steps have been generated."

[0789] Step 6:

[0790] The terminal presents the user with the execution steps sent from the server. The user proceeds with the project by following these steps. The terminal displays each step of the procedure and prompts the user to take the next action accordingly. Specifically, it displays prompts such as, "Are you ready to proceed to the next step?"

[0791] Step 7:

[0792] The server monitors the user's emotional state and progress throughout the project using an emotion engine. Inputs include user progress data and entered emotional states. The server analyzes this data and, if necessary, generates encouraging or feedback messages, which are then presented to the user via the terminal. For example, it might send a message like, "Great job! Progress is on track."

[0793] (Application Example 2)

[0794] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0795] Currently, in project execution, users spend a lot of time selecting necessary information and tools, making it difficult to manage progress and maintain motivation. This is especially true for personal projects within the home, where a lack of adequate support is particularly noticeable, often leading to a decline in individual motivation.

[0796] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0797] In this invention, the server includes means for acquiring user requests, means for transmitting user history information, means for analyzing received data and suggesting optimal equipment and resources, and means for evaluating the user's emotional state during project progress and generating appropriate messages. This enables the user to efficiently manage the project process from start to finish and to maintain high motivation throughout the process.

[0798] "Means for obtaining user requirements" refers to an interface that allows users to input their goals and desired outcomes for a project via digital devices and to accurately receive that information.

[0799] "Means for transmitting user history information" refers to a function for collecting data related to a user's past activities and projects and sending it to a server for analysis.

[0800] "A means of analyzing received data and proposing the optimal equipment and resources" refers to a process that uses algorithms based on collected information to present the tools and resources most suitable for the user's project execution.

[0801] "Means for evaluating the user's emotional state during project progress and generating appropriate messages" refers to a mechanism for analyzing the user's mental state according to the project's progress and providing encouragement or advice as needed.

[0802] The system for implementing this invention mainly consists of three components: a server, a terminal, and a user.

[0803] First, users provide information through an interface using their device to input project goals and interests. This interface is designed to allow users to easily input data. Similarly, user history information is also obtained from the device and sent to the server.

[0804] Next, the server analyzes the received data. This analysis utilizes a machine learning framework using Python (e.g., TensorFlow) to identify and suggest the most suitable equipment and resources for the user's project. Specifically, if a user wants to start a home garden, it will show the necessary tools, materials, and procedures. In this process, APIs and web crawling technologies are used to collect relevant information from external networks.

[0805] Furthermore, the server tracks user progress, assesses user emotional state, and generates messages to maintain project motivation. This process utilizes natural language processing libraries (e.g., NLTK). Specifically, if a user is feeling stressed about their progress, the server automatically provides relaxing advice and encouraging messages.

[0806] Ultimately, the generated procedures and messages are presented to the user through the device. This allows the user to carry out the project efficiently and effectively.

[0807] Examples of prompts include, "Please suggest the steps and tools needed to start a home garden project. Also, please create encouraging messages for the user as they progress." Based on such prompts, the AI ​​model generates appropriate output to meet the user's needs.

[0808] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0809] Step 1:

[0810] Users input project goals and interests using a device. This input is stored on the device as specific project goals and aspirations. This information specifically reflects the user's intentions and expected project outcomes.

[0811] Step 2:

[0812] The terminal collects historical information about past projects along with the user's input data. This includes information about the projects the user has worked on previously and the resources they used. This historical information is used to extract the user's project habits and preferences. The collected data is sent to the server.

[0813] Step 3:

[0814] The server analyzes the received user input data and historical information. This analysis uses a Python machine learning framework (e.g., TensorFlow) and algorithms to identify the most suitable equipment and resources for the user. The input to this analysis is the characteristics and historical data of the project the user is requesting, and the output is a list of suitable tool and resource candidates.

[0815] Step 4:

[0816] The server uses APIs and web crawling technologies to retrieve data from external networks to collect necessary information. This externally retrieved data includes the latest technical information and project-related documents. The input to this process is project-related keywords, and the output is relevant, detailed information.

[0817] Step 5:

[0818] The server designs specific execution procedures based on the analysis results and collected information. This includes installation instructions for the tools to be used and step-by-step guides for proceeding with the project. The input for this step is the analyzed information and acquired external data, and the output is the execution procedure for the user.

[0819] Step 6:

[0820] The terminal presents the user with the generated execution procedure. This procedure is a detailed guide to support the project from start to finish. The user can then complete the project by following this procedure.

[0821] Step 7:

[0822] The server analyzes user progress data and emotional state during project execution and generates appropriate messages. It uses a natural language processing library (e.g., NLTK) to provide encouragement and suggestions tailored to the user's state. The input for this analysis is progress data and user psychological feedback, and the output is a personalized message of encouragement.

[0823] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0824] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0825] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0826] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0827] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0828] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0829] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0830] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0831] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0832] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0833] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0834] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0835] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0836] 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.

[0837] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0838] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0839] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0840] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0841] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0842] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0843] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0844] The following is further disclosed regarding the embodiments described above.

[0845] (Claim 1)

[0846] Means of obtaining user interests and goals,

[0847] Means for transmitting user history and personal data,

[0848] A means of analyzing received data and recommending the most suitable tools and resources,

[0849] A means for collecting relevant information and generating specific execution procedures,

[0850] A means of presenting the generated procedure to the user,

[0851] A means to track user progress and provide feedback,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, characterized in that the recommendation of the tools and resources is performed using a machine learning algorithm.

[0855] (Claim 3)

[0856] The system according to claim 1, characterized in that the information gathering means utilizes APIs and web crawling technologies to automatically acquire data from an external network.

[0857] "Example 1"

[0858] (Claim 1)

[0859] A device for acquiring user requests and objectives,

[0860] A device that transmits user history and personal information,

[0861] A device that analyzes received data and recommends the optimal equipment and resources,

[0862] A device that collects relevant information from external resources and generates specific operating procedures,

[0863] A device that displays the generated procedure to the user,

[0864] A device that tracks the user's progress and provides feedback,

[0865] An information processing system that includes this.

[0866] (Claim 2)

[0867] The information processing system according to claim 1, characterized in that the recommendation of the aforementioned devices and resources is performed using a machine learning algorithm.

[0868] (Claim 3)

[0869] The information processing system according to claim 1, characterized in that the information gathering device utilizes an applied programming interface and data gathering technology that automatically perform the acquisition of information from an external communication network.

[0870] "Application Example 1"

[0871] (Claim 1)

[0872] A device for acquiring user interests and goals,

[0873] A device that transmits user history and personal information,

[0874] A device that analyzes received information and recommends the optimal payment technology and resources,

[0875] A device that collects relevant information and generates specific procedures,

[0876] A device that presents the generated procedure to the user,

[0877] A device that tracks the user's progress and provides evaluations,

[0878] A system that includes this.

[0879] (Claim 2)

[0880] The system according to claim 1, characterized in that the payment technology and resource recommendations are performed by a machine learning algorithm.

[0881] (Claim 3)

[0882] The system according to claim 1, characterized in that the information gathering device utilizes a program interface and data gathering technology that automatically acquire data from an external information network.

[0883] "Example 2 of combining an emotion engine"

[0884] (Claim 1)

[0885] Means of obtaining user interests and goals,

[0886] Means for transmitting user history and personal data,

[0887] A means of analyzing received data and recommending the most suitable tools and resources,

[0888] A means of collecting external information and designing specific execution procedures,

[0889] A means of presenting the designed procedure to the user,

[0890] A means of monitoring user progress and providing feedback that takes emotional state into account,

[0891] A system that includes this.

[0892] (Claim 2)

[0893] The system according to claim 1, characterized in that the recommendation of the tools and resources is performed using a machine learning algorithm.

[0894] (Claim 3)

[0895] The system according to claim 1, characterized in that the information gathering means uses data acquisition technology and web crawling technology for automatically collecting data from an external network.

[0896] "Application example 2 when combining with an emotional engine"

[0897] (Claim 1)

[0898] Means for obtaining user requirements,

[0899] A means of transmitting user history information,

[0900] A means of analyzing received data and proposing the optimal equipment and resources,

[0901] A means of collecting relevant information and generating specific implementation methods,

[0902] A means of presenting the generated method to the user,

[0903] A means to track user progress and provide feedback,

[0904] A means of evaluating the user's emotional state during project progress and generating appropriate messages,

[0905] A system that includes this.

[0906] (Claim 2)

[0907] The system according to claim 1, characterized in that it generates messages aimed at improving motivation during project progress.

[0908] (Claim 3)

[0909] The system according to claim 1, characterized in that the information gathering means supports the progress of the project through an interface. [Explanation of Symbols]

[0910] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A device for acquiring user interests and goals, A device that transmits user history and personal information, A device that analyzes received information and recommends the optimal payment technology and resources, A device that collects relevant information and generates specific procedures, A device that presents the generated procedure to the user, A device that tracks the user's progress and provides evaluations, A system that includes this.

2. The system according to claim 1, characterized in that the payment technology and resource recommendations are performed by a machine learning algorithm.

3. The system according to claim 1, characterized in that the information gathering device utilizes a program interface and data gathering technology that automatically acquire data from an external information network.

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