System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving prompts provided by experts; means for storing and categorizing the prompts in a database; means for receiving user search queries and retrieving relevant prompts from the database; means for automatically generating applications based on the retrieved prompts; means for providing the generated applications to users; means for receiving and storing user feedback and ratings in the database and updating rating scores of the prompts; and means for calculating and paying expert rewards based on ratings and usage.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In recent years, demand for applications utilizing artificial intelligence has been increasing. However, creating accurate and effective prompts has proven difficult, especially in fields requiring advanced expertise. This problem makes it difficult for average users to obtain high-quality results. Furthermore, because the accuracy of the output results depends on the quality of the prompts, there can be significant differences even for requests with the same purpose. Furthermore, the lack of an appropriate reward system for prompts created by experts makes it difficult to attract high-quality prompts. The present invention aims to solve these problems. [Means for solving the problem]

[0005] The present invention provides a means for receiving prompts provided by experts, storing them in a database, and classifying them by category. It also includes a means for receiving a user's search query, searching for and retrieving relevant prompts, and providing them to the user. It also includes a means for automatically generating an application based on the retrieved prompts, and a means for providing the generated application to the user in a downloadable format. By providing a means for receiving feedback and ratings from users, storing them in a database, and updating the rating score, it becomes possible to accumulate high-quality prompts. It also includes a means for calculating and paying a fee to experts based on the rating and number of uses, thereby improving the motivation of prompt providers.

[0006] Furthermore, the present invention provides a means for receiving user authentication information and performing authentication, thereby enabling safe and appropriate service provision. User usability is improved by displaying a home screen if authentication is successful and an error message if authentication fails. Furthermore, the automatically generated application generates program code for specific tasks such as data analysis, natural language processing, and machine learning, which can be packaged and provided to users in a downloadable format. This makes it possible to obtain high-quality results without specialized knowledge, significantly improving user convenience.

[0007] An "expert" is someone who has advanced knowledge and experience in a particular field and creates and provides prompts related to that field.

[0008] A "prompt" is a piece of text or a command that instructs a computer program or application to operate or perform a specific task.

[0009] A "database" refers to a management system that structures and stores data and enables efficient operations such as searching and updating.

[0010] "User" refers to an individual or organization that uses the System to utilize an application generated from a prompt.

[0011] A "search query" refers to a string of characters or keywords that a user enters to search for specific information.

[0012] "Application" refers to a software program with a specific purpose that runs on a computer.

[0013] "Feedback" refers to information that provides users with evaluations, opinions, and suggestions for improvement regarding the application or prompt they used.

[0014] An "evaluation score" is a numerical representation of user evaluations and serves as an indicator of the quality of prompts and applications.

[0015] "Reward" refers to the compensation paid to experts for prompts provided by them based on their evaluation and frequency of use.

[0016] "Authentication information" refers to information such as ID and password used by a user to log in to a system.

[0017] "Authentication" refers to the process of verifying the entered authentication information and confirming the user's identity.

[0018] "Downloadable format" refers to a file format that allows users to obtain an application via the Internet and run it on their own computer, etc.

[0019] "Data analysis" refers to the process of collecting data, analyzing it using statistical methods and algorithms, and extracting useful information.

[0020] "Natural language processing" refers to the technology that allows computers to understand and process human language.

[0021] "Machine learning" refers to algorithms and techniques that allow computers to learn from data and improve through experience. [Brief explanation of the drawings]

[0022] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0023] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0024] First, the terms used in the following description will be explained.

[0025] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0026] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0027] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0028] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0029] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0030] [First embodiment]

[0031] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0032] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0033] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0034] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0035] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0036] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0037] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0038] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0039] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0040] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0041] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0042] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0043] The present invention relates to a platform that automatically generates an application based on a prompt created by an expert and provides the application to a user. The system of the present invention is implemented with the following configuration.

[0044] Register an expert prompt

[0045] Server Processing

[0046] Experts use a web interface to provide prompts based on their knowledge and experience. The server receives the prompts and stores them in a database. The prompts are categorized according to specific fields or purposes. For example, prompts for data analysis and prompts for natural language processing are stored in separate categories.

[0047] User authentication and search prompts

[0048] Terminal handling

[0049] A user accesses the system using their own terminal and enters their ID and password on the login screen. The terminal sends this authentication information to the server. The server verifies the authentication information, and if authentication is successful, the home screen is displayed to the user. The home screen provides a search interface where users can search for prompts by entering categories or keywords.

[0050] Automatically generate applications based on prompts

[0051] Server Processing

[0052] The server receives the prompt selected by the user and begins the process of automatically generating an application based on that prompt. For example, if a "Stock Analysis Prompt" is selected, the server first identifies the required data source (e.g., stock price data API) and analysis method. Using this information, the server automatically generates a Python script and packages the script.

[0053] Application provision and use

[0054] Terminal handling

[0055] The server generates an application and provides it to the user as a download link. The user can click the link to download the application and run it on their computer. For example, they can open a downloaded Python script in Jupyter Notebook to analyze stock price data.

[0056] Feedback and Ratings

[0057] User Action

[0058] After using the application, users can provide their evaluation and feedback to the system. For example, if the application performs as expected, they can give it a high rating, and if there is room for improvement, they can leave a comment.

[0059] Server Processing

[0060] The server receives user feedback and ratings and stores them in a database. At the same time, it dynamically updates the prompt rating scores and calculates rewards for related experts. Experts who provide highly rated or frequently used prompts are rewarded.

[0061] Reward System

[0062] Server Processing

[0063] The server periodically compiles the evaluation score and number of times each prompt is used, and calculates the reward for the expert based on that data. For example, the server compiles the data at the end of the month, and pays rewards to experts who provide prompts with a rating of 4 or higher and that are used more than a certain number of times. The rewards are given as bank transfers or points.

[0064] This system allows experts to receive fair compensation and enables users to easily use high-quality applications. The present invention brings great benefits to both users and experts and promotes the accumulation of high-quality prompts.

[0065] The processing flow will be explained below.

[0066] Program processing steps and detailed explanation

[0067] Step 1:

[0068] Experts register prompts

[0069] Using the web interface, the expert inputs a prompt based on their own knowledge and experience, then presses the send button, and the terminal sends the input prompt to the server.

[0070] Step 2:

[0071] The server receives and saves the prompt

[0072] The server receives prompts sent by experts, which are stored in a database and classified into specific categories (e.g., data analysis, natural language processing).

[0073] Step 3:

[0074] A user logs into the system

[0075] The user accesses the system from a terminal and enters their ID and password on the login screen. The terminal then sends this authentication information to the server.

[0076] Step 4:

[0077] The server authenticates the user

[0078] The server checks the received authentication information against a database and displays the home screen if authentication is successful, or returns an error message if authentication fails.

[0079] Step 5:

[0080] The user searches for the prompt

[0081] After logging in, the user uses the search interface to search for prompts by category or keyword. The terminal sends the search query to the server.

[0082] Step 6:

[0083] The server retrieves and returns the prompt

[0084] The server searches its database based on the received search query and returns a list of matching prompts to the terminal.

[0085] Step 7:

[0086] The user selects a prompt

[0087] The user selects the appropriate prompt from the list of returned prompts, and the terminal then sends the information to the server.

[0088] Step 8:

[0089] The server automatically generates the application

[0090] The server analyzes the selected prompts, identifies the required data sources and analysis methods, and automatically generates program code (e.g., Python scripts) based on this information.

[0091] Step 9:

[0092] The server packages and serves the application

[0093] The server packages the automatically generated program code and generates a download link for the user, which is sent back to the device and displayed to the user.

[0094] Step 10:

[0095] Users download and use the application

[0096] Users click the provided link to download the application and run it on their device, for example, to open a Python script in Jupyter Notebook and perform data analysis.

[0097] Step 11:

[0098] Users provide feedback

[0099] After using an application, users input their evaluation and feedback into the system and submit it. The terminal then sends this to the server.

[0100] Step 12:

[0101] The server stores and updates feedback and ratings

[0102] The server stores the received feedback and ratings in a database and dynamically updates the rating scores of the prompts.

[0103] Step 13:

[0104] The server calculates the reward for the expert.

[0105] The server calculates the reward for the expert based on the evaluation score and number of times the prompt is used. The reward is notified to the expert and paid periodically.

[0106] These processing steps enable the automatic generation of high-quality applications based on prompts provided by experts, and the provision of these applications to users. This system benefits both users and experts, and promotes the accumulation of high-quality prompts.

[0107] Example 1

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

[0109] Currently, when trying to generate an application using prompts created by experts, the process is often manual and time-consuming and labor-intensive. Furthermore, the quality and feedback management of the generated application is left to the experts, resulting in a lack of uniformity. Furthermore, the compensation system for experts is unclear, which may lead to inadequate compensation. There is a need to resolve these issues and provide an efficient and fair system for both experts and users.

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

[0111] In this invention, the server includes: means for receiving prompts provided by experts; means for storing the prompts in a data storage device and classifying them by category; means for receiving user search queries and searching for and retrieving relevant prompts from the database; means for automatically generating software based on the retrieved prompts; means for providing the generated software to the user; means for receiving feedback and ratings from users, storing them in a data storage device, and updating the prompt ratings; and means for calculating and paying remuneration to experts based on the ratings and number of uses. This enables efficient management of prompts provided by experts and the provision of high-quality applications to users. Furthermore, since experts can be paid appropriately based on the feedback and ratings, a fair remuneration system can be established.

[0112] An "expert" is someone who has advanced knowledge and experience in a particular field and can create prompts related to that field.

[0113] A "prompt" is a text instruction written by an expert based on a specific task or objective, which the software then automatically generates.

[0114] A "data storage device" is a device for permanently storing information, including databases.

[0115] A "user" is a person who uses the system to obtain a specific application and execute it to achieve a purpose.

[0116] A "search query" is a string of characters that a user enters to search for required information from the database within the system.

[0117] "Software" means a collection of program code and associated files that are automatically generated based on prompts.

[0118] "Feedback" refers to the evaluations and opinions users provide after using an application, which are used to improve the system.

[0119] "Evaluation" refers to a quantitative scoring of the application used by the user, and the system compiles this to calculate the compensation paid to the expert.

[0120] "Reward" refers to compensation such as money or points paid to an expert based on the usage record and evaluation score of the prompt provided by the expert.

[0121] The present invention is a platform that automatically generates an application based on a prompt created by an expert and provides the application to a user, and is implemented with the following configuration.

[0122] The system consists of three main components: a server, a terminal, and a user. Here we will explain in detail how each component works and how it implements the invention.

[0123] Register an expert prompt

[0124] Server Processing

[0125] Experts use a web browser (e.g., a general-purpose web browser) to enter prompts through a web interface. The server receives the input data as HTTP requests and stores the prompts in a database (e.g., a general-purpose database management system). The prompts are then categorized according to specific domains or purposes.

[0126] User authentication and search prompts

[0127] Terminal handling

[0128] The user uses a web browser (e.g., a general-purpose web browser) on a device (e.g., a general-purpose personal computer) to enter their ID and password on the login screen. The device sends this information to the server, which then checks the authentication information against a database. If authentication is successful, the server provides the user with the home screen.

[0129] Automatically generate applications based on prompts

[0130] Server Processing

[0131] The server receives the prompt selected by the user and automatically generates software (e.g., a Python script) based on the prompt. For example, if a "Stock Analysis Prompt" is selected, the server identifies the stock price data API and analysis method, and generates a Python script based on that. This script adds lines to import necessary libraries (e.g., general-purpose libraries).

[0132] Application provision and use

[0133] Terminal handling

[0134] The application generated by the server is provided to the user's device as a download link. The user clicks the link to download the application and run it on their computer. For example, the user loads the "Stock Price Forecast.ipynb" file and uses Jupyter Notebook to perform analysis.

[0135] Feedback and Ratings

[0136] User Action

[0137] After using an application, users can rate and submit their feedback to the server. For example, if the application performs as expected, users can leave a comment such as "It's easy to use, but it's slow."

[0138] Server Processing

[0139] The server stores the received ratings and feedback in a database, updates the prompt's rating score, calculates the reward to the expert based on the rating and number of uses, and pays the reward in an appropriate way (e.g., bank transfer or points).

[0140] Examples of concrete examples and prompts

[0141] Examples:

[0142] Expert-submitted prompt: "Generate code to predict stock prices using Python"

[0143] User: Search for "Stock price prediction application"

[0144] Example of code generated by the server (specific code is not shown in this example)

[0145] Example prompt sentence:

[0146] "Generate prediction code using an LSTM model using AAPL stock price data in Python."

[0147] "Create a natural language processing prompt."

[0148] This system allows experts to receive fair compensation and enables users to easily use high-quality applications. In addition, since experts can be paid fairly based on feedback and evaluation, it is possible to build a fair compensation system.

[0149] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0150] Step 1:

[0151] Server Processing

[0152] The server receives prompts provided by experts, who use a web browser to enter the prompts into a web interface and submit them. The server receives the HTTP requests, stores them in a database, and categorizes them into categories.

[0153] Input: Expert-entered prompt and category information.

[0154] Output: Prompt and category information stored in a database.

[0155] Specific operation: The server saves the received prompt in text format in the database and adds category information using an SQL insert statement.

[0156] Step 2:

[0157] Terminal handling

[0158] The user accesses the login screen using the device's web browser and enters their ID and password, which is then sent to the server.

[0159] Input: The ID and password entered by the user.

[0160] Output: The authentication information sent to the server.

[0161] Specific operation: The user enters "user@example.com" and "password123" into the login form and clicks the login button.

[0162] Step 3:

[0163] Server Processing

[0164] The server checks the credentials against a database to verify them, and if successful presents the user with a home screen, otherwise it displays an error message.

[0165] Input: ID and password sent from the device.

[0166] Output: HTML of the home screen if authentication is successful, an error message if authentication fails.

[0167] What happens: The server uses an SQL query to check the authentication information in the database and generates an HTML response based on the authentication result.

[0168] Step 4:

[0169] Terminal handling

[0170] The home screen search interface allows users to enter keywords to search for a specific prompt, such as "stock price prediction."

[0171] Input: Keywords entered by the user into the search box on the home screen.

[0172] Output: The HTTP request with the keyword sent to the server.

[0173] Specific behavior: The user enters "stock price prediction" in the search box and clicks the search button.

[0174] Step 5:

[0175] Server Processing

[0176] The server searches the database based on the received search query, generates a list containing relevant prompts, and returns it to the user.

[0177] Input: User's search query (e.g. "stock price predictions").

[0178] Output: A list of the corresponding prompts in HTML format.

[0179] Specific operation: The server searches the database using an SQL query, generates the search results as an HTML list, and sends it to the user.

[0180] Step 6:

[0181] Terminal handling

[0182] The user selects a particular prompt from the search results and sends a request to the server.

[0183] Input: The prompt selected by the user.

[0184] Output: A prompt selection request is sent to the server.

[0185] Specific behavior: The user clicks on "Stock Price Prediction Prompt" that appears in the search results.

[0186] Step 7:

[0187] Server Processing

[0188] The server generates the application based on the selected prompts, for example by automatically generating a Python script to identify the required data sources and analysis methods.

[0189] Input: The user's prompt selection information.

[0190] Output: The generated Python script.

[0191] What it does: The server generates Python code based on the prompt and associated template, adding lines to import the necessary libraries.

[0192] Step 8:

[0193] Server Processing

[0194] Package the generated script and generate a URL from which the user can download it.

[0195] Input: The generated Python script.

[0196] Output: Download link.

[0197] Specific operation: The server compresses the script using a ZIP compression tool, generates a download URL, and provides it to the user.

[0198] Step 9:

[0199] Terminal handling

[0200] The user clicks on the download link to download the application and run it on their computer.

[0201] Input: Download link.

[0202] Output: The application downloaded to the device.

[0203] What happens: The user clicks on the provided link, downloads the ZIP file, unzips it, and opens it in Jupyter Notebook.

[0204] Step 10:

[0205] User Action

[0206] After using the application, the user inputs the evaluation and feedback and transmits them to the server.

[0207] Input: User ratings and feedback.

[0208] Output: Feedback information sent to the server.

[0209] Specific behavior: A user enters a score of "4" and a comment "Easy to use, but slow" in the evaluation form and clicks the submit button.

[0210] Step 11:

[0211] Server Processing

[0212] The server stores the received ratings and feedback in a database, updates the prompt's rating score, and calculates and pays the expert a reward based on the rating and number of uses.

[0213] Input: User ratings and feedback.

[0214] Output: Updated prompt evaluation scores and reward data for the expert.

[0215] Specific operation: The server updates the evaluation score, calculates the reward, and transfers the reward to the expert or awards points.

[0216] Through these steps, we can efficiently manage prompts based on the knowledge and experience of experts, provide high-quality applications to users, and properly reflect feedback and evaluations. This system will realize a fair and efficient application creation and reward system.

[0217] (Application example 1)

[0218] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0219] Managing the operation of robots in factories requires specialized knowledge and advanced skills. Furthermore, generating programs for rapid and effective response is extremely difficult, posing a major challenge for many companies. Current technology lacks a general-purpose means for easily generating and utilizing such programs. Therefore, in order to improve factory operational efficiency and rapidly put specialized knowledge to practical use, there is a need for an easy way to generate applications specialized for factory robots and to incorporate expert knowledge as feedback on an ongoing basis.

[0220] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0221] In this invention, the server includes means for receiving prompts provided by experts, means for saving the prompts in a database and categorizing them, means for receiving user search queries and searching for and retrieving relevant prompts from the database, means for automatically generating an application based on the retrieved prompts, means for providing the generated application to users, means for generating a program including operation and management of factory robots, means for providing the generated program to smartphones and tablet devices, means for receiving feedback and ratings from users, saving the feedback and ratings in a database and updating the evaluation scores of the prompts, and means for calculating and paying remuneration to experts based on the ratings and number of uses. This enables effective operation and management of factory robots and smooth practical application of expert knowledge.

[0222] An "expert" is someone who has advanced knowledge and experience in a particular field and can provide prompts.

[0223] A "prompt" is text information that provides guidelines or instructions for a specific task or purpose.

[0224] A "database" is a collection of information that centrally stores and manages multiple data sets and allows them to be searched and retrieved as needed.

[0225] A "category" is a group or segment for classifying and managing related prompts or data.

[0226] A "user" is a person who uses the system to perform prompt searches and generate applications.

[0227] A "search query" is a question or keyword that a user enters into a system to obtain specific information.

[0228] An "application" is a program that is automatically generated based on a prompt to perform a specific task.

[0229] A "factory robot" is a mechanical device used to automate and streamline work and operations in a factory.

[0230] A "smartphone" is a mobile device equipped with advanced communication functions and capable of running a wide variety of applications.

[0231] A "tablet device" is a mobile information terminal that has advanced communication functions similar to a smartphone and a larger screen.

[0232] "Feedback" is the act of providing the system with impressions and opinions after using an application.

[0233] The "rating score" is a numerical representation of the quality of a prompt or application based on user feedback.

[0234] "Remuneration" refers to compensation paid based on the usage and evaluation of the prompts provided by the expert.

[0235] "Program code" is text that contains a set of instructions for a computer to execute.

[0236] "Packaging" refers to the process of compiling the generated program code into a format that users can easily download and run.

[0237] "Route design" refers to a plan to optimize the movement routes and work sequences of robots within a factory.

[0238] The present invention is a system that automatically generates applications specialized for managing robot operations in factories by utilizing prompts based on the knowledge and experience of experts, and provides the applications to users. A specific embodiment of the present invention will be described below.

[0239] Overall system overview

[0240] This system is primarily composed of a server, user terminals, and smartphones or tablet terminals. The server is responsible for managing and processing various data, while the user terminal is responsible for communicating with the system and using applications.

[0241] Expert prompts provided and stored in a database

[0242] Experts provide prompts through a web interface. The server receives the prompts and efficiently stores them in a database. The prompts are categorized into categories such as "Data Analysis," "Natural Language Processing," and "Machine Learning," each of which is managed with its own index.

[0243] User authentication and search prompts

[0244] Users access the system from their devices and authenticate by entering their ID and password. If authentication is successful, the home screen is displayed. From here, users can enter a search query and search for and retrieve the corresponding prompt from the database. At this stage, the server analyzes the search query and provides the most appropriate prompt.

[0245] Automatically generate applications based on prompts

[0246] Based on the prompts selected by the user, the server automatically generates an application to support the operation and management of factory robots. This process involves generating program code using knowledge provided by experts to create a robot operation program that includes optimal control algorithms and flow line designs.

[0247] Application submission and feedback system

[0248] The generated application is packaged in a format that can be used on smartphones and tablets and provided as a download link. Users can click the link to download the application and run it on their device. After using the application, users send their feedback and rating to the server, which stores it in a database and updates the prompt's rating score.

[0249] Reward system for experts

[0250] The server periodically compiles the evaluation score and frequency of use of each prompt and calculates the reward for the expert based on that data. For example, an expert who provides a prompt with a high evaluation and frequency of use will be paid an appropriate reward.

[0251] Examples of concrete examples and prompts

[0252] Examples:

[0253] Experts design control algorithms for the effective operation of automated transport robots in factories and register these as prompts in the system. Users can log in using their smartphone app, select the appropriate prompt, and apply the automatically generated control program to the robot based on the prompt.

[0254] Example prompt sentence:

[0255] "An algorithm for designing efficient movement paths for automated transport robots in factories. Required fields: initial position of the robot, target position, and obstacle placement."

[0256] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0257] Step 1: Expert Prompts

[0258] Experts enter prompts through a web interface, which include guidelines and instructions for specific tasks. The server receives the prompts and stores them in a prompt database, where they are indexed and categorized by categories such as "data analysis," "natural language processing," and "machine learning."

[0259] Input: Prompt from an expert

[0260] Output: Prompts stored in the database

[0261] Step 2: User Authentication

[0262] The user accesses the system from their device and enters their ID and password for authentication. The device sends the authentication information to the server, which then verifies it. If authentication is successful, the home screen is displayed and the user can proceed to the next step.

[0263] Input: User ID, Password

[0264] Output: Authentication success or failure, home screen

[0265] Step 3: Find the prompt

[0266] A user enters a search query on the home screen and searches the prompt database. The server analyzes the search query and lists relevant prompts to provide to the user.

[0267] Input: Search query

[0268] Output: A list of applicable prompts

[0269] Step 4: Auto-generating the application

[0270] When a user selects a prompt from the search results, the server automatically generates an application based on that prompt. Specifically, appropriate program code is generated based on the prompt content. This program code includes control algorithms for factory robots and flow line designs.

[0271] Input:PromptSelect

[0272] Output: Auto-generated program code

[0273] Step 5: Serving the Application

[0274] The generated application is packaged in a format that can be used on smartphones and tablets and provided to the user as a download link, which the user can click to download the application and run it on their device.

[0275] Input: Generated program code

[0276] Output: Download link, application installed on device

[0277] Step 6: Gather feedback

[0278] After using the application, the user provides feedback and a rating based on the quality and usefulness of the application. The server receives this feedback and updates the rating score in the prompt database.

[0279] Input: Feedback, Rating

[0280] Output: Updated rating score

[0281] Step 7: Calculate the expert's fee

[0282] The server periodically compiles the evaluation score and frequency of use of each prompt, and calculates the reward for the expert based on that data. This reward is paid to the expert who provides the prompt with the highest evaluation and frequency of use.

[0283] Input: Evaluation score, number of uses

[0284] Output: Calculated reward, payment to the expert

[0285] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0286] The present invention improves the user experience by combining a platform that automatically generates applications based on prompts created by experts and provides those applications to users with an emotion engine that recognizes the user's emotions. The system of the present invention is implemented with the following configuration.

[0287] Register an expert prompt

[0288] Server Processing

[0289] Experts provide prompts based on their knowledge and experience through a web interface. The server receives the prompts and stores them in a database. The prompts are categorized according to specific fields or purposes. For example, prompts for data analysis and prompts for natural language processing are stored in separate categories.

[0290] User authentication and search prompts

[0291] Terminal handling

[0292] A user accesses the system using their own terminal and enters their ID and password on the login screen. The terminal sends this authentication information to the server. The server verifies the authentication information, and if authentication is successful, the user is shown the home screen. The home screen provides a search interface where users can search for prompts by entering categories or keywords.

[0293] Automatically generate applications based on prompts

[0294] Server Processing

[0295] The server receives the prompt selected by the user and begins the process of automatically generating an application based on that prompt. For example, if a "Stock Analysis Prompt" is selected, the server first identifies the required data source (e.g., stock price data API) and analysis method. Using this information, the server automatically generates a Python script and packages the script.

[0296] Application provision and use

[0297] Terminal handling

[0298] The server generates an application and provides it to the user as a download link. The user can click the link to download the application and run it on their computer. For example, they can open a downloaded Python script in Jupyter Notebook to analyze stock price data.

[0299] Feedback and Emotion Recognition

[0300] User Action

[0301] After using the application, users enter and submit their evaluations and feedback to the system. The emotion engine analyzes the user's emotions in real time using data such as text input, voice, and facial expressions. For example, when a user enters a text comment, the system analyzes the user's writing style and tone to infer their emotional state.

[0302] Server Processing

[0303] The server receives information from the emotion engine and evaluates the credibility of the feedback. It also adjusts the prompt's rating score based on the feedback and the user's emotional state and stores it in a database. For example, if the user expresses positive emotions, the rating will be set higher.

[0304] Reward System

[0305] Server Processing

[0306] The server periodically compiles the evaluation score and number of times each prompt is used, and calculates the reward for the expert based on that data. The reward is periodically notified to the expert and paid. For example, the data is compiled at the end of the month, and rewards are paid to experts who provide prompts with a rating of 4 or higher and that are used more than a certain number of times. This reward is awarded by bank transfer or as points.

[0307] Utilizing the Emotion Engine

[0308] Server Processing

[0309] The emotion engine is also utilized when the user selects a prompt. The server analyzes the user's emotional state and recommends prompts that suit the user's emotions. For example, if the user is feeling stressed, it can recommend a task that will have a relaxing effect, improving the user experience.

[0310] In this way, the system of the present invention not only automatically generates high-quality applications based on prompts provided by experts and provides them to users, but also improves the user experience by recognizing the user's emotions using an emotion engine and correcting the evaluation score and recommending prompts based on those emotions. This system brings great benefits to both users and experts and promotes the accumulation of high-quality prompts.

[0311] The processing flow will be explained below.

[0312] Program processing steps and detailed explanation

[0313] Step 1:

[0314] Experts register prompts

[0315] Using the web interface, the expert inputs a prompt based on their own knowledge and experience, then presses the send button, and the terminal sends the input prompt to the server.

[0316] Step 2:

[0317] The server receives and saves the prompt

[0318] The server receives prompts sent by experts, which are stored in a database and classified into specific categories (e.g., data analysis, natural language processing).

[0319] Step 3:

[0320] A user logs into the system

[0321] The user accesses the system from a terminal and enters their ID and password on the login screen. The terminal then sends this authentication information to the server.

[0322] Step 4:

[0323] The server authenticates the user

[0324] The server checks the received authentication information against a database and displays the home screen if authentication is successful, or returns an error message if authentication fails.

[0325] Step 5:

[0326] The user searches for the prompt

[0327] After logging in, the user uses the search interface to search for prompts by category or keyword. The terminal sends the search query to the server.

[0328] Step 6:

[0329] The server retrieves and returns the prompt

[0330] The server searches its database based on the received search query and returns a list of matching prompts to the terminal.

[0331] Step 7:

[0332] The user selects a prompt

[0333] The user selects the appropriate prompt from the list of returned prompts, and the terminal then sends the information to the server.

[0334] Step 8:

[0335] The server automatically generates the application

[0336] The server analyzes the selected prompts, identifies the required data sources and analysis methods, and automatically generates program code (e.g., Python scripts) based on this information.

[0337] Step 9:

[0338] The server packages and serves the application

[0339] The server packages the automatically generated program code and generates a download link for the user, which is sent back to the device and displayed to the user.

[0340] Step 10:

[0341] Users download and use the application

[0342] Users click the provided link to download the application and run it on their device, for example, to open a Python script in Jupyter Notebook and perform data analysis.

[0343] Step 11:

[0344] Users provide feedback

[0345] After using the application, users enter their evaluation and feedback into the system and submit it. The emotion engine also analyzes the user's text, voice, facial expressions, and other data. The device then sends this information to the server.

[0346] Step 12:

[0347] The server stores and updates feedback and ratings

[0348] The server receives information from the emotion engine, evaluates the credibility of the feedback based on it, and adjusts the prompt evaluation score based on the feedback content and emotional state, and stores the correct score in a database.

[0349] Step 13:

[0350] The server uses an emotion engine to recommend prompts

[0351] The server uses an emotion engine to analyze the user's emotional state in real time and recommends appropriate prompts to the user, for example, recommending a relaxing task to a user who is feeling stressed.

[0352] Step 14:

[0353] The server calculates the reward for the expert.

[0354] The server periodically compiles the evaluation score and the number of times each prompt is used, and calculates the reward for the expert based on that data. The reward is then notified to the expert and paid periodically.

[0355] These processing steps enable the automatic generation of high-quality applications based on prompts provided by experts and the provision of these applications to users. This system benefits both users and experts and promotes the accumulation of high-quality prompts. In addition, the use of an emotion engine makes it possible to provide a more personalized experience according to the user's emotional state.

[0356] Example 2

[0357] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0358] Previous systems were unable to consider the user's emotional state when automatically generating applications based on prompts provided by experts. This limited the improvement of the user experience, and the prompt evaluation and recommendation process relied on subjective evaluation. Furthermore, the reliability of the feedback could be reduced, making it difficult to evaluate the quality of the prompts.

[0359] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0360] In this invention, the server includes: means for receiving prompts provided by an expert; means for saving the prompts in a database and categorizing them; means for receiving a user's search query and searching for and retrieving relevant prompts from the database; means for automatically generating an application based on the retrieved prompts; means for providing the generated application to the user; means for receiving feedback and ratings from the user and saving them in a database and updating the prompt rating score; means for calculating and paying a fee to the expert based on the rating and the number of times the prompts are used; and means for analyzing the user's emotional state, evaluating the credibility of the feedback, and recommending prompts. This allows prompts to be evaluated and recommended based on the user's emotional state, improving the user experience and the quality of prompts through more credible feedback.

[0361] An "expert" is someone who has knowledge and experience in a particular field or purpose and can provide prompts based on that knowledge.

[0362] A "prompt" is text or data containing information or instructions necessary for the automatic generation of an application for a specific domain or purpose.

[0363] A "database" is a system for systematically storing and managing multiple data, and is used to store prompts and feedback.

[0364] "User" means a person who uses the system to search and select prompts and use the application.

[0365] "Emotional state" refers to a user's emotional or mood state, and is determined using text and voice analysis.

[0366] "Feedback" means information, including ratings and comments, provided by a User after using an Application.

[0367] The "evaluation score" is a numerical representation of the quality of the prompt or application based on feedback.

[0368] "Rewards" are monetary or point-based compensation provided to experts who contribute to the system.

[0369] "Application" means software or a program that is automatically generated based on prompts.

[0370] An "emotion engine" is an algorithm or system for analyzing a user's emotional state.

[0371] A "search query" is a keyword or condition that a user enters to search for prompts in a database.

[0372] "Auto-generation" refers to the process by which the system automatically generates an application or program based on prompts.

[0373] "Packaging" refers to the process of assembling the generated program code into an executable format.

[0374] "Credibility" refers to the degree to which the feedback or rating provided is accurate and trustworthy.

[0375] "Recommendation" refers to the act of suggesting appropriate prompts or applications based on the user's emotional state and past behavior.

[0376] The present invention relates to a platform for automatically generating applications based on prompts created by experts and providing those applications to users. This system is characterized by improving the user experience by combining an emotion engine.

[0377] Register an expert prompt

[0378] Server Processing

[0379] Experts enter prompts based on their knowledge and experience through a web interface, and the server receives the prompts, categorizes them, and stores them in a database, such as "prompts for data analysis" and "prompts for natural language processing."

[0380] As a specific example, an expert creates a "stock price prediction model" and registers the prompt in the system.

[0381] User authentication and search prompts

[0382] Terminal handling

[0383] A user accesses the system using their own terminal and enters their ID and password on the login screen. The terminal sends this authentication information to the server. The server verifies the authentication information, and if authentication is successful, the user is shown the home screen. The home screen has a search interface, and users can search for prompts by entering categories or keywords.

[0384] As a concrete example, consider a user who logs in and searches for "stock price predictions." The user enters the search keywords and gets relevant prompts.

[0385] Automatically generate applications based on prompts

[0386] Server Processing

[0387] Once the server receives the prompt selected by the user, it starts the process of automatically generating an application based on that prompt. For example, if a "Stock Analysis Prompt" is selected, the server will identify the required data source (e.g., stock data API) and analysis method, and generate Python scripts and JavaScript code.

[0388] As a specific example, an application is generated based on the prompt, "Generate a Python script for stock price prediction. The data source used will be the Yahoo Finance API, and the analysis method will be the machine learning random forest model."

[0389] Application provision and use

[0390] Terminal handling

[0391] The server generates an application and provides it to the user as a download link. The user can click the link to download the application and run it on their computer. For example, they can open a downloaded Python script in Jupyter Notebook to analyze stock price data.

[0392] Feedback and Emotion Recognition

[0393] User Action

[0394] After using the application, users enter their evaluation and feedback into the system and submit it. The emotion engine analyzes the user's emotions in real time using data such as text input, voice, and facial expressions. For example, when a user enters a text comment, the system analyzes the user's writing style and tone to infer their emotional state.

[0395] Server Processing

[0396] The server receives information from the emotion engine and evaluates the credibility of the feedback. It also adjusts the prompt's rating score based on the feedback and the user's emotional state and stores it in a database. For example, if the user expresses positive emotions, the rating will be set higher.

[0397] Reward System

[0398] Server Processing

[0399] The server periodically compiles the evaluation score and number of times each prompt is used, and calculates the reward for the expert based on that data. The reward is periodically notified to the expert and paid. For example, the data is compiled at the end of the month, and rewards are paid to experts who provide prompts with a rating of 4 or higher and that are used more than a certain number of times. This reward is awarded by bank transfer or as points.

[0400] Utilizing the Emotion Engine

[0401] Server Processing

[0402] The emotion engine is also utilized when the user selects a prompt. The server analyzes the user's emotional state and recommends prompts that suit the user's emotions. For example, if the user is feeling stressed, it can recommend a task that will have a relaxing effect, improving the user experience.

[0403] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0404] Step 1: Register an expert prompt

[0405] Server Processing

[0406] The expert opens an input screen on the web interface and enters the prompt content. The terminal sends this input data to the server. The server receives this data, performs validation (checks the input content, detects prohibited characters, etc.), and then saves the prompt in a database. The input is "prompt information" and the output is "saved prompt data."

[0407] Step 2: User authentication and home screen display

[0408] Terminal handling

[0409] The user enters their ID and password on the login screen, and the device sends this authentication information to the server. The server checks the authentication information against the user information in the database to see if they match. If authentication is successful, a home screen is generated and sent to the device. The input is "authentication information" and the output is "HTML data for the home screen."

[0410] Step 3: Find the prompt

[0411] Terminal handling

[0412] The user enters search keywords and categories on the home screen and clicks the "Search" button. The device sends the search query to the server. The server searches the database for relevant prompts and sends the results to the user's device. The input is the "search query" and the output is a "list of search result prompts."

[0413] Step 4: Auto-generate the application based on prompts

[0414] Server Processing

[0415] The user selects a prompt from the search results and sends that information from their device to the server. The server identifies the required data source (e.g., stock price data API) and analysis method based on the prompt content, and automatically generates Python scripts and JavaScript code. The generated code is packaged and provided as a download link. The input is the "selected prompt information," and the output is the "download link for the generated application."

[0416] Step 5: Serving and Using the Application

[0417] Terminal handling

[0418] When the user clicks the download link provided by the server, the application is downloaded to the device, and the user runs the downloaded application. For example, the user opens a Python script in Jupyter Notebook to analyze stock price data.

[0419] Step 6: Enter feedback and recognize emotions

[0420] User Action

[0421] After using the application, users enter their ratings and comments in a feedback form and send it from their device to the server. The server inputs the received feedback data into an emotion engine and performs text and voice analysis to estimate the user's emotional state. The input is "feedback data" and the output is "emotion analysis results."

[0422] Step 7: Process feedback and store it in a database

[0423] Server Processing

[0424] The server evaluates the credibility of the feedback based on the sentiment analysis results and stores the information in a database. It also corrects and updates the prompt's rating score. The input is the sentiment analysis results and feedback data, and the output is the updated rating score.

[0425] Step 8: Implementing the reward system

[0426] Server Processing

[0427] The server periodically compiles the prompt evaluation scores and number of uses in the database and calculates the rewards to the experts based on that data. The reward information is notified to the experts, and they are paid by bank transfer or as points. The input is "prompt evaluation scores and number of uses," and the output is "reward calculation results and notification."

[0428] Step 9: Prompt recommendation using the emotion engine

[0429] Server Processing

[0430] When a user tries to select a prompt on the home screen, the system analyzes past feedback and current input state to recommend an appropriate prompt. The emotion engine analyzes the user's emotional state in real time and suggests relaxing or stimulating tasks based on that. The input is "user feedback and current input state," and the output is "recommended prompt."

[0431] (Application example 2)

[0432] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0433] Conventional systems that automatically generate applications based on prompts from experts do not take user emotions into account, limiting the improvement of user experience. Furthermore, because no advertising optimization based on user emotions is performed, the effectiveness of advertising is limited. The present invention aims to further improve the user experience by analyzing user emotions in real time and generating optimal advertising based on that analysis.

[0434] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0435] In this invention, the server includes a means for receiving prompts provided by experts, a means for storing the prompts in a data storage device and categorizing them, and a means for receiving a user's search query and retrieving the corresponding prompt from the data storage device, thereby enabling real-time analysis of user sentiment, evaluation of the credibility of the feedback, and generation of optimal advertisements based on the sentiment analysis.

[0436] A "prompt" is a short instruction or question provided by an expert that serves as a cue for performing a specific task or analysis.

[0437] A "data storage device" is a storage device or database for storing, classifying, and managing data.

[0438] A "search query" refers to the operation or content of a user sending a request or question to a system.

[0439] "Auto-generation" refers to the process by which a system automatically creates an application or program based on input it receives.

[0440] "Feedback" refers to opinions and ratings provided by users after using a system or application.

[0441] The "evaluation score" is a numerical representation of the usefulness and performance of the system or prompt based on feedback.

[0442] "Remuneration" means the compensation paid to an expert for providing or using a prompt.

[0443] "Emotion analysis" is the process of detecting and recognizing emotions from user input and behavior and determining their state.

[0444] "Ad generation" refers to the process of automatically creating advertising content based on specific criteria and data.

[0445] The present invention is a system that improves user experience by combining a platform that automatically generates applications based on prompts provided by experts and provides them to users with an emotion engine that recognizes user emotions. The system of the present invention is implemented with the following specific configuration.

[0446] Register an expert prompt

[0447] The server receives prompts provided by experts and stores them in a data storage device. The saved prompts are classified into specific categories so that they can be easily searched and retrieved later. For example, various prompts based on the experts' knowledge, such as prompts for data analysis and natural language processing, are registered.

[0448] User authentication and search prompts

[0449] The device receives the user's authentication information and sends it to the server. The server verifies this information and performs authentication. If authentication is successful, the home screen is displayed. The user can use the search interface on the home screen to search for prompts by entering keywords or categories.

[0450] Automatically generate applications based on prompts

[0451] The server receives the prompt selected by the user and starts the automatic generation of the application based on the prompt. For example, if a prompt for stock price analysis is selected, the server identifies the required data source (e.g., stock price data API) and analysis method, and generates and packages a Python script based on the selected data.

[0452] Application provision and use

[0453] The generated application is provided to the user's device as a download link. The user can click the link to download and run the application. For example, a Python script can be opened in Jupyter Notebook to analyze stock price data.

[0454] Feedback and Emotion Recognition

[0455] After using an application, users enter their evaluation and feedback into the system and submit it. The device analyzes the user's input text, voice, and facial expressions, and recognizes emotions in real time using an emotion engine. For example, if a user enters "This application is very easy to use," the emotion engine will interpret this as a positive emotion.

[0456] Sentiment analysis and ad generation

[0457] The server evaluates the credibility of the feedback based on the emotional data obtained from the emotion engine. It also generates advertisements according to the user's emotions and displays them at the optimal time. For example, if a user is feeling stressed, it can display an advertisement with a relaxing effect.

[0458] Reward System

[0459] The server periodically compiles the evaluation score and the number of times each prompt is used, and calculates and pays a reward to the expert based on that data. For example, an expert who provides a prompt with a high evaluation score and a high number of uses is paid a predetermined reward.

[0460] Examples of concrete examples and prompts

[0461] For example, if a user provides feedback such as "This product is very easy to use, but the price is a little high," the emotional data can be analyzed based on that feedback, and an advertisement can be generated and displayed that highlights promotional offers for the price.

[0462] Examples of prompts provided by experts include:

[0463] Perform sentiment analysis of user feedback and generate ads that highlight the benefits of your product if the responses are mostly positive, or highlight discounts and offers if the responses are mostly negative.

[0464] The above is a detailed description of the mode for carrying out the present invention.

[0465] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0466] Step 1:

[0467] Experts submit prompts through a web interface.

[0468] Input: The text data of the prompt.

[0469] Specific operation: The expert inputs the prompt to be provided and sends it to the system. The server receives the prompt and stores it in the database. When storing the prompt, it classifies it into a specific category.

[0470] Step 2:

[0471] The terminal receives the user's authentication information and sends it to the server.

[0472] Input: User ID and password.

[0473] Specific operation: The user enters their ID and password on the login screen and clicks the submit button. The server verifies the authentication information, and if authentication is successful, the home screen is displayed; if it is unsuccessful, an error message is displayed.

[0474] Step 3:

[0475] The user searches for the prompt in the search interface on the home screen.

[0476] Input: Search query (keywords and / or categories).

[0477] Specific operation: The user enters keywords or categories in the search bar and presses the search button. The server searches the database for the corresponding prompt and displays the results.

[0478] Step 4:

[0479] The server automatically generates an application based on the prompts selected by the user.

[0480] Input: Selected prompt.

[0481] Specific operation: Based on the selected prompt, the server identifies the required data source (e.g., API) and analysis method, and automatically generates program code such as a Python script. The generated code is then packaged.

[0482] Step 5:

[0483] The server provides the generated application to the user.

[0484] Input: The generated program code.

[0485] Specific operation: The server provides the automatically generated application to the user's device as a download link. The user clicks on the link to download the application.

[0486] Step 6:

[0487] Users can use the downloaded application to check the results.

[0488] Input: The downloaded application.

[0489] Specific operation: The user runs the downloaded application (e.g., Python script) in their own environment. For example, they open the script in Jupyter Notebook and check the analysis results.

[0490] Step 7:

[0491] Users provide feedback after using the application and the server analyzes their emotions.

[0492] Input: Feedback text.

[0493] Specific operation: The user inputs feedback about the application and sends it to the server. The server uses an emotion engine to analyze the feedback content and recognize the emotion.

[0494] Step 8:

[0495] The server generates an advertisement based on the emotion data and displays it to the user.

[0496] Input: Parsed emotion data.

[0497] Specific operation: The server automatically generates optimal advertisements (e.g., promotional offers or advertisements with a relaxing effect) based on the analyzed emotional data. The generated advertisements are then displayed on the user's device.

[0498] Step 9:

[0499] The server evaluates the veracity of the feedback and updates the prompt's evaluation score.

[0500] Input: Feedback content and sentiment data.

[0501] Specific operation: The server adjusts the prompt's evaluation score based on the feedback content and emotion data, and stores it in the database. If there are more positive emotions, the score will increase, and if there are more negative emotions, the score will decrease.

[0502] Step 10:

[0503] The server periodically compiles the prompt ratings and usage counts and pays rewards to the experts.

[0504] Input: Prompt rating score and usage count.

[0505] Specific operation: The server periodically calculates the reward for the expert based on the evaluation score and the number of times the expert has been used. The reward is notified to the expert and paid in a predetermined format (bank transfer, points, etc.).

[0506] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0507] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0508] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0509] [Second embodiment]

[0510] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0511] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0512] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0513] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0514] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0515] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0516] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0517] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0518] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0519] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0520] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0521] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0522] The present invention relates to a platform that automatically generates an application based on a prompt created by an expert and provides the application to a user. The system of the present invention is implemented with the following configuration.

[0523] Register an expert prompt

[0524] Server Processing

[0525] Experts use a web interface to provide prompts based on their knowledge and experience. The server receives the prompts and stores them in a database. The prompts are categorized according to specific fields or purposes. For example, prompts for data analysis and prompts for natural language processing are stored in separate categories.

[0526] User authentication and search prompts

[0527] Terminal handling

[0528] A user accesses the system using their own terminal and enters their ID and password on the login screen. The terminal sends this authentication information to the server. The server verifies the authentication information, and if authentication is successful, the home screen is displayed to the user. The home screen provides a search interface where users can search for prompts by entering categories or keywords.

[0529] Automatically generate applications based on prompts

[0530] Server Processing

[0531] The server receives the prompt selected by the user and begins the process of automatically generating an application based on that prompt. For example, if a "Stock Analysis Prompt" is selected, the server first identifies the required data source (e.g., stock price data API) and analysis method. Using this information, the server automatically generates a Python script and packages the script.

[0532] Application provision and use

[0533] Terminal handling

[0534] The server generates an application and provides it to the user as a download link. The user can click the link to download the application and run it on their computer. For example, they can open a downloaded Python script in Jupyter Notebook to analyze stock price data.

[0535] Feedback and Ratings

[0536] User Action

[0537] After using the application, users can provide their evaluation and feedback to the system. For example, if the application performs as expected, they can give it a high rating, and if there is room for improvement, they can leave a comment.

[0538] Server Processing

[0539] The server receives user feedback and ratings and stores them in a database. At the same time, it dynamically updates the prompt rating scores and calculates rewards for related experts. Experts who provide highly rated or frequently used prompts are rewarded.

[0540] Reward System

[0541] Server Processing

[0542] The server periodically compiles the evaluation score and number of times each prompt is used, and calculates the reward for the expert based on that data. For example, the server compiles the data at the end of the month, and pays rewards to experts who provide prompts with a rating of 4 or higher and that are used more than a certain number of times. The rewards are given as bank transfers or points.

[0543] This system allows experts to receive fair compensation and enables users to easily use high-quality applications. The present invention brings great benefits to both users and experts and promotes the accumulation of high-quality prompts.

[0544] The processing flow will be explained below.

[0545] Program processing steps and detailed explanation

[0546] Step 1:

[0547] Experts register prompts

[0548] Using the web interface, the expert inputs a prompt based on their own knowledge and experience, then presses the send button, and the terminal sends the input prompt to the server.

[0549] Step 2:

[0550] The server receives and saves the prompt

[0551] The server receives prompts sent by experts, which are stored in a database and classified into specific categories (e.g., data analysis, natural language processing).

[0552] Step 3:

[0553] A user logs into the system

[0554] The user accesses the system from a terminal and enters their ID and password on the login screen. The terminal then sends this authentication information to the server.

[0555] Step 4:

[0556] The server authenticates the user

[0557] The server checks the received authentication information against a database and displays the home screen if authentication is successful, or returns an error message if authentication fails.

[0558] Step 5:

[0559] The user searches for the prompt

[0560] After logging in, the user uses the search interface to search for prompts by category or keyword. The terminal sends the search query to the server.

[0561] Step 6:

[0562] The server retrieves and returns the prompt

[0563] The server searches its database based on the received search query and returns a list of matching prompts to the terminal.

[0564] Step 7:

[0565] The user selects a prompt

[0566] The user selects the appropriate prompt from the list of returned prompts, and the terminal then sends the information to the server.

[0567] Step 8:

[0568] The server automatically generates the application

[0569] The server analyzes the selected prompts, identifies the required data sources and analysis methods, and automatically generates program code (e.g., Python scripts) based on this information.

[0570] Step 9:

[0571] The server packages and serves the application

[0572] The server packages the automatically generated program code and generates a download link for the user, which is sent back to the device and displayed to the user.

[0573] Step 10:

[0574] Users download and use the application

[0575] Users click the provided link to download the application and run it on their device, for example, to open a Python script in Jupyter Notebook and perform data analysis.

[0576] Step 11:

[0577] Users provide feedback

[0578] After using an application, users input their evaluation and feedback into the system and submit it. The terminal then sends this to the server.

[0579] Step 12:

[0580] The server stores and updates feedback and ratings

[0581] The server stores the received feedback and ratings in a database and dynamically updates the rating scores of the prompts.

[0582] Step 13:

[0583] The server calculates the reward for the expert.

[0584] The server calculates the reward for the expert based on the evaluation score and number of times the prompt is used. The reward is notified to the expert and paid periodically.

[0585] These processing steps enable the automatic generation of high-quality applications based on prompts provided by experts, and the provision of these applications to users. This system benefits both users and experts, and promotes the accumulation of high-quality prompts.

[0586] Example 1

[0587] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0588] Currently, when trying to generate an application using prompts created by experts, the process is often manual and time-consuming and labor-intensive. Furthermore, the quality and feedback management of the generated application is left to the experts, resulting in a lack of uniformity. Furthermore, the compensation system for experts is unclear, which may lead to inadequate compensation. There is a need to resolve these issues and provide an efficient and fair system for both experts and users.

[0589] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0590] In this invention, the server includes: means for receiving prompts provided by experts; means for storing the prompts in a data storage device and classifying them by category; means for receiving user search queries and searching for and retrieving relevant prompts from the database; means for automatically generating software based on the retrieved prompts; means for providing the generated software to the user; means for receiving feedback and ratings from users, storing them in a data storage device, and updating the prompt ratings; and means for calculating and paying remuneration to experts based on the ratings and number of uses. This enables efficient management of prompts provided by experts and the provision of high-quality applications to users. Furthermore, since experts can be paid appropriately based on the feedback and ratings, a fair remuneration system can be established.

[0591] An "expert" is someone who has advanced knowledge and experience in a particular field and can create prompts related to that field.

[0592] A "prompt" is a text instruction written by an expert based on a specific task or objective, which the software then automatically generates.

[0593] A "data storage device" is a device for permanently storing information, including databases.

[0594] A "user" is a person who uses the system to obtain a specific application and execute it to achieve a purpose.

[0595] A "search query" is a string of characters that a user enters to search for required information from the database within the system.

[0596] "Software" means a collection of program code and associated files that are automatically generated based on prompts.

[0597] "Feedback" refers to the evaluations and opinions users provide after using an application, which are used to improve the system.

[0598] "Evaluation" refers to a quantitative scoring of the application used by the user, and the system compiles this to calculate the compensation paid to the expert.

[0599] "Reward" refers to compensation such as money or points paid to an expert based on the usage record and evaluation score of the prompt provided by the expert.

[0600] The present invention is a platform that automatically generates an application based on a prompt created by an expert and provides the application to a user, and is implemented with the following configuration.

[0601] The system consists of three main components: a server, a terminal, and a user. Here we will explain in detail how each component works and how it implements the invention.

[0602] Register an expert prompt

[0603] Server Processing

[0604] Experts use a web browser (e.g., a general-purpose web browser) to enter prompts through a web interface. The server receives the input data as HTTP requests and stores the prompts in a database (e.g., a general-purpose database management system). The prompts are then categorized according to specific domains or purposes.

[0605] User authentication and search prompts

[0606] Terminal handling

[0607] The user uses a web browser (e.g., a general-purpose web browser) on a device (e.g., a general-purpose personal computer) to enter their ID and password on the login screen. The device sends this information to the server, which then checks the authentication information against a database. If authentication is successful, the server provides the user with the home screen.

[0608] Automatically generate applications based on prompts

[0609] Server Processing

[0610] The server receives the prompt selected by the user and automatically generates software (e.g., a Python script) based on the prompt. For example, if a "Stock Analysis Prompt" is selected, the server identifies the stock price data API and analysis method, and generates a Python script based on that. This script adds lines to import necessary libraries (e.g., general-purpose libraries).

[0611] Application provision and use

[0612] Terminal handling

[0613] The application generated by the server is provided to the user's device as a download link. The user clicks the link to download the application and run it on their computer. For example, the user loads the "Stock Price Forecast.ipynb" file and uses Jupyter Notebook to perform analysis.

[0614] Feedback and Ratings

[0615] User Action

[0616] After using an application, users can rate and submit their feedback to the server. For example, if the application performs as expected, users can leave a comment such as "It's easy to use, but it's slow."

[0617] Server Processing

[0618] The server stores the received ratings and feedback in a database, updates the prompt's rating score, calculates the reward to the expert based on the rating and number of uses, and pays the reward in an appropriate way (e.g., bank transfer or points).

[0619] Examples of concrete examples and prompts

[0620] Examples:

[0621] Expert-submitted prompt: "Generate code to predict stock prices using Python"

[0622] User: Search for "Stock price prediction application"

[0623] Example of code generated by the server (specific code is not shown in this example)

[0624] Example prompt sentence:

[0625] "Generate prediction code using an LSTM model using AAPL stock price data in Python."

[0626] "Create a natural language processing prompt."

[0627] This system allows experts to receive fair compensation and enables users to easily use high-quality applications. In addition, since experts can be paid fairly based on feedback and evaluation, it is possible to build a fair compensation system.

[0628] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0629] Step 1:

[0630] Server Processing

[0631] The server receives prompts provided by experts, who use a web browser to enter the prompts into a web interface and submit them. The server receives the HTTP requests, stores them in a database, and categorizes them into categories.

[0632] Input: Expert-entered prompt and category information.

[0633] Output: Prompt and category information stored in a database.

[0634] Specific operation: The server saves the received prompt in text format in the database and adds category information using an SQL insert statement.

[0635] Step 2:

[0636] Terminal handling

[0637] The user accesses the login screen using the device's web browser and enters their ID and password, which is then sent to the server.

[0638] Input: The ID and password entered by the user.

[0639] Output: The authentication information sent to the server.

[0640] Specific operation: The user enters "user@example.com" and "password123" into the login form and clicks the login button.

[0641] Step 3:

[0642] Server Processing

[0643] The server checks the credentials against a database to verify them, and if successful presents the user with a home screen, otherwise it displays an error message.

[0644] Input: ID and password sent from the device.

[0645] Output: HTML of the home screen if authentication is successful, an error message if authentication fails.

[0646] What happens: The server uses an SQL query to check the authentication information in the database and generates an HTML response based on the authentication result.

[0647] Step 4:

[0648] Terminal handling

[0649] The home screen search interface allows users to enter keywords to search for a specific prompt, such as "stock price prediction."

[0650] Input: Keywords entered by the user into the search box on the home screen.

[0651] Output: The HTTP request with the keyword sent to the server.

[0652] Specific behavior: The user enters "stock price prediction" in the search box and clicks the search button.

[0653] Step 5:

[0654] Server Processing

[0655] The server searches the database based on the received search query, generates a list containing relevant prompts, and returns it to the user.

[0656] Input: User's search query (e.g. "stock price predictions").

[0657] Output: A list of the corresponding prompts in HTML format.

[0658] Specific operation: The server searches the database using an SQL query, generates the search results as an HTML list, and sends it to the user.

[0659] Step 6:

[0660] Terminal handling

[0661] The user selects a particular prompt from the search results and sends a request to the server.

[0662] Input: The prompt selected by the user.

[0663] Output: A prompt selection request is sent to the server.

[0664] Specific behavior: The user clicks on "Stock Price Prediction Prompt" that appears in the search results.

[0665] Step 7:

[0666] Server Processing

[0667] The server generates the application based on the selected prompts, for example by automatically generating a Python script to identify the required data sources and analysis methods.

[0668] Input: The user's prompt selection information.

[0669] Output: The generated Python script.

[0670] What it does: The server generates Python code based on the prompt and associated template, adding lines to import the necessary libraries.

[0671] Step 8:

[0672] Server Processing

[0673] Package the generated script and generate a URL from which the user can download it.

[0674] Input: The generated Python script.

[0675] Output: Download link.

[0676] Specific operation: The server compresses the script using a ZIP compression tool, generates a download URL, and provides it to the user.

[0677] Step 9:

[0678] Terminal handling

[0679] The user clicks on the download link to download the application and run it on their computer.

[0680] Input: Download link.

[0681] Output: The application downloaded to the device.

[0682] What happens: The user clicks on the provided link, downloads the ZIP file, unzips it, and opens it in Jupyter Notebook.

[0683] Step 10:

[0684] User Action

[0685] After using the application, the user inputs the evaluation and feedback and transmits them to the server.

[0686] Input: User ratings and feedback.

[0687] Output: Feedback information sent to the server.

[0688] Specific behavior: A user enters a score of "4" and a comment "Easy to use, but slow" in the evaluation form and clicks the submit button.

[0689] Step 11:

[0690] Server Processing

[0691] The server stores the received ratings and feedback in a database, updates the prompt's rating score, and calculates and pays the expert a reward based on the rating and number of uses.

[0692] Input: User ratings and feedback.

[0693] Output: Updated prompt evaluation scores and reward data for the expert.

[0694] Specific operation: The server updates the evaluation score, calculates the reward, and transfers the reward to the expert or awards points.

[0695] Through these steps, we can efficiently manage prompts based on the knowledge and experience of experts, provide high-quality applications to users, and properly reflect feedback and evaluations. This system will realize a fair and efficient application creation and reward system.

[0696] (Application example 1)

[0697] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0698] Managing the operation of robots in factories requires specialized knowledge and advanced skills. Furthermore, generating programs for rapid and effective response is extremely difficult, posing a major challenge for many companies. Current technology lacks a general-purpose means for easily generating and utilizing such programs. Therefore, in order to improve factory operational efficiency and rapidly put specialized knowledge to practical use, there is a need for an easy way to generate applications specialized for factory robots and to incorporate expert knowledge as feedback on an ongoing basis.

[0699] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0700] In this invention, the server includes means for receiving prompts provided by experts, means for saving the prompts in a database and categorizing them, means for receiving user search queries and searching for and retrieving relevant prompts from the database, means for automatically generating an application based on the retrieved prompts, means for providing the generated application to users, means for generating a program including operation and management of factory robots, means for providing the generated program to smartphones and tablet devices, means for receiving feedback and ratings from users, saving the feedback and ratings in a database and updating the evaluation scores of the prompts, and means for calculating and paying remuneration to experts based on the ratings and number of uses. This enables effective operation and management of factory robots and smooth practical application of expert knowledge.

[0701] An "expert" is someone who has advanced knowledge and experience in a particular field and can provide prompts.

[0702] A "prompt" is text information that provides guidelines or instructions for a specific task or purpose.

[0703] A "database" is a collection of information that centrally stores and manages multiple data sets and allows them to be searched and retrieved as needed.

[0704] A "category" is a group or segment for classifying and managing related prompts or data.

[0705] A "user" is a person who uses the system to perform prompt searches and generate applications.

[0706] A "search query" is a question or keyword that a user enters into a system to obtain specific information.

[0707] An "application" is a program that is automatically generated based on a prompt to perform a specific task.

[0708] A "factory robot" is a mechanical device used to automate and streamline work and operations in a factory.

[0709] A "smartphone" is a mobile device equipped with advanced communication functions and capable of running a wide variety of applications.

[0710] A "tablet device" is a mobile information terminal that has advanced communication functions similar to a smartphone and a larger screen.

[0711] "Feedback" is the act of providing the system with impressions and opinions after using an application.

[0712] The "rating score" is a numerical representation of the quality of a prompt or application based on user feedback.

[0713] "Remuneration" refers to compensation paid based on the usage and evaluation of the prompts provided by the expert.

[0714] "Program code" is text that contains a set of instructions for a computer to execute.

[0715] "Packaging" refers to the process of compiling the generated program code into a format that users can easily download and run.

[0716] "Route design" refers to a plan to optimize the movement routes and work sequences of robots within a factory.

[0717] The present invention is a system that automatically generates applications specialized for managing robot operations in factories by utilizing prompts based on the knowledge and experience of experts, and provides the applications to users. A specific embodiment of the present invention will be described below.

[0718] Overall system overview

[0719] This system is primarily composed of a server, user terminals, and smartphones or tablet terminals. The server is responsible for managing and processing various data, while the user terminal is responsible for communicating with the system and using applications.

[0720] Expert prompts provided and stored in a database

[0721] Experts provide prompts through a web interface. The server receives the prompts and efficiently stores them in a database. The prompts are categorized into categories such as "Data Analysis," "Natural Language Processing," and "Machine Learning," each of which is managed with its own index.

[0722] User authentication and search prompts

[0723] Users access the system from their devices and authenticate by entering their ID and password. If authentication is successful, the home screen is displayed. From here, users can enter a search query and search for and retrieve the corresponding prompt from the database. At this stage, the server analyzes the search query and provides the most appropriate prompt.

[0724] Automatically generate applications based on prompts

[0725] Based on the prompts selected by the user, the server automatically generates an application to support the operation and management of factory robots. This process involves generating program code using knowledge provided by experts to create a robot operation program that includes optimal control algorithms and flow line designs.

[0726] Application submission and feedback system

[0727] The generated application is packaged in a format that can be used on smartphones and tablets and provided as a download link. Users can click the link to download the application and run it on their device. After using the application, users send their feedback and rating to the server, which stores it in a database and updates the prompt's rating score.

[0728] Reward system for experts

[0729] The server periodically compiles the evaluation score and frequency of use of each prompt and calculates the reward for the expert based on that data. For example, an expert who provides a prompt with a high evaluation and frequency of use will be paid an appropriate reward.

[0730] Examples of concrete examples and prompts

[0731] Examples:

[0732] Experts design control algorithms for the effective operation of automated transport robots in factories and register these as prompts in the system. Users can log in using their smartphone app, select the appropriate prompt, and apply the automatically generated control program to the robot based on the prompt.

[0733] Example prompt sentence:

[0734] "An algorithm for designing efficient movement paths for automated transport robots in factories. Required fields: initial position of the robot, target position, and obstacle placement."

[0735] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0736] Step 1: Expert Prompts

[0737] Experts enter prompts through a web interface, which include guidelines and instructions for specific tasks. The server receives the prompts and stores them in a prompt database, where they are indexed and categorized by categories such as "data analysis," "natural language processing," and "machine learning."

[0738] Input: Prompt from an expert

[0739] Output: Prompts stored in the database

[0740] Step 2: User Authentication

[0741] The user accesses the system from their device and enters their ID and password for authentication. The device sends the authentication information to the server, which then verifies it. If authentication is successful, the home screen is displayed and the user can proceed to the next step.

[0742] Input: User ID, Password

[0743] Output: Authentication success or failure, home screen

[0744] Step 3: Find the prompt

[0745] A user enters a search query on the home screen and searches the prompt database. The server analyzes the search query and lists relevant prompts to provide to the user.

[0746] Input: Search query

[0747] Output: A list of applicable prompts

[0748] Step 4: Auto-generating the application

[0749] When a user selects a prompt from the search results, the server automatically generates an application based on that prompt. Specifically, appropriate program code is generated based on the prompt content. This program code includes control algorithms for factory robots and flow line designs.

[0750] Input:PromptSelect

[0751] Output: Auto-generated program code

[0752] Step 5: Serving the Application

[0753] The generated application is packaged in a format that can be used on smartphones and tablets and provided to the user as a download link, which the user can click to download the application and run it on their device.

[0754] Input: Generated program code

[0755] Output: Download link, application installed on device

[0756] Step 6: Gather feedback

[0757] After using the application, the user provides feedback and a rating based on the quality and usefulness of the application. The server receives this feedback and updates the rating score in the prompt database.

[0758] Input: Feedback, Rating

[0759] Output: Updated rating score

[0760] Step 7: Calculate the expert's fee

[0761] The server periodically compiles the evaluation score and frequency of use of each prompt, and calculates the reward for the expert based on that data. This reward is paid to the expert who provides the prompt with the highest evaluation and frequency of use.

[0762] Input: Evaluation score, number of uses

[0763] Output: Calculated reward, payment to the expert

[0764] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0765] The present invention improves the user experience by combining a platform that automatically generates applications based on prompts created by experts and provides those applications to users with an emotion engine that recognizes the user's emotions. The system of the present invention is implemented with the following configuration.

[0766] Register an expert prompt

[0767] Server Processing

[0768] Experts provide prompts based on their knowledge and experience through a web interface. The server receives the prompts and stores them in a database. The prompts are categorized according to specific fields or purposes. For example, prompts for data analysis and prompts for natural language processing are stored in separate categories.

[0769] User authentication and search prompts

[0770] Terminal handling

[0771] A user accesses the system using their own terminal and enters their ID and password on the login screen. The terminal sends this authentication information to the server. The server verifies the authentication information, and if authentication is successful, the user is shown the home screen. The home screen provides a search interface where users can search for prompts by entering categories or keywords.

[0772] Automatically generate applications based on prompts

[0773] Server Processing

[0774] The server receives the prompt selected by the user and begins the process of automatically generating an application based on that prompt. For example, if a "Stock Analysis Prompt" is selected, the server first identifies the required data source (e.g., stock price data API) and analysis method. Using this information, the server automatically generates a Python script and packages the script.

[0775] Application provision and use

[0776] Terminal handling

[0777] The server generates an application and provides it to the user as a download link. The user can click the link to download the application and run it on their computer. For example, they can open a downloaded Python script in Jupyter Notebook to analyze stock price data.

[0778] Feedback and Emotion Recognition

[0779] User Action

[0780] After using the application, users enter and submit their evaluations and feedback to the system. The emotion engine analyzes the user's emotions in real time using data such as text input, voice, and facial expressions. For example, when a user enters a text comment, the system analyzes the user's writing style and tone to infer their emotional state.

[0781] Server Processing

[0782] The server receives information from the emotion engine and evaluates the credibility of the feedback. It also adjusts the prompt's rating score based on the feedback and the user's emotional state and stores it in a database. For example, if the user expresses positive emotions, the rating will be set higher.

[0783] Reward System

[0784] Server Processing

[0785] The server periodically compiles the evaluation score and number of times each prompt is used, and calculates the reward for the expert based on that data. The reward is periodically notified to the expert and paid. For example, the data is compiled at the end of the month, and rewards are paid to experts who provide prompts with a rating of 4 or higher and that are used more than a certain number of times. This reward is awarded by bank transfer or as points.

[0786] Utilizing the Emotion Engine

[0787] Server Processing

[0788] The emotion engine is also utilized when the user selects a prompt. The server analyzes the user's emotional state and recommends prompts that suit the user's emotions. For example, if the user is feeling stressed, it can recommend a task that will have a relaxing effect, improving the user experience.

[0789] In this way, the system of the present invention not only automatically generates high-quality applications based on prompts provided by experts and provides them to users, but also improves the user experience by recognizing the user's emotions using an emotion engine and correcting the evaluation score and recommending prompts based on those emotions. This system brings great benefits to both users and experts and promotes the accumulation of high-quality prompts.

[0790] The processing flow will be explained below.

[0791] Program processing steps and detailed explanation

[0792] Step 1:

[0793] Experts register prompts

[0794] Using the web interface, the expert inputs a prompt based on their own knowledge and experience, then presses the send button, and the terminal sends the input prompt to the server.

[0795] Step 2:

[0796] The server receives and saves the prompt

[0797] The server receives prompts sent by experts, which are stored in a database and classified into specific categories (e.g., data analysis, natural language processing).

[0798] Step 3:

[0799] A user logs into the system

[0800] The user accesses the system from a terminal and enters their ID and password on the login screen. The terminal then sends this authentication information to the server.

[0801] Step 4:

[0802] The server authenticates the user

[0803] The server checks the received authentication information against a database and displays the home screen if authentication is successful, or returns an error message if authentication fails.

[0804] Step 5:

[0805] The user searches for the prompt

[0806] After logging in, the user uses the search interface to search for prompts by category or keyword. The terminal sends the search query to the server.

[0807] Step 6:

[0808] The server retrieves and returns the prompt

[0809] The server searches its database based on the received search query and returns a list of matching prompts to the terminal.

[0810] Step 7:

[0811] The user selects a prompt

[0812] The user selects the appropriate prompt from the list of returned prompts, and the terminal then sends the information to the server.

[0813] Step 8:

[0814] The server automatically generates the application

[0815] The server analyzes the selected prompts, identifies the required data sources and analysis methods, and automatically generates program code (e.g., Python scripts) based on this information.

[0816] Step 9:

[0817] The server packages and serves the application

[0818] The server packages the automatically generated program code and generates a download link for the user, which is sent back to the device and displayed to the user.

[0819] Step 10:

[0820] Users download and use the application

[0821] Users click the provided link to download the application and run it on their device, for example, to open a Python script in Jupyter Notebook and perform data analysis.

[0822] Step 11:

[0823] Users provide feedback

[0824] After using the application, users enter their evaluation and feedback into the system and submit it. The emotion engine also analyzes the user's text, voice, facial expressions, and other data. The device then sends this information to the server.

[0825] Step 12:

[0826] The server stores and updates feedback and ratings

[0827] The server receives information from the emotion engine, evaluates the credibility of the feedback based on it, and adjusts the prompt evaluation score based on the feedback content and emotional state, and stores the correct score in a database.

[0828] Step 13:

[0829] The server uses an emotion engine to recommend prompts

[0830] The server uses an emotion engine to analyze the user's emotional state in real time and recommends appropriate prompts to the user, for example, recommending a relaxing task to a user who is feeling stressed.

[0831] Step 14:

[0832] The server calculates the reward for the expert.

[0833] The server periodically compiles the evaluation score and the number of times each prompt is used, and calculates the reward for the expert based on that data. The reward is then notified to the expert and paid periodically.

[0834] These processing steps enable the automatic generation of high-quality applications based on prompts provided by experts and the provision of these applications to users. This system benefits both users and experts and promotes the accumulation of high-quality prompts. In addition, the use of an emotion engine makes it possible to provide a more personalized experience according to the user's emotional state.

[0835] Example 2

[0836] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0837] Previous systems were unable to consider the user's emotional state when automatically generating applications based on prompts provided by experts. This limited the improvement of the user experience, and the prompt evaluation and recommendation process relied on subjective evaluation. Furthermore, the reliability of the feedback could be reduced, making it difficult to evaluate the quality of the prompts.

[0838] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0839] In this invention, the server includes: means for receiving prompts provided by an expert; means for saving the prompts in a database and categorizing them; means for receiving a user's search query and searching for and retrieving relevant prompts from the database; means for automatically generating an application based on the retrieved prompts; means for providing the generated application to the user; means for receiving feedback and ratings from the user and saving them in a database and updating the prompt rating score; means for calculating and paying a fee to the expert based on the rating and the number of times the prompts are used; and means for analyzing the user's emotional state, evaluating the credibility of the feedback, and recommending prompts. This allows prompts to be evaluated and recommended based on the user's emotional state, improving the user experience and the quality of prompts through more credible feedback.

[0840] An "expert" is someone who has knowledge and experience in a particular field or purpose and can provide prompts based on that knowledge.

[0841] A "prompt" is text or data containing information or instructions necessary for the automatic generation of an application for a specific domain or purpose.

[0842] A "database" is a system for systematically storing and managing multiple data, and is used to store prompts and feedback.

[0843] "User" means a person who uses the system to search and select prompts and use the application.

[0844] "Emotional state" refers to a user's emotional or mood state, and is determined using text and voice analysis.

[0845] "Feedback" means information, including ratings and comments, provided by a User after using an Application.

[0846] The "evaluation score" is a numerical representation of the quality of the prompt or application based on feedback.

[0847] "Rewards" are monetary or point-based compensation provided to experts who contribute to the system.

[0848] "Application" means software or a program that is automatically generated based on prompts.

[0849] An "emotion engine" is an algorithm or system for analyzing a user's emotional state.

[0850] A "search query" is a keyword or condition that a user enters to search for prompts in a database.

[0851] "Auto-generation" refers to the process by which the system automatically generates an application or program based on prompts.

[0852] "Packaging" refers to the process of assembling the generated program code into an executable format.

[0853] "Credibility" refers to the degree to which the feedback or rating provided is accurate and trustworthy.

[0854] "Recommendation" refers to the act of suggesting appropriate prompts or applications based on the user's emotional state and past behavior.

[0855] The present invention relates to a platform for automatically generating applications based on prompts created by experts and providing those applications to users. This system is characterized by improving the user experience by combining an emotion engine.

[0856] Register an expert prompt

[0857] Server Processing

[0858] Experts enter prompts based on their knowledge and experience through a web interface, and the server receives the prompts, categorizes them, and stores them in a database, such as "prompts for data analysis" and "prompts for natural language processing."

[0859] As a specific example, an expert creates a "stock price prediction model" and registers the prompt in the system.

[0860] User authentication and search prompts

[0861] Terminal handling

[0862] A user accesses the system using their own terminal and enters their ID and password on the login screen. The terminal sends this authentication information to the server. The server verifies the authentication information, and if authentication is successful, the user is shown the home screen. The home screen has a search interface, and users can search for prompts by entering categories or keywords.

[0863] As a concrete example, consider a user who logs in and searches for "stock price predictions." The user enters the search keywords and gets relevant prompts.

[0864] Automatically generate applications based on prompts

[0865] Server Processing

[0866] Once the server receives the prompt selected by the user, it starts the process of automatically generating an application based on that prompt. For example, if a "Stock Analysis Prompt" is selected, the server will identify the required data source (e.g., stock data API) and analysis method, and generate Python scripts and JavaScript code.

[0867] As a specific example, an application is generated based on the prompt, "Generate a Python script for stock price prediction. The data source used will be the Yahoo Finance API, and the analysis method will be the machine learning random forest model."

[0868] Application provision and use

[0869] Terminal handling

[0870] The server generates an application and provides it to the user as a download link. The user can click the link to download the application and run it on their computer. For example, they can open a downloaded Python script in Jupyter Notebook to analyze stock price data.

[0871] Feedback and Emotion Recognition

[0872] User Action

[0873] After using the application, users enter their evaluation and feedback into the system and submit it. The emotion engine analyzes the user's emotions in real time using data such as text input, voice, and facial expressions. For example, when a user enters a text comment, the system analyzes the user's writing style and tone to infer their emotional state.

[0874] Server Processing

[0875] The server receives information from the emotion engine and evaluates the credibility of the feedback. It also adjusts the prompt's rating score based on the feedback and the user's emotional state and stores it in a database. For example, if the user expresses positive emotions, the rating will be set higher.

[0876] Reward System

[0877] Server Processing

[0878] The server periodically compiles the evaluation score and number of times each prompt is used, and calculates the reward for the expert based on that data. The reward is periodically notified to the expert and paid. For example, the data is compiled at the end of the month, and rewards are paid to experts who provide prompts with a rating of 4 or higher and that are used more than a certain number of times. This reward is awarded by bank transfer or as points.

[0879] Utilizing the Emotion Engine

[0880] Server Processing

[0881] The emotion engine is also utilized when the user selects a prompt. The server analyzes the user's emotional state and recommends prompts that suit the user's emotions. For example, if the user is feeling stressed, it can recommend a task that will have a relaxing effect, improving the user experience.

[0882] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0883] Step 1: Register an expert prompt

[0884] Server Processing

[0885] The expert opens an input screen on the web interface and enters the prompt content. The terminal sends this input data to the server. The server receives this data, performs validation (checks the input content, detects prohibited characters, etc.), and then saves the prompt in a database. The input is "prompt information" and the output is "saved prompt data."

[0886] Step 2: User authentication and home screen display

[0887] Terminal handling

[0888] The user enters their ID and password on the login screen, and the device sends this authentication information to the server. The server checks the authentication information against the user information in the database to see if they match. If authentication is successful, a home screen is generated and sent to the device. The input is "authentication information" and the output is "HTML data for the home screen."

[0889] Step 3: Find the prompt

[0890] Terminal handling

[0891] The user enters search keywords and categories on the home screen and clicks the "Search" button. The device sends the search query to the server. The server searches the database for relevant prompts and sends the results to the user's device. The input is the "search query" and the output is a "list of search result prompts."

[0892] Step 4: Auto-generate the application based on prompts

[0893] Server Processing

[0894] The user selects a prompt from the search results and sends that information from their device to the server. The server identifies the required data source (e.g., stock price data API) and analysis method based on the prompt content, and automatically generates Python scripts and JavaScript code. The generated code is packaged and provided as a download link. The input is the "selected prompt information," and the output is the "download link for the generated application."

[0895] Step 5: Serving and Using the Application

[0896] Terminal handling

[0897] When the user clicks the download link provided by the server, the application is downloaded to the device, and the user runs the downloaded application. For example, the user opens a Python script in Jupyter Notebook to analyze stock price data.

[0898] Step 6: Enter feedback and recognize emotions

[0899] User Action

[0900] After using the application, users enter their ratings and comments in a feedback form and send it from their device to the server. The server inputs the received feedback data into an emotion engine and performs text and voice analysis to estimate the user's emotional state. The input is "feedback data" and the output is "emotion analysis results."

[0901] Step 7: Process feedback and store it in a database

[0902] Server Processing

[0903] The server evaluates the credibility of the feedback based on the sentiment analysis results and stores the information in a database. It also corrects and updates the prompt's rating score. The input is the sentiment analysis results and feedback data, and the output is the updated rating score.

[0904] Step 8: Implementing the reward system

[0905] Server Processing

[0906] The server periodically compiles the prompt evaluation scores and number of uses in the database and calculates the rewards to the experts based on that data. The reward information is notified to the experts, and they are paid by bank transfer or as points. The input is "prompt evaluation scores and number of uses," and the output is "reward calculation results and notification."

[0907] Step 9: Prompt recommendation using the emotion engine

[0908] Server Processing

[0909] When a user tries to select a prompt on the home screen, the system analyzes past feedback and current input state to recommend an appropriate prompt. The emotion engine analyzes the user's emotional state in real time and suggests relaxing or stimulating tasks based on that. The input is "user feedback and current input state," and the output is "recommended prompt."

[0910] (Application example 2)

[0911] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0912] Conventional systems that automatically generate applications based on prompts from experts do not take user emotions into account, limiting the improvement of user experience. Furthermore, because no advertising optimization based on user emotions is performed, the effectiveness of advertising is limited. The present invention aims to further improve the user experience by analyzing user emotions in real time and generating optimal advertising based on that analysis.

[0913] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0914] In this invention, the server includes a means for receiving prompts provided by experts, a means for storing the prompts in a data storage device and categorizing them, and a means for receiving a user's search query and retrieving the corresponding prompt from the data storage device, thereby enabling real-time analysis of user sentiment, evaluation of the credibility of the feedback, and generation of optimal advertisements based on the sentiment analysis.

[0915] A "prompt" is a short instruction or question provided by an expert that serves as a cue for performing a specific task or analysis.

[0916] A "data storage device" is a storage device or database for storing, classifying, and managing data.

[0917] A "search query" refers to the operation or content of a user sending a request or question to a system.

[0918] "Auto-generation" refers to the process by which a system automatically creates an application or program based on input it receives.

[0919] "Feedback" refers to opinions and ratings provided by users after using a system or application.

[0920] The "evaluation score" is a numerical representation of the usefulness and performance of the system or prompt based on feedback.

[0921] "Remuneration" means the compensation paid to an expert for providing or using a prompt.

[0922] "Emotion analysis" is the process of detecting and recognizing emotions from user input and behavior and determining their state.

[0923] "Ad generation" refers to the process of automatically creating advertising content based on specific criteria and data.

[0924] The present invention is a system that improves user experience by combining a platform that automatically generates applications based on prompts provided by experts and provides them to users with an emotion engine that recognizes user emotions. The system of the present invention is implemented with the following specific configuration.

[0925] Register an expert prompt

[0926] The server receives prompts provided by experts and stores them in a data storage device. The saved prompts are classified into specific categories so that they can be easily searched and retrieved later. For example, various prompts based on the experts' knowledge, such as prompts for data analysis and natural language processing, are registered.

[0927] User authentication and search prompts

[0928] The device receives the user's authentication information and sends it to the server. The server verifies this information and performs authentication. If authentication is successful, the home screen is displayed. The user can use the search interface on the home screen to search for prompts by entering keywords or categories.

[0929] Automatically generate applications based on prompts

[0930] The server receives the prompt selected by the user and starts the automatic generation of the application based on the prompt. For example, if a prompt for stock price analysis is selected, the server identifies the required data source (e.g., stock price data API) and analysis method, and generates and packages a Python script based on the selected data.

[0931] Application provision and use

[0932] The generated application is provided to the user's device as a download link. The user can click the link to download and run the application. For example, a Python script can be opened in Jupyter Notebook to analyze stock price data.

[0933] Feedback and Emotion Recognition

[0934] After using an application, users enter their evaluation and feedback into the system and submit it. The device analyzes the user's input text, voice, and facial expressions, and recognizes emotions in real time using an emotion engine. For example, if a user enters "This application is very easy to use," the emotion engine will interpret this as a positive emotion.

[0935] Sentiment analysis and ad generation

[0936] The server evaluates the credibility of the feedback based on the emotional data obtained from the emotion engine. It also generates advertisements according to the user's emotions and displays them at the optimal time. For example, if a user is feeling stressed, it can display an advertisement with a relaxing effect.

[0937] Reward System

[0938] The server periodically compiles the evaluation score and the number of times each prompt is used, and calculates and pays a reward to the expert based on that data. For example, an expert who provides a prompt with a high evaluation score and a high number of uses is paid a predetermined reward.

[0939] Examples of concrete examples and prompts

[0940] For example, if a user provides feedback such as "This product is very easy to use, but the price is a little high," the emotional data can be analyzed based on that feedback, and an advertisement can be generated and displayed that highlights promotional offers for the price.

[0941] Examples of prompts provided by experts include:

[0942] Perform sentiment analysis of user feedback and generate ads that highlight the benefits of your product if the responses are mostly positive, or highlight discounts and offers if the responses are mostly negative.

[0943] The above is a detailed description of the mode for carrying out the present invention.

[0944] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0945] Step 1:

[0946] Experts submit prompts through a web interface.

[0947] Input: The text data of the prompt.

[0948] Specific operation: The expert inputs the prompt to be provided and sends it to the system. The server receives the prompt and stores it in the database. When storing the prompt, it classifies it into a specific category.

[0949] Step 2:

[0950] The terminal receives the user's authentication information and sends it to the server.

[0951] Input: User ID and password.

[0952] Specific operation: The user enters their ID and password on the login screen and clicks the submit button. The server verifies the authentication information, and if authentication is successful, the home screen is displayed; if it is unsuccessful, an error message is displayed.

[0953] Step 3:

[0954] The user searches for the prompt in the search interface on the home screen.

[0955] Input: Search query (keywords and / or categories).

[0956] Specific operation: The user enters keywords or categories in the search bar and presses the search button. The server searches the database for the corresponding prompt and displays the results.

[0957] Step 4:

[0958] The server automatically generates an application based on the prompts selected by the user.

[0959] Input: Selected prompt.

[0960] Specific operation: Based on the selected prompt, the server identifies the required data source (e.g., API) and analysis method, and automatically generates program code such as a Python script. The generated code is then packaged.

[0961] Step 5:

[0962] The server provides the generated application to the user.

[0963] Input: The generated program code.

[0964] Specific operation: The server provides the automatically generated application to the user's device as a download link. The user clicks on the link to download the application.

[0965] Step 6:

[0966] Users can use the downloaded application to check the results.

[0967] Input: The downloaded application.

[0968] Specific operation: The user runs the downloaded application (e.g., Python script) in their own environment. For example, they open the script in Jupyter Notebook and check the analysis results.

[0969] Step 7:

[0970] Users provide feedback after using the application and the server analyzes their emotions.

[0971] Input: Feedback text.

[0972] Specific operation: The user inputs feedback about the application and sends it to the server. The server uses an emotion engine to analyze the feedback content and recognize the emotion.

[0973] Step 8:

[0974] The server generates an advertisement based on the emotion data and displays it to the user.

[0975] Input: Parsed emotion data.

[0976] Specific operation: The server automatically generates optimal advertisements (e.g., promotional offers or advertisements with a relaxing effect) based on the analyzed emotional data. The generated advertisements are then displayed on the user's device.

[0977] Step 9:

[0978] The server evaluates the veracity of the feedback and updates the prompt's evaluation score.

[0979] Input: Feedback content and sentiment data.

[0980] Specific operation: The server adjusts the prompt's evaluation score based on the feedback content and emotion data, and stores it in the database. If there are more positive emotions, the score will increase, and if there are more negative emotions, the score will decrease.

[0981] Step 10:

[0982] The server periodically compiles the prompt ratings and usage counts and pays rewards to the experts.

[0983] Input: Prompt rating score and usage count.

[0984] Specific operation: The server periodically calculates the reward for the expert based on the evaluation score and the number of times the expert has been used. The reward is notified to the expert and paid in a predetermined format (bank transfer, points, etc.).

[0985] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0986] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0987] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0988] [Third embodiment]

[0989] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0990] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0991] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0992] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0993] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0994] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0995] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0996] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0997] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0998] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0999] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1000] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1001] The present invention relates to a platform that automatically generates an application based on a prompt created by an expert and provides the application to a user. The system of the present invention is implemented with the following configuration.

[1002] Register an expert prompt

[1003] Server Processing

[1004] Experts use a web interface to provide prompts based on their knowledge and experience. The server receives the prompts and stores them in a database. The prompts are categorized according to specific fields or purposes. For example, prompts for data analysis and prompts for natural language processing are stored in separate categories.

[1005] User authentication and search prompts

[1006] Terminal handling

[1007] A user accesses the system using their own terminal and enters their ID and password on the login screen. The terminal sends this authentication information to the server. The server verifies the authentication information, and if authentication is successful, the home screen is displayed to the user. The home screen provides a search interface where users can search for prompts by entering categories or keywords.

[1008] Automatically generate applications based on prompts

[1009] Server Processing

[1010] The server receives the prompt selected by the user and begins the process of automatically generating an application based on that prompt. For example, if a "Stock Analysis Prompt" is selected, the server first identifies the required data source (e.g., stock price data API) and analysis method. Using this information, the server automatically generates a Python script and packages the script.

[1011] Application provision and use

[1012] Terminal handling

[1013] The server generates an application and provides it to the user as a download link. The user can click the link to download the application and run it on their computer. For example, they can open a downloaded Python script in Jupyter Notebook to analyze stock price data.

[1014] Feedback and Ratings

[1015] User Action

[1016] After using the application, users can provide their evaluation and feedback to the system. For example, if the application performs as expected, they can give it a high rating, and if there is room for improvement, they can leave a comment.

[1017] Server Processing

[1018] The server receives user feedback and ratings and stores them in a database. At the same time, it dynamically updates the prompt rating scores and calculates rewards for related experts. Experts who provide highly rated or frequently used prompts are rewarded.

[1019] Reward System

[1020] Server Processing

[1021] The server periodically compiles the evaluation score and number of times each prompt is used, and calculates the reward for the expert based on that data. For example, the server compiles the data at the end of the month, and pays rewards to experts who provide prompts with a rating of 4 or higher and that are used more than a certain number of times. The rewards are given as bank transfers or points.

[1022] This system allows experts to receive fair compensation and enables users to easily use high-quality applications. The present invention brings great benefits to both users and experts and promotes the accumulation of high-quality prompts.

[1023] The processing flow will be explained below.

[1024] Program processing steps and detailed explanation

[1025] Step 1:

[1026] Experts register prompts

[1027] Using the web interface, the expert inputs a prompt based on their own knowledge and experience, then presses the send button, and the terminal sends the input prompt to the server.

[1028] Step 2:

[1029] The server receives and saves the prompt

[1030] The server receives prompts sent by experts, which are stored in a database and classified into specific categories (e.g., data analysis, natural language processing).

[1031] Step 3:

[1032] A user logs into the system

[1033] The user accesses the system from a terminal and enters their ID and password on the login screen. The terminal then sends this authentication information to the server.

[1034] Step 4:

[1035] The server authenticates the user

[1036] The server checks the received authentication information against a database and displays the home screen if authentication is successful, or returns an error message if authentication fails.

[1037] Step 5:

[1038] The user searches for the prompt

[1039] After logging in, the user uses the search interface to search for prompts by category or keyword. The terminal sends the search query to the server.

[1040] Step 6:

[1041] The server retrieves and returns the prompt

[1042] The server searches its database based on the received search query and returns a list of matching prompts to the terminal.

[1043] Step 7:

[1044] The user selects a prompt

[1045] The user selects the appropriate prompt from the list of returned prompts, and the terminal then sends the information to the server.

[1046] Step 8:

[1047] The server automatically generates the application

[1048] The server analyzes the selected prompts, identifies the required data sources and analysis methods, and automatically generates program code (e.g., Python scripts) based on this information.

[1049] Step 9:

[1050] The server packages and serves the application

[1051] The server packages the automatically generated program code and generates a download link for the user, which is sent back to the device and displayed to the user.

[1052] Step 10:

[1053] Users download and use the application

[1054] Users click the provided link to download the application and run it on their device, for example, to open a Python script in Jupyter Notebook and perform data analysis.

[1055] Step 11:

[1056] Users provide feedback

[1057] After using an application, users input their evaluation and feedback into the system and submit it. The terminal then sends this to the server.

[1058] Step 12:

[1059] The server stores and updates feedback and ratings

[1060] The server stores the received feedback and ratings in a database and dynamically updates the rating scores of the prompts.

[1061] Step 13:

[1062] The server calculates the reward for the expert.

[1063] The server calculates the reward for the expert based on the evaluation score and number of times the prompt is used. The reward is notified to the expert and paid periodically.

[1064] These processing steps enable the automatic generation of high-quality applications based on prompts provided by experts, and the provision of these applications to users. This system benefits both users and experts, and promotes the accumulation of high-quality prompts.

[1065] Example 1

[1066] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1067] Currently, when trying to generate an application using prompts created by experts, the process is often manual and time-consuming and labor-intensive. Furthermore, the quality and feedback management of the generated application is left to the experts, resulting in a lack of uniformity. Furthermore, the compensation system for experts is unclear, which may lead to inadequate compensation. There is a need to resolve these issues and provide an efficient and fair system for both experts and users.

[1068] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1069] In this invention, the server includes: means for receiving prompts provided by experts; means for storing the prompts in a data storage device and classifying them by category; means for receiving user search queries and searching for and retrieving relevant prompts from the database; means for automatically generating software based on the retrieved prompts; means for providing the generated software to the user; means for receiving feedback and ratings from users, storing them in a data storage device, and updating the prompt ratings; and means for calculating and paying remuneration to experts based on the ratings and number of uses. This enables efficient management of prompts provided by experts and the provision of high-quality applications to users. Furthermore, since experts can be paid appropriately based on the feedback and ratings, a fair remuneration system can be established.

[1070] An "expert" is someone who has advanced knowledge and experience in a particular field and can create prompts related to that field.

[1071] A "prompt" is a text instruction written by an expert based on a specific task or objective, which the software then automatically generates.

[1072] A "data storage device" is a device for permanently storing information, including databases.

[1073] A "user" is a person who uses the system to obtain a specific application and execute it to achieve a purpose.

[1074] A "search query" is a string of characters that a user enters to search for required information from the database within the system.

[1075] "Software" means a collection of program code and associated files that are automatically generated based on prompts.

[1076] "Feedback" refers to the evaluations and opinions users provide after using an application, which are used to improve the system.

[1077] "Evaluation" refers to a quantitative scoring of the application used by the user, and the system compiles this to calculate the compensation paid to the expert.

[1078] "Reward" refers to compensation such as money or points paid to an expert based on the usage record and evaluation score of the prompt provided by the expert.

[1079] The present invention is a platform that automatically generates an application based on a prompt created by an expert and provides the application to a user, and is implemented with the following configuration.

[1080] The system consists of three main components: a server, a terminal, and a user. Here we will explain in detail how each component works and how it implements the invention.

[1081] Register an expert prompt

[1082] Server Processing

[1083] Experts use a web browser (e.g., a general-purpose web browser) to enter prompts through a web interface. The server receives the input data as HTTP requests and stores the prompts in a database (e.g., a general-purpose database management system). The prompts are then categorized according to specific domains or purposes.

[1084] User authentication and search prompts

[1085] Terminal handling

[1086] The user uses a web browser (e.g., a general-purpose web browser) on a device (e.g., a general-purpose personal computer) to enter their ID and password on the login screen. The device sends this information to the server, which then checks the authentication information against a database. If authentication is successful, the server provides the user with the home screen.

[1087] Automatically generate applications based on prompts

[1088] Server Processing

[1089] The server receives the prompt selected by the user and automatically generates software (e.g., a Python script) based on the prompt. For example, if a "Stock Analysis Prompt" is selected, the server identifies the stock price data API and analysis method, and generates a Python script based on that. This script adds lines to import necessary libraries (e.g., general-purpose libraries).

[1090] Application provision and use

[1091] Terminal handling

[1092] The application generated by the server is provided to the user's device as a download link. The user clicks the link to download the application and run it on their computer. For example, the user loads the "Stock Price Forecast.ipynb" file and uses Jupyter Notebook to perform analysis.

[1093] Feedback and Ratings

[1094] User Action

[1095] After using an application, users can rate and submit their feedback to the server. For example, if the application performs as expected, users can leave a comment such as "It's easy to use, but it's slow."

[1096] Server Processing

[1097] The server stores the received ratings and feedback in a database, updates the prompt's rating score, calculates the reward to the expert based on the rating and number of uses, and pays the reward in an appropriate way (e.g., bank transfer or points).

[1098] Examples of concrete examples and prompts

[1099] Examples:

[1100] Expert-submitted prompt: "Generate code to predict stock prices using Python"

[1101] User: Search for "Stock price prediction application"

[1102] Example of code generated by the server (specific code is not shown in this example)

[1103] Example prompt sentence:

[1104] "Generate prediction code using an LSTM model using AAPL stock price data in Python."

[1105] "Create a natural language processing prompt."

[1106] This system allows experts to receive fair compensation and enables users to easily use high-quality applications. In addition, since experts can be paid fairly based on feedback and evaluation, it is possible to build a fair compensation system.

[1107] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1108] Step 1:

[1109] Server Processing

[1110] The server receives prompts provided by experts, who use a web browser to enter the prompts into a web interface and submit them. The server receives the HTTP requests, stores them in a database, and categorizes them into categories.

[1111] Input: Expert-entered prompt and category information.

[1112] Output: Prompt and category information stored in a database.

[1113] Specific operation: The server saves the received prompt in text format in the database and adds category information using an SQL insert statement.

[1114] Step 2:

[1115] Terminal handling

[1116] The user accesses the login screen using the device's web browser and enters their ID and password, which is then sent to the server.

[1117] Input: The ID and password entered by the user.

[1118] Output: The authentication information sent to the server.

[1119] Specific operation: The user enters "user@example.com" and "password123" into the login form and clicks the login button.

[1120] Step 3:

[1121] Server Processing

[1122] The server checks the credentials against a database to verify them, and if successful presents the user with a home screen, otherwise it displays an error message.

[1123] Input: ID and password sent from the device.

[1124] Output: HTML of the home screen if authentication is successful, an error message if authentication fails.

[1125] What happens: The server uses an SQL query to check the authentication information in the database and generates an HTML response based on the authentication result.

[1126] Step 4:

[1127] Terminal handling

[1128] The home screen search interface allows users to enter keywords to search for a specific prompt, such as "stock price prediction."

[1129] Input: Keywords entered by the user into the search box on the home screen.

[1130] Output: The HTTP request with the keyword sent to the server.

[1131] Specific behavior: The user enters "stock price prediction" in the search box and clicks the search button.

[1132] Step 5:

[1133] Server Processing

[1134] The server searches the database based on the received search query, generates a list containing relevant prompts, and returns it to the user.

[1135] Input: User's search query (e.g. "stock price predictions").

[1136] Output: A list of the corresponding prompts in HTML format.

[1137] Specific operation: The server searches the database using an SQL query, generates the search results as an HTML list, and sends it to the user.

[1138] Step 6:

[1139] Terminal handling

[1140] The user selects a particular prompt from the search results and sends a request to the server.

[1141] Input: The prompt selected by the user.

[1142] Output: A prompt selection request is sent to the server.

[1143] Specific behavior: The user clicks on "Stock Price Prediction Prompt" that appears in the search results.

[1144] Step 7:

[1145] Server Processing

[1146] The server generates the application based on the selected prompts, for example by automatically generating a Python script to identify the required data sources and analysis methods.

[1147] Input: The user's prompt selection information.

[1148] Output: The generated Python script.

[1149] What it does: The server generates Python code based on the prompt and associated template, adding lines to import the necessary libraries.

[1150] Step 8:

[1151] Server Processing

[1152] Package the generated script and generate a URL from which the user can download it.

[1153] Input: The generated Python script.

[1154] Output: Download link.

[1155] Specific operation: The server compresses the script using a ZIP compression tool, generates a download URL, and provides it to the user.

[1156] Step 9:

[1157] Terminal handling

[1158] The user clicks on the download link to download the application and run it on their computer.

[1159] Input: Download link.

[1160] Output: The application downloaded to the device.

[1161] What happens: The user clicks on the provided link, downloads the ZIP file, unzips it, and opens it in Jupyter Notebook.

[1162] Step 10:

[1163] User Action

[1164] After using the application, the user inputs the evaluation and feedback and transmits them to the server.

[1165] Input: User ratings and feedback.

[1166] Output: Feedback information sent to the server.

[1167] Specific behavior: A user enters a score of "4" and a comment "Easy to use, but slow" in the evaluation form and clicks the submit button.

[1168] Step 11:

[1169] Server Processing

[1170] The server stores the received ratings and feedback in a database, updates the prompt's rating score, and calculates and pays the expert a reward based on the rating and number of uses.

[1171] Input: User ratings and feedback.

[1172] Output: Updated prompt evaluation scores and reward data for the expert.

[1173] Specific operation: The server updates the evaluation score, calculates the reward, and transfers the reward to the expert or awards points.

[1174] Through these steps, we can efficiently manage prompts based on the knowledge and experience of experts, provide high-quality applications to users, and properly reflect feedback and evaluations. This system will realize a fair and efficient application creation and reward system.

[1175] (Application example 1)

[1176] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1177] Managing the operation of robots in factories requires specialized knowledge and advanced skills. Furthermore, generating programs for rapid and effective response is extremely difficult, posing a major challenge for many companies. Current technology lacks a general-purpose means for easily generating and utilizing such programs. Therefore, in order to improve factory operational efficiency and rapidly put specialized knowledge to practical use, there is a need for an easy way to generate applications specialized for factory robots and to incorporate expert knowledge as feedback on an ongoing basis.

[1178] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1179] In this invention, the server includes means for receiving prompts provided by experts, means for saving the prompts in a database and categorizing them, means for receiving user search queries and searching for and retrieving relevant prompts from the database, means for automatically generating an application based on the retrieved prompts, means for providing the generated application to users, means for generating a program including operation and management of factory robots, means for providing the generated program to smartphones and tablet devices, means for receiving feedback and ratings from users, saving the feedback and ratings in a database and updating the evaluation scores of the prompts, and means for calculating and paying remuneration to experts based on the ratings and number of uses. This enables effective operation and management of factory robots and smooth practical application of expert knowledge.

[1180] An "expert" is someone who has advanced knowledge and experience in a particular field and can provide prompts.

[1181] A "prompt" is text information that provides guidelines or instructions for a specific task or purpose.

[1182] A "database" is a collection of information that centrally stores and manages multiple data sets and allows them to be searched and retrieved as needed.

[1183] A "category" is a group or segment for classifying and managing related prompts or data.

[1184] A "user" is a person who uses the system to perform prompt searches and generate applications.

[1185] A "search query" is a question or keyword that a user enters into a system to obtain specific information.

[1186] An "application" is a program that is automatically generated based on a prompt to perform a specific task.

[1187] A "factory robot" is a mechanical device used to automate and streamline work and operations in a factory.

[1188] A "smartphone" is a mobile device equipped with advanced communication functions and capable of running a wide variety of applications.

[1189] A "tablet device" is a mobile information terminal that has advanced communication functions similar to a smartphone and a larger screen.

[1190] "Feedback" is the act of providing the system with impressions and opinions after using an application.

[1191] The "rating score" is a numerical representation of the quality of a prompt or application based on user feedback.

[1192] "Remuneration" refers to compensation paid based on the usage and evaluation of the prompts provided by the expert.

[1193] "Program code" is text that contains a set of instructions for a computer to execute.

[1194] "Packaging" refers to the process of compiling the generated program code into a format that users can easily download and run.

[1195] "Route design" refers to a plan to optimize the movement routes and work sequences of robots within a factory.

[1196] The present invention is a system that automatically generates applications specialized for managing robot operations in factories by utilizing prompts based on the knowledge and experience of experts, and provides the applications to users. A specific embodiment of the present invention will be described below.

[1197] Overall system overview

[1198] This system is primarily composed of a server, user terminals, and smartphones or tablet terminals. The server is responsible for managing and processing various data, while the user terminal is responsible for communicating with the system and using applications.

[1199] Expert prompts provided and stored in a database

[1200] Experts provide prompts through a web interface. The server receives the prompts and efficiently stores them in a database. The prompts are categorized into categories such as "Data Analysis," "Natural Language Processing," and "Machine Learning," each of which is managed with its own index.

[1201] User authentication and search prompts

[1202] Users access the system from their devices and authenticate by entering their ID and password. If authentication is successful, the home screen is displayed. From here, users can enter a search query and search for and retrieve the corresponding prompt from the database. At this stage, the server analyzes the search query and provides the most appropriate prompt.

[1203] Automatically generate applications based on prompts

[1204] Based on the prompts selected by the user, the server automatically generates an application to support the operation and management of factory robots. This process involves generating program code using knowledge provided by experts to create a robot operation program that includes optimal control algorithms and flow line designs.

[1205] Application submission and feedback system

[1206] The generated application is packaged in a format that can be used on smartphones and tablets and provided as a download link. Users can click the link to download the application and run it on their device. After using the application, users send their feedback and rating to the server, which stores it in a database and updates the prompt's rating score.

[1207] Reward system for experts

[1208] The server periodically compiles the evaluation score and frequency of use of each prompt and calculates the reward for the expert based on that data. For example, an expert who provides a prompt with a high evaluation and frequency of use will be paid an appropriate reward.

[1209] Examples of concrete examples and prompts

[1210] Examples:

[1211] Experts design control algorithms for the effective operation of automated transport robots in factories and register these as prompts in the system. Users can log in using their smartphone app, select the appropriate prompt, and apply the automatically generated control program to the robot based on the prompt.

[1212] Example prompt sentence:

[1213] "An algorithm for designing efficient movement paths for automated transport robots in factories. Required fields: initial position of the robot, target position, and obstacle placement."

[1214] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1215] Step 1: Expert Prompts

[1216] Experts enter prompts through a web interface, which include guidelines and instructions for specific tasks. The server receives the prompts and stores them in a prompt database, where they are indexed and categorized by categories such as "data analysis," "natural language processing," and "machine learning."

[1217] Input: Prompt from an expert

[1218] Output: Prompts stored in the database

[1219] Step 2: User Authentication

[1220] The user accesses the system from their device and enters their ID and password for authentication. The device sends the authentication information to the server, which then verifies it. If authentication is successful, the home screen is displayed and the user can proceed to the next step.

[1221] Input: User ID, Password

[1222] Output: Authentication success or failure, home screen

[1223] Step 3: Find the prompt

[1224] A user enters a search query on the home screen and searches the prompt database. The server analyzes the search query and lists relevant prompts to provide to the user.

[1225] Input: Search query

[1226] Output: A list of applicable prompts

[1227] Step 4: Auto-generating the application

[1228] When a user selects a prompt from the search results, the server automatically generates an application based on that prompt. Specifically, appropriate program code is generated based on the prompt content. This program code includes control algorithms for factory robots and flow line designs.

[1229] Input:PromptSelect

[1230] Output: Auto-generated program code

[1231] Step 5: Serving the Application

[1232] The generated application is packaged in a format that can be used on smartphones and tablets and provided to the user as a download link, which the user can click to download the application and run it on their device.

[1233] Input: Generated program code

[1234] Output: Download link, application installed on device

[1235] Step 6: Gather feedback

[1236] After using the application, the user provides feedback and a rating based on the quality and usefulness of the application. The server receives this feedback and updates the rating score in the prompt database.

[1237] Input: Feedback, Rating

[1238] Output: Updated rating score

[1239] Step 7: Calculate the expert's fee

[1240] The server periodically compiles the evaluation score and frequency of use of each prompt, and calculates the reward for the expert based on that data. This reward is paid to the expert who provides the prompt with the highest evaluation and frequency of use.

[1241] Input: Evaluation score, number of uses

[1242] Output: Calculated reward, payment to the expert

[1243] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1244] The present invention improves the user experience by combining a platform that automatically generates applications based on prompts created by experts and provides those applications to users with an emotion engine that recognizes the user's emotions. The system of the present invention is implemented with the following configuration.

[1245] Register an expert prompt

[1246] Server Processing

[1247] Experts provide prompts based on their knowledge and experience through a web interface. The server receives the prompts and stores them in a database. The prompts are categorized according to specific fields or purposes. For example, prompts for data analysis and prompts for natural language processing are stored in separate categories.

[1248] User authentication and search prompts

[1249] Terminal handling

[1250] A user accesses the system using their own terminal and enters their ID and password on the login screen. The terminal sends this authentication information to the server. The server verifies the authentication information, and if authentication is successful, the user is shown the home screen. The home screen provides a search interface where users can search for prompts by entering categories or keywords.

[1251] Automatically generate applications based on prompts

[1252] Server Processing

[1253] The server receives the prompt selected by the user and begins the process of automatically generating an application based on that prompt. For example, if a "Stock Analysis Prompt" is selected, the server first identifies the required data source (e.g., stock price data API) and analysis method. Using this information, the server automatically generates a Python script and packages the script.

[1254] Application provision and use

[1255] Terminal handling

[1256] The server generates an application and provides it to the user as a download link. The user can click the link to download the application and run it on their computer. For example, they can open a downloaded Python script in Jupyter Notebook to analyze stock price data.

[1257] Feedback and Emotion Recognition

[1258] User Action

[1259] After using the application, users enter and submit their evaluations and feedback to the system. The emotion engine analyzes the user's emotions in real time using data such as text input, voice, and facial expressions. For example, when a user enters a text comment, the system analyzes the user's writing style and tone to infer their emotional state.

[1260] Server Processing

[1261] The server receives information from the emotion engine and evaluates the credibility of the feedback. It also adjusts the prompt's rating score based on the feedback and the user's emotional state and stores it in a database. For example, if the user expresses positive emotions, the rating will be set higher.

[1262] Reward System

[1263] Server Processing

[1264] The server periodically compiles the evaluation score and number of times each prompt is used, and calculates the reward for the expert based on that data. The reward is periodically notified to the expert and paid. For example, the data is compiled at the end of the month, and rewards are paid to experts who provide prompts with a rating of 4 or higher and that are used more than a certain number of times. This reward is awarded by bank transfer or as points.

[1265] Utilizing the Emotion Engine

[1266] Server Processing

[1267] The emotion engine is also utilized when the user selects a prompt. The server analyzes the user's emotional state and recommends prompts that suit the user's emotions. For example, if the user is feeling stressed, it can recommend a task that will have a relaxing effect, improving the user experience.

[1268] In this way, the system of the present invention not only automatically generates high-quality applications based on prompts provided by experts and provides them to users, but also improves the user experience by recognizing the user's emotions using an emotion engine and correcting the evaluation score and recommending prompts based on those emotions. This system brings great benefits to both users and experts and promotes the accumulation of high-quality prompts.

[1269] The processing flow will be explained below.

[1270] Program processing steps and detailed explanation

[1271] Step 1:

[1272] Experts register prompts

[1273] Using the web interface, the expert inputs a prompt based on their own knowledge and experience, then presses the send button, and the terminal sends the input prompt to the server.

[1274] Step 2:

[1275] The server receives and saves the prompt

[1276] The server receives prompts sent by experts, which are stored in a database and classified into specific categories (e.g., data analysis, natural language processing).

[1277] Step 3:

[1278] A user logs into the system

[1279] The user accesses the system from a terminal and enters their ID and password on the login screen. The terminal then sends this authentication information to the server.

[1280] Step 4:

[1281] The server authenticates the user

[1282] The server checks the received authentication information against a database and displays the home screen if authentication is successful, or returns an error message if authentication fails.

[1283] Step 5:

[1284] The user searches for the prompt

[1285] After logging in, the user uses the search interface to search for prompts by category or keyword. The terminal sends the search query to the server.

[1286] Step 6:

[1287] The server retrieves and returns the prompt

[1288] The server searches its database based on the received search query and returns a list of matching prompts to the terminal.

[1289] Step 7:

[1290] The user selects a prompt

[1291] The user selects the appropriate prompt from the list of returned prompts, and the terminal then sends the information to the server.

[1292] Step 8:

[1293] The server automatically generates the application

[1294] The server analyzes the selected prompts, identifies the required data sources and analysis methods, and automatically generates program code (e.g., Python scripts) based on this information.

[1295] Step 9:

[1296] The server packages and serves the application

[1297] The server packages the automatically generated program code and generates a download link for the user, which is sent back to the device and displayed to the user.

[1298] Step 10:

[1299] Users download and use the application

[1300] Users click the provided link to download the application and run it on their device, for example, to open a Python script in Jupyter Notebook and perform data analysis.

[1301] Step 11:

[1302] Users provide feedback

[1303] After using the application, users enter their evaluation and feedback into the system and submit it. The emotion engine also analyzes the user's text, voice, facial expressions, and other data. The device then sends this information to the server.

[1304] Step 12:

[1305] The server stores and updates feedback and ratings

[1306] The server receives information from the emotion engine, evaluates the credibility of the feedback based on it, and adjusts the prompt evaluation score based on the feedback content and emotional state, and stores the correct score in a database.

[1307] Step 13:

[1308] The server uses an emotion engine to recommend prompts

[1309] The server uses an emotion engine to analyze the user's emotional state in real time and recommends appropriate prompts to the user, for example, recommending a relaxing task to a user who is feeling stressed.

[1310] Step 14:

[1311] The server calculates the reward for the expert.

[1312] The server periodically compiles the evaluation score and the number of times each prompt is used, and calculates the reward for the expert based on that data. The reward is then notified to the expert and paid periodically.

[1313] These processing steps enable the automatic generation of high-quality applications based on prompts provided by experts and the provision of these applications to users. This system benefits both users and experts and promotes the accumulation of high-quality prompts. In addition, the use of an emotion engine makes it possible to provide a more personalized experience according to the user's emotional state.

[1314] Example 2

[1315] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1316] Previous systems were unable to consider the user's emotional state when automatically generating applications based on prompts provided by experts. This limited the improvement of the user experience, and the prompt evaluation and recommendation process relied on subjective evaluation. Furthermore, the reliability of the feedback could be reduced, making it difficult to evaluate the quality of the prompts.

[1317] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1318] In this invention, the server includes: means for receiving prompts provided by an expert; means for saving the prompts in a database and categorizing them; means for receiving a user's search query and searching for and retrieving relevant prompts from the database; means for automatically generating an application based on the retrieved prompts; means for providing the generated application to the user; means for receiving feedback and ratings from the user and saving them in a database and updating the prompt rating score; means for calculating and paying a fee to the expert based on the rating and the number of times the prompts are used; and means for analyzing the user's emotional state, evaluating the credibility of the feedback, and recommending prompts. This allows prompts to be evaluated and recommended based on the user's emotional state, improving the user experience and the quality of prompts through more credible feedback.

[1319] An "expert" is someone who has knowledge and experience in a particular field or purpose and can provide prompts based on that knowledge.

[1320] A "prompt" is text or data containing information or instructions necessary for the automatic generation of an application for a specific domain or purpose.

[1321] A "database" is a system for systematically storing and managing multiple data, and is used to store prompts and feedback.

[1322] "User" means a person who uses the system to search and select prompts and use the application.

[1323] "Emotional state" refers to a user's emotional or mood state, and is determined using text and voice analysis.

[1324] "Feedback" means information, including ratings and comments, provided by a User after using an Application.

[1325] The "evaluation score" is a numerical representation of the quality of the prompt or application based on feedback.

[1326] "Rewards" are monetary or point-based compensation provided to experts who contribute to the system.

[1327] "Application" means software or a program that is automatically generated based on prompts.

[1328] An "emotion engine" is an algorithm or system for analyzing a user's emotional state.

[1329] A "search query" is a keyword or condition that a user enters to search for prompts in a database.

[1330] "Auto-generation" refers to the process by which the system automatically generates an application or program based on prompts.

[1331] "Packaging" refers to the process of assembling the generated program code into an executable format.

[1332] "Credibility" refers to the degree to which the feedback or rating provided is accurate and trustworthy.

[1333] "Recommendation" refers to the act of suggesting appropriate prompts or applications based on the user's emotional state and past behavior.

[1334] The present invention relates to a platform for automatically generating applications based on prompts created by experts and providing those applications to users. This system is characterized by improving the user experience by combining an emotion engine.

[1335] Register an expert prompt

[1336] Server Processing

[1337] Experts enter prompts based on their knowledge and experience through a web interface, and the server receives the prompts, categorizes them, and stores them in a database, such as "prompts for data analysis" and "prompts for natural language processing."

[1338] As a specific example, an expert creates a "stock price prediction model" and registers the prompt in the system.

[1339] User authentication and search prompts

[1340] Terminal handling

[1341] A user accesses the system using their own terminal and enters their ID and password on the login screen. The terminal sends this authentication information to the server. The server verifies the authentication information, and if authentication is successful, the user is shown the home screen. The home screen has a search interface, and users can search for prompts by entering categories or keywords.

[1342] As a concrete example, consider a user who logs in and searches for "stock price predictions." The user enters the search keywords and gets relevant prompts.

[1343] Automatically generate applications based on prompts

[1344] Server Processing

[1345] Once the server receives the prompt selected by the user, it starts the process of automatically generating an application based on that prompt. For example, if a "Stock Analysis Prompt" is selected, the server will identify the required data source (e.g., stock data API) and analysis method, and generate Python scripts and JavaScript code.

[1346] As a specific example, an application is generated based on the prompt, "Generate a Python script for stock price prediction. The data source used will be the Yahoo Finance API, and the analysis method will be the machine learning random forest model."

[1347] Application provision and use

[1348] Terminal handling

[1349] The server generates an application and provides it to the user as a download link. The user can click the link to download the application and run it on their computer. For example, they can open a downloaded Python script in Jupyter Notebook to analyze stock price data.

[1350] Feedback and Emotion Recognition

[1351] User Action

[1352] After using the application, users enter their evaluation and feedback into the system and submit it. The emotion engine analyzes the user's emotions in real time using data such as text input, voice, and facial expressions. For example, when a user enters a text comment, the system analyzes the user's writing style and tone to infer their emotional state.

[1353] Server Processing

[1354] The server receives information from the emotion engine and evaluates the credibility of the feedback. It also adjusts the prompt's rating score based on the feedback and the user's emotional state and stores it in a database. For example, if the user expresses positive emotions, the rating will be set higher.

[1355] Reward System

[1356] Server Processing

[1357] The server periodically compiles the evaluation score and number of times each prompt is used, and calculates the reward for the expert based on that data. The reward is periodically notified to the expert and paid. For example, the data is compiled at the end of the month, and rewards are paid to experts who provide prompts with a rating of 4 or higher and that are used more than a certain number of times. This reward is awarded by bank transfer or as points.

[1358] Utilizing the Emotion Engine

[1359] Server Processing

[1360] The emotion engine is also utilized when the user selects a prompt. The server analyzes the user's emotional state and recommends prompts that suit the user's emotions. For example, if the user is feeling stressed, it can recommend a task that will have a relaxing effect, improving the user experience.

[1361] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1362] Step 1: Register an expert prompt

[1363] Server Processing

[1364] The expert opens an input screen on the web interface and enters the prompt content. The terminal sends this input data to the server. The server receives this data, performs validation (checks the input content, detects prohibited characters, etc.), and then saves the prompt in a database. The input is "prompt information" and the output is "saved prompt data."

[1365] Step 2: User authentication and home screen display

[1366] Terminal handling

[1367] The user enters their ID and password on the login screen, and the device sends this authentication information to the server. The server checks the authentication information against the user information in the database to see if they match. If authentication is successful, a home screen is generated and sent to the device. The input is "authentication information" and the output is "HTML data for the home screen."

[1368] Step 3: Find the prompt

[1369] Terminal handling

[1370] The user enters search keywords and categories on the home screen and clicks the "Search" button. The device sends the search query to the server. The server searches the database for relevant prompts and sends the results to the user's device. The input is the "search query" and the output is a "list of search result prompts."

[1371] Step 4: Auto-generate the application based on prompts

[1372] Server Processing

[1373] The user selects a prompt from the search results and sends that information from their device to the server. The server identifies the required data source (e.g., stock price data API) and analysis method based on the prompt content, and automatically generates Python scripts and JavaScript code. The generated code is packaged and provided as a download link. The input is the "selected prompt information," and the output is the "download link for the generated application."

[1374] Step 5: Serving and Using the Application

[1375] Terminal handling

[1376] When the user clicks the download link provided by the server, the application is downloaded to the device, and the user runs the downloaded application. For example, the user opens a Python script in Jupyter Notebook to analyze stock price data.

[1377] Step 6: Enter feedback and recognize emotions

[1378] User Action

[1379] After using the application, users enter their ratings and comments in a feedback form and send it from their device to the server. The server inputs the received feedback data into an emotion engine and performs text and voice analysis to estimate the user's emotional state. The input is "feedback data" and the output is "emotion analysis results."

[1380] Step 7: Process feedback and store it in a database

[1381] Server Processing

[1382] The server evaluates the credibility of the feedback based on the sentiment analysis results and stores the information in a database. It also corrects and updates the prompt's rating score. The input is the sentiment analysis results and feedback data, and the output is the updated rating score.

[1383] Step 8: Implementing the reward system

[1384] Server Processing

[1385] The server periodically compiles the prompt evaluation scores and number of uses in the database and calculates the rewards to the experts based on that data. The reward information is notified to the experts, and they are paid by bank transfer or as points. The input is "prompt evaluation scores and number of uses," and the output is "reward calculation results and notification."

[1386] Step 9: Prompt recommendation using the emotion engine

[1387] Server Processing

[1388] When a user tries to select a prompt on the home screen, the system analyzes past feedback and current input state to recommend an appropriate prompt. The emotion engine analyzes the user's emotional state in real time and suggests relaxing or stimulating tasks based on that. The input is "user feedback and current input state," and the output is "recommended prompt."

[1389] (Application example 2)

[1390] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1391] Conventional systems that automatically generate applications based on prompts from experts do not take user emotions into account, limiting the improvement of user experience. Furthermore, because no advertising optimization based on user emotions is performed, the effectiveness of advertising is limited. The present invention aims to further improve the user experience by analyzing user emotions in real time and generating optimal advertising based on that analysis.

[1392] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1393] In this invention, the server includes a means for receiving prompts provided by experts, a means for storing the prompts in a data storage device and categorizing them, and a means for receiving a user's search query and retrieving the corresponding prompt from the data storage device, thereby enabling real-time analysis of user sentiment, evaluation of the credibility of the feedback, and generation of optimal advertisements based on the sentiment analysis.

[1394] A "prompt" is a short instruction or question provided by an expert that serves as a cue for performing a specific task or analysis.

[1395] A "data storage device" is a storage device or database for storing, classifying, and managing data.

[1396] A "search query" refers to the operation or content of a user sending a request or question to a system.

[1397] "Auto-generation" refers to the process by which a system automatically creates an application or program based on input it receives.

[1398] "Feedback" refers to opinions and ratings provided by users after using a system or application.

[1399] The "evaluation score" is a numerical representation of the usefulness and performance of the system or prompt based on feedback.

[1400] "Remuneration" means the compensation paid to an expert for providing or using a prompt.

[1401] "Emotion analysis" is the process of detecting and recognizing emotions from user input and behavior and determining their state.

[1402] "Ad generation" refers to the process of automatically creating advertising content based on specific criteria and data.

[1403] The present invention is a system that improves user experience by combining a platform that automatically generates applications based on prompts provided by experts and provides them to users with an emotion engine that recognizes user emotions. The system of the present invention is implemented with the following specific configuration.

[1404] Register an expert prompt

[1405] The server receives prompts provided by experts and stores them in a data storage device. The saved prompts are classified into specific categories so that they can be easily searched and retrieved later. For example, various prompts based on the experts' knowledge, such as prompts for data analysis and natural language processing, are registered.

[1406] User authentication and search prompts

[1407] The device receives the user's authentication information and sends it to the server. The server verifies this information and performs authentication. If authentication is successful, the home screen is displayed. The user can use the search interface on the home screen to search for prompts by entering keywords or categories.

[1408] Automatically generate applications based on prompts

[1409] The server receives the prompt selected by the user and starts the automatic generation of the application based on the prompt. For example, if a prompt for stock price analysis is selected, the server identifies the required data source (e.g., stock price data API) and analysis method, and generates and packages a Python script based on the selected data.

[1410] Application provision and use

[1411] The generated application is provided to the user's device as a download link. The user can click the link to download and run the application. For example, a Python script can be opened in Jupyter Notebook to analyze stock price data.

[1412] Feedback and Emotion Recognition

[1413] After using an application, users enter their evaluation and feedback into the system and submit it. The device analyzes the user's input text, voice, and facial expressions, and recognizes emotions in real time using an emotion engine. For example, if a user enters "This application is very easy to use," the emotion engine will interpret this as a positive emotion.

[1414] Sentiment analysis and ad generation

[1415] The server evaluates the credibility of the feedback based on the emotional data obtained from the emotion engine. It also generates advertisements according to the user's emotions and displays them at the optimal time. For example, if a user is feeling stressed, it can display an advertisement with a relaxing effect.

[1416] Reward System

[1417] The server periodically compiles the evaluation score and the number of times each prompt is used, and calculates and pays a reward to the expert based on that data. For example, an expert who provides a prompt with a high evaluation score and a high number of uses is paid a predetermined reward.

[1418] Examples of concrete examples and prompts

[1419] For example, if a user provides feedback such as "This product is very easy to use, but the price is a little high," the emotional data can be analyzed based on that feedback, and an advertisement can be generated and displayed that highlights promotional offers for the price.

[1420] Examples of prompts provided by experts include:

[1421] Perform sentiment analysis of user feedback and generate ads that highlight the benefits of your product if the responses are mostly positive, or highlight discounts and offers if the responses are mostly negative.

[1422] The above is a detailed description of the mode for carrying out the present invention.

[1423] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1424] Step 1:

[1425] Experts submit prompts through a web interface.

[1426] Input: The text data of the prompt.

[1427] Specific operation: The expert inputs the prompt to be provided and sends it to the system. The server receives the prompt and stores it in the database. When storing the prompt, it classifies it into a specific category.

[1428] Step 2:

[1429] The terminal receives the user's authentication information and sends it to the server.

[1430] Input: User ID and password.

[1431] Specific operation: The user enters their ID and password on the login screen and clicks the submit button. The server verifies the authentication information, and if authentication is successful, the home screen is displayed; if it is unsuccessful, an error message is displayed.

[1432] Step 3:

[1433] The user searches for the prompt in the search interface on the home screen.

[1434] Input: Search query (keywords and / or categories).

[1435] Specific operation: The user enters keywords or categories in the search bar and presses the search button. The server searches the database for the corresponding prompt and displays the results.

[1436] Step 4:

[1437] The server automatically generates an application based on the prompts selected by the user.

[1438] Input: Selected prompt.

[1439] Specific operation: Based on the selected prompt, the server identifies the required data source (e.g., API) and analysis method, and automatically generates program code such as a Python script. The generated code is then packaged.

[1440] Step 5:

[1441] The server provides the generated application to the user.

[1442] Input: The generated program code.

[1443] Specific operation: The server provides the automatically generated application to the user's device as a download link. The user clicks on the link to download the application.

[1444] Step 6:

[1445] Users can use the downloaded application to check the results.

[1446] Input: The downloaded application.

[1447] Specific operation: The user runs the downloaded application (e.g., Python script) in their own environment. For example, they open the script in Jupyter Notebook and check the analysis results.

[1448] Step 7:

[1449] Users provide feedback after using the application and the server analyzes their emotions.

[1450] Input: Feedback text.

[1451] Specific operation: The user inputs feedback about the application and sends it to the server. The server uses an emotion engine to analyze the feedback content and recognize the emotion.

[1452] Step 8:

[1453] The server generates an advertisement based on the emotion data and displays it to the user.

[1454] Input: Parsed emotion data.

[1455] Specific operation: The server automatically generates optimal advertisements (e.g., promotional offers or advertisements with a relaxing effect) based on the analyzed emotional data. The generated advertisements are then displayed on the user's device.

[1456] Step 9:

[1457] The server evaluates the veracity of the feedback and updates the prompt's evaluation score.

[1458] Input: Feedback content and sentiment data.

[1459] Specific operation: The server adjusts the prompt's evaluation score based on the feedback content and emotion data, and stores it in the database. If there are more positive emotions, the score will increase, and if there are more negative emotions, the score will decrease.

[1460] Step 10:

[1461] The server periodically compiles the prompt ratings and usage counts and pays rewards to the experts.

[1462] Input: Prompt rating score and usage count.

[1463] Specific operation: The server periodically calculates the reward for the expert based on the evaluation score and the number of times the expert has been used. The reward is notified to the expert and paid in a predetermined format (bank transfer, points, etc.).

[1464] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1465] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1466] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1467] [Fourth embodiment]

[1468] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1469] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1470] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1471] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1472] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1473] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1474] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1475] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1476] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1477] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1478] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1479] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1480] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1481] The present invention relates to a platform that automatically generates an application based on a prompt created by an expert and provides the application to a user. The system of the present invention is implemented with the following configuration.

[1482] Register an expert prompt

[1483] Server Processing

[1484] Experts use a web interface to provide prompts based on their knowledge and experience. The server receives the prompts and stores them in a database. The prompts are categorized according to specific fields or purposes. For example, prompts for data analysis and prompts for natural language processing are stored in separate categories.

[1485] User authentication and search prompts

[1486] Terminal handling

[1487] A user accesses the system using their own terminal and enters their ID and password on the login screen. The terminal sends this authentication information to the server. The server verifies the authentication information, and if authentication is successful, the home screen is displayed to the user. The home screen provides a search interface where users can search for prompts by entering categories or keywords.

[1488] Automatically generate applications based on prompts

[1489] Server Processing

[1490] The server receives the prompt selected by the user and begins the process of automatically generating an application based on that prompt. For example, if a "Stock Analysis Prompt" is selected, the server first identifies the required data source (e.g., stock price data API) and analysis method. Using this information, the server automatically generates a Python script and packages the script.

[1491] Application provision and use

[1492] Terminal handling

[1493] The server generates an application and provides it to the user as a download link. The user can click the link to download the application and run it on their computer. For example, they can open a downloaded Python script in Jupyter Notebook to analyze stock price data.

[1494] Feedback and Ratings

[1495] User Action

[1496] After using the application, users can provide their evaluation and feedback to the system. For example, if the application performs as expected, they can give it a high rating, and if there is room for improvement, they can leave a comment.

[1497] Server Processing

[1498] The server receives user feedback and ratings and stores them in a database. At the same time, it dynamically updates the prompt rating scores and calculates rewards for related experts. Experts who provide highly rated or frequently used prompts are rewarded.

[1499] Reward System

[1500] Server Processing

[1501] The server periodically compiles the evaluation score and number of times each prompt is used, and calculates the reward for the expert based on that data. For example, the server compiles the data at the end of the month, and pays rewards to experts who provide prompts with a rating of 4 or higher and that are used more than a certain number of times. The rewards are given as bank transfers or points.

[1502] This system allows experts to receive fair compensation and enables users to easily use high-quality applications. The present invention brings great benefits to both users and experts and promotes the accumulation of high-quality prompts.

[1503] The processing flow will be explained below.

[1504] Program processing steps and detailed explanation

[1505] Step 1:

[1506] Experts register prompts

[1507] Using the web interface, the expert inputs a prompt based on their own knowledge and experience, then presses the send button, and the terminal sends the input prompt to the server.

[1508] Step 2:

[1509] The server receives and saves the prompt

[1510] The server receives prompts sent by experts, which are stored in a database and classified into specific categories (e.g., data analysis, natural language processing).

[1511] Step 3:

[1512] A user logs into the system

[1513] The user accesses the system from a terminal and enters their ID and password on the login screen. The terminal then sends this authentication information to the server.

[1514] Step 4:

[1515] The server authenticates the user

[1516] The server checks the received authentication information against a database and displays the home screen if authentication is successful, or returns an error message if authentication fails.

[1517] Step 5:

[1518] The user searches for the prompt

[1519] After logging in, the user uses the search interface to search for prompts by category or keyword. The terminal sends the search query to the server.

[1520] Step 6:

[1521] The server retrieves and returns the prompt

[1522] The server searches its database based on the received search query and returns a list of matching prompts to the terminal.

[1523] Step 7:

[1524] The user selects a prompt

[1525] The user selects the appropriate prompt from the list of returned prompts, and the terminal then sends the information to the server.

[1526] Step 8:

[1527] The server automatically generates the application

[1528] The server analyzes the selected prompts, identifies the required data sources and analysis methods, and automatically generates program code (e.g., Python scripts) based on this information.

[1529] Step 9:

[1530] The server packages and serves the application

[1531] The server packages the automatically generated program code and generates a download link for the user, which is sent back to the device and displayed to the user.

[1532] Step 10:

[1533] Users download and use the application

[1534] Users click the provided link to download the application and run it on their device, for example, to open a Python script in Jupyter Notebook and perform data analysis.

[1535] Step 11:

[1536] Users provide feedback

[1537] After using an application, users input their evaluation and feedback into the system and submit it. The terminal then sends this to the server.

[1538] Step 12:

[1539] The server stores and updates feedback and ratings

[1540] The server stores the received feedback and ratings in a database and dynamically updates the rating scores of the prompts.

[1541] Step 13:

[1542] The server calculates the reward for the expert.

[1543] The server calculates the reward for the expert based on the evaluation score and number of times the prompt is used. The reward is notified to the expert and paid periodically.

[1544] These processing steps enable the automatic generation of high-quality applications based on prompts provided by experts, and the provision of these applications to users. This system benefits both users and experts, and promotes the accumulation of high-quality prompts.

[1545] Example 1

[1546] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1547] Currently, when trying to generate an application using prompts created by experts, the process is often manual and time-consuming and labor-intensive. Furthermore, the quality and feedback management of the generated application is left to the experts, resulting in a lack of uniformity. Furthermore, the compensation system for experts is unclear, which may lead to inadequate compensation. There is a need to resolve these issues and provide an efficient and fair system for both experts and users.

[1548] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1549] In this invention, the server includes: means for receiving prompts provided by experts; means for storing the prompts in a data storage device and classifying them by category; means for receiving user search queries and searching for and retrieving relevant prompts from the database; means for automatically generating software based on the retrieved prompts; means for providing the generated software to the user; means for receiving feedback and ratings from users, storing them in a data storage device, and updating the prompt ratings; and means for calculating and paying remuneration to experts based on the ratings and number of uses. This enables efficient management of prompts provided by experts and the provision of high-quality applications to users. Furthermore, since experts can be paid appropriately based on the feedback and ratings, a fair remuneration system can be established.

[1550] An "expert" is someone who has advanced knowledge and experience in a particular field and can create prompts related to that field.

[1551] A "prompt" is a text instruction written by an expert based on a specific task or objective, which the software then automatically generates.

[1552] A "data storage device" is a device for permanently storing information, including databases.

[1553] A "user" is a person who uses the system to obtain a specific application and execute it to achieve a purpose.

[1554] A "search query" is a string of characters that a user enters to search for required information from the database within the system.

[1555] "Software" means a collection of program code and associated files that are automatically generated based on prompts.

[1556] "Feedback" refers to the evaluations and opinions users provide after using an application, which are used to improve the system.

[1557] "Evaluation" refers to a quantitative scoring of the application used by the user, and the system compiles this to calculate the compensation paid to the expert.

[1558] "Reward" refers to compensation such as money or points paid to an expert based on the usage record and evaluation score of the prompt provided by the expert.

[1559] The present invention is a platform that automatically generates an application based on a prompt created by an expert and provides the application to a user, and is implemented with the following configuration.

[1560] The system consists of three main components: a server, a terminal, and a user. Here we will explain in detail how each component works and how it implements the invention.

[1561] Register an expert prompt

[1562] Server Processing

[1563] Experts use a web browser (e.g., a general-purpose web browser) to enter prompts through a web interface. The server receives the input data as HTTP requests and stores the prompts in a database (e.g., a general-purpose database management system). The prompts are then categorized according to specific domains or purposes.

[1564] User authentication and search prompts

[1565] Terminal handling

[1566] The user uses a web browser (e.g., a general-purpose web browser) on a device (e.g., a general-purpose personal computer) to enter their ID and password on the login screen. The device sends this information to the server, which then checks the authentication information against a database. If authentication is successful, the server provides the user with the home screen.

[1567] Automatically generate applications based on prompts

[1568] Server Processing

[1569] The server receives the prompt selected by the user and automatically generates software (e.g., a Python script) based on the prompt. For example, if a "Stock Analysis Prompt" is selected, the server identifies the stock price data API and analysis method, and generates a Python script based on that. This script adds lines to import necessary libraries (e.g., general-purpose libraries).

[1570] Application provision and use

[1571] Terminal handling

[1572] The application generated by the server is provided to the user's device as a download link. The user clicks the link to download the application and run it on their computer. For example, the user loads the "Stock Price Forecast.ipynb" file and uses Jupyter Notebook to perform analysis.

[1573] Feedback and Ratings

[1574] User Action

[1575] After using an application, users can rate and submit their feedback to the server. For example, if the application performs as expected, users can leave a comment such as "It's easy to use, but it's slow."

[1576] Server Processing

[1577] The server stores the received ratings and feedback in a database, updates the prompt's rating score, calculates the reward to the expert based on the rating and number of uses, and pays the reward in an appropriate way (e.g., bank transfer or points).

[1578] Examples of concrete examples and prompts

[1579] Examples:

[1580] Expert-submitted prompt: "Generate code to predict stock prices using Python"

[1581] User: Search for "Stock price prediction application"

[1582] Example of code generated by the server (specific code is not shown in this example)

[1583] Example prompt sentence:

[1584] "Generate prediction code using an LSTM model using AAPL stock price data in Python."

[1585] "Create a natural language processing prompt."

[1586] This system allows experts to receive fair compensation and enables users to easily use high-quality applications. In addition, since experts can be paid fairly based on feedback and evaluation, it is possible to build a fair compensation system.

[1587] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1588] Step 1:

[1589] Server Processing

[1590] The server receives prompts provided by experts, who use a web browser to enter the prompts into a web interface and submit them. The server receives the HTTP requests, stores them in a database, and categorizes them into categories.

[1591] Input: Expert-entered prompt and category information.

[1592] Output: Prompt and category information stored in a database.

[1593] Specific operation: The server saves the received prompt in text format in the database and adds category information using an SQL insert statement.

[1594] Step 2:

[1595] Terminal handling

[1596] The user accesses the login screen using the device's web browser and enters their ID and password, which is then sent to the server.

[1597] Input: The ID and password entered by the user.

[1598] Output: The authentication information sent to the server.

[1599] Specific operation: The user enters "user@example.com" and "password123" into the login form and clicks the login button.

[1600] Step 3:

[1601] Server Processing

[1602] The server checks the credentials against a database to verify them, and if successful presents the user with a home screen, otherwise it displays an error message.

[1603] Input: ID and password sent from the device.

[1604] Output: HTML of the home screen if authentication is successful, an error message if authentication fails.

[1605] What happens: The server uses an SQL query to check the authentication information in the database and generates an HTML response based on the authentication result.

[1606] Step 4:

[1607] Terminal handling

[1608] The home screen search interface allows users to enter keywords to search for a specific prompt, such as "stock price prediction."

[1609] Input: Keywords entered by the user into the search box on the home screen.

[1610] Output: The HTTP request with the keyword sent to the server.

[1611] Specific behavior: The user enters "stock price prediction" in the search box and clicks the search button.

[1612] Step 5:

[1613] Server Processing

[1614] The server searches the database based on the received search query, generates a list containing relevant prompts, and returns it to the user.

[1615] Input: User's search query (e.g. "stock price predictions").

[1616] Output: A list of the corresponding prompts in HTML format.

[1617] Specific operation: The server searches the database using an SQL query, generates the search results as an HTML list, and sends it to the user.

[1618] Step 6:

[1619] Terminal handling

[1620] The user selects a particular prompt from the search results and sends a request to the server.

[1621] Input: The prompt selected by the user.

[1622] Output: A prompt selection request is sent to the server.

[1623] Specific behavior: The user clicks on "Stock Price Prediction Prompt" that appears in the search results.

[1624] Step 7:

[1625] Server Processing

[1626] The server generates the application based on the selected prompts, for example by automatically generating a Python script to identify the required data sources and analysis methods.

[1627] Input: The user's prompt selection information.

[1628] Output: The generated Python script.

[1629] What it does: The server generates Python code based on the prompt and associated template, adding lines to import the necessary libraries.

[1630] Step 8:

[1631] Server Processing

[1632] Package the generated script and generate a URL from which the user can download it.

[1633] Input: The generated Python script.

[1634] Output: Download link.

[1635] Specific operation: The server compresses the script using a ZIP compression tool, generates a download URL, and provides it to the user.

[1636] Step 9:

[1637] Terminal handling

[1638] The user clicks on the download link to download the application and run it on their computer.

[1639] Input: Download link.

[1640] Output: The application downloaded to the device.

[1641] What happens: The user clicks on the provided link, downloads the ZIP file, unzips it, and opens it in Jupyter Notebook.

[1642] Step 10:

[1643] User Action

[1644] After using the application, the user inputs the evaluation and feedback and transmits them to the server.

[1645] Input: User ratings and feedback.

[1646] Output: Feedback information sent to the server.

[1647] Specific behavior: A user enters a score of "4" and a comment "Easy to use, but slow" in the evaluation form and clicks the submit button.

[1648] Step 11:

[1649] Server Processing

[1650] The server stores the received ratings and feedback in a database, updates the prompt's rating score, and calculates and pays the expert a reward based on the rating and number of uses.

[1651] Input: User ratings and feedback.

[1652] Output: Updated prompt evaluation scores and reward data for the expert.

[1653] Specific operation: The server updates the evaluation score, calculates the reward, and transfers the reward to the expert or awards points.

[1654] Through these steps, we can efficiently manage prompts based on the knowledge and experience of experts, provide high-quality applications to users, and properly reflect feedback and evaluations. This system will realize a fair and efficient application creation and reward system.

[1655] (Application example 1)

[1656] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1657] Managing the operation of robots in factories requires specialized knowledge and advanced skills. Furthermore, generating programs for rapid and effective response is extremely difficult, posing a major challenge for many companies. Current technology lacks a general-purpose means for easily generating and utilizing such programs. Therefore, in order to improve factory operational efficiency and rapidly put specialized knowledge to practical use, there is a need for an easy way to generate applications specialized for factory robots and to incorporate expert knowledge as feedback on an ongoing basis.

[1658] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1659] In this invention, the server includes means for receiving prompts provided by experts, means for saving the prompts in a database and categorizing them, means for receiving user search queries and searching for and retrieving relevant prompts from the database, means for automatically generating an application based on the retrieved prompts, means for providing the generated application to users, means for generating a program including operation and management of factory robots, means for providing the generated program to smartphones and tablet devices, means for receiving feedback and ratings from users, saving the feedback and ratings in a database and updating the evaluation scores of the prompts, and means for calculating and paying remuneration to experts based on the ratings and number of uses. This enables effective operation and management of factory robots and smooth practical application of expert knowledge.

[1660] An "expert" is someone who has advanced knowledge and experience in a particular field and can provide prompts.

[1661] A "prompt" is text information that provides guidelines or instructions for a specific task or purpose.

[1662] A "database" is a collection of information that centrally stores and manages multiple data sets and allows them to be searched and retrieved as needed.

[1663] A "category" is a group or segment for classifying and managing related prompts or data.

[1664] A "user" is a person who uses the system to perform prompt searches and generate applications.

[1665] A "search query" is a question or keyword that a user enters into a system to obtain specific information.

[1666] An "application" is a program that is automatically generated based on a prompt to perform a specific task.

[1667] A "factory robot" is a mechanical device used to automate and streamline work and operations in a factory.

[1668] A "smartphone" is a mobile device equipped with advanced communication functions and capable of running a wide variety of applications.

[1669] A "tablet device" is a mobile information terminal that has advanced communication functions similar to a smartphone and a larger screen.

[1670] "Feedback" is the act of providing the system with impressions and opinions after using an application.

[1671] The "rating score" is a numerical representation of the quality of a prompt or application based on user feedback.

[1672] "Remuneration" refers to compensation paid based on the usage and evaluation of the prompts provided by the expert.

[1673] "Program code" is text that contains a set of instructions for a computer to execute.

[1674] "Packaging" refers to the process of compiling the generated program code into a format that users can easily download and run.

[1675] "Route design" refers to a plan to optimize the movement routes and work sequences of robots within a factory.

[1676] The present invention is a system that automatically generates applications specialized for managing robot operations in factories by utilizing prompts based on the knowledge and experience of experts, and provides the applications to users. A specific embodiment of the present invention will be described below.

[1677] Overall system overview

[1678] This system is primarily composed of a server, user terminals, and smartphones or tablet terminals. The server is responsible for managing and processing various data, while the user terminal is responsible for communicating with the system and using applications.

[1679] Expert prompts provided and stored in a database

[1680] Experts provide prompts through a web interface. The server receives the prompts and efficiently stores them in a database. The prompts are categorized into categories such as "Data Analysis," "Natural Language Processing," and "Machine Learning," each of which is managed with its own index.

[1681] User authentication and search prompts

[1682] Users access the system from their devices and authenticate by entering their ID and password. If authentication is successful, the home screen is displayed. From here, users can enter a search query and search for and retrieve the corresponding prompt from the database. At this stage, the server analyzes the search query and provides the most appropriate prompt.

[1683] Automatically generate applications based on prompts

[1684] Based on the prompts selected by the user, the server automatically generates an application to support the operation and management of factory robots. This process involves generating program code using knowledge provided by experts to create a robot operation program that includes optimal control algorithms and flow line designs.

[1685] Application submission and feedback system

[1686] The generated application is packaged in a format that can be used on smartphones and tablets and provided as a download link. Users can click the link to download the application and run it on their device. After using the application, users send their feedback and rating to the server, which stores it in a database and updates the prompt's rating score.

[1687] Reward system for experts

[1688] The server periodically compiles the evaluation score and frequency of use of each prompt and calculates the reward for the expert based on that data. For example, an expert who provides a prompt with a high evaluation and frequency of use will be paid an appropriate reward.

[1689] Examples of concrete examples and prompts

[1690] Examples:

[1691] Experts design control algorithms for the effective operation of automated transport robots in factories and register these as prompts in the system. Users can log in using their smartphone app, select the appropriate prompt, and apply the automatically generated control program to the robot based on the prompt.

[1692] Example prompt sentence:

[1693] "An algorithm for designing efficient movement paths for automated transport robots in factories. Required fields: initial position of the robot, target position, and obstacle placement."

[1694] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1695] Step 1: Expert Prompts

[1696] Experts enter prompts through a web interface, which include guidelines and instructions for specific tasks. The server receives the prompts and stores them in a prompt database, where they are indexed and categorized by categories such as "data analysis," "natural language processing," and "machine learning."

[1697] Input: Prompt from an expert

[1698] Output: Prompts stored in the database

[1699] Step 2: User Authentication

[1700] The user accesses the system from their device and enters their ID and password for authentication. The device sends the authentication information to the server, which then verifies it. If authentication is successful, the home screen is displayed and the user can proceed to the next step.

[1701] Input: User ID, Password

[1702] Output: Authentication success or failure, home screen

[1703] Step 3: Find the prompt

[1704] A user enters a search query on the home screen and searches the prompt database. The server analyzes the search query and lists relevant prompts to provide to the user.

[1705] Input: Search query

[1706] Output: A list of applicable prompts

[1707] Step 4: Auto-generating the application

[1708] When a user selects a prompt from the search results, the server automatically generates an application based on that prompt. Specifically, appropriate program code is generated based on the prompt content. This program code includes control algorithms for factory robots and flow line designs.

[1709] Input:PromptSelect

[1710] Output: Auto-generated program code

[1711] Step 5: Serving the Application

[1712] The generated application is packaged in a format that can be used on smartphones and tablets and provided to the user as a download link, which the user can click to download the application and run it on their device.

[1713] Input: Generated program code

[1714] Output: Download link, application installed on device

[1715] Step 6: Gather feedback

[1716] After using the application, the user provides feedback and a rating based on the quality and usefulness of the application. The server receives this feedback and updates the rating score in the prompt database.

[1717] Input: Feedback, Rating

[1718] Output: Updated rating score

[1719] Step 7: Calculate the expert's fee

[1720] The server periodically compiles the evaluation score and frequency of use of each prompt, and calculates the reward for the expert based on that data. This reward is paid to the expert who provides the prompt with the highest evaluation and frequency of use.

[1721] Input: Evaluation score, number of uses

[1722] Output: Calculated reward, payment to the expert

[1723] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1724] The present invention improves the user experience by combining a platform that automatically generates applications based on prompts created by experts and provides those applications to users with an emotion engine that recognizes the user's emotions. The system of the present invention is implemented with the following configuration.

[1725] Register an expert prompt

[1726] Server Processing

[1727] Experts provide prompts based on their knowledge and experience through a web interface. The server receives the prompts and stores them in a database. The prompts are categorized according to specific fields or purposes. For example, prompts for data analysis and prompts for natural language processing are stored in separate categories.

[1728] User authentication and search prompts

[1729] Terminal handling

[1730] A user accesses the system using their own terminal and enters their ID and password on the login screen. The terminal sends this authentication information to the server. The server verifies the authentication information, and if authentication is successful, the user is shown the home screen. The home screen provides a search interface where users can search for prompts by entering categories or keywords.

[1731] Automatically generate applications based on prompts

[1732] Server Processing

[1733] The server receives the prompt selected by the user and begins the process of automatically generating an application based on that prompt. For example, if a "Stock Analysis Prompt" is selected, the server first identifies the required data source (e.g., stock price data API) and analysis method. Using this information, the server automatically generates a Python script and packages the script.

[1734] Application provision and use

[1735] Terminal handling

[1736] The server generates an application and provides it to the user as a download link. The user can click the link to download the application and run it on their computer. For example, they can open a downloaded Python script in Jupyter Notebook to analyze stock price data.

[1737] Feedback and Emotion Recognition

[1738] User Action

[1739] After using the application, users enter and submit their evaluations and feedback to the system. The emotion engine analyzes the user's emotions in real time using data such as text input, voice, and facial expressions. For example, when a user enters a text comment, the system analyzes the user's writing style and tone to infer their emotional state.

[1740] Server Processing

[1741] The server receives information from the emotion engine and evaluates the credibility of the feedback. It also adjusts the prompt's rating score based on the feedback and the user's emotional state and stores it in a database. For example, if the user expresses positive emotions, the rating will be set higher.

[1742] Reward System

[1743] Server Processing

[1744] The server periodically compiles the evaluation score and number of times each prompt is used, and calculates the reward for the expert based on that data. The reward is periodically notified to the expert and paid. For example, the data is compiled at the end of the month, and rewards are paid to experts who provide prompts with a rating of 4 or higher and that are used more than a certain number of times. This reward is awarded by bank transfer or as points.

[1745] Utilizing the Emotion Engine

[1746] Server Processing

[1747] The emotion engine is also utilized when the user selects a prompt. The server analyzes the user's emotional state and recommends prompts that suit the user's emotions. For example, if the user is feeling stressed, it can recommend a task that will have a relaxing effect, improving the user experience.

[1748] In this way, the system of the present invention not only automatically generates high-quality applications based on prompts provided by experts and provides them to users, but also improves the user experience by recognizing the user's emotions using an emotion engine and correcting the evaluation score and recommending prompts based on those emotions. This system brings great benefits to both users and experts and promotes the accumulation of high-quality prompts.

[1749] The processing flow will be explained below.

[1750] Program processing steps and detailed explanation

[1751] Step 1:

[1752] Experts register prompts

[1753] Using the web interface, the expert inputs a prompt based on their own knowledge and experience, then presses the send button, and the terminal sends the input prompt to the server.

[1754] Step 2:

[1755] The server receives and saves the prompt

[1756] The server receives prompts sent by experts, which are stored in a database and classified into specific categories (e.g., data analysis, natural language processing).

[1757] Step 3:

[1758] A user logs into the system

[1759] The user accesses the system from a terminal and enters their ID and password on the login screen. The terminal then sends this authentication information to the server.

[1760] Step 4:

[1761] The server authenticates the user

[1762] The server checks the received authentication information against a database and displays the home screen if authentication is successful, or returns an error message if authentication fails.

[1763] Step 5:

[1764] The user searches for the prompt

[1765] After logging in, the user uses the search interface to search for prompts by category or keyword. The terminal sends the search query to the server.

[1766] Step 6:

[1767] The server retrieves and returns the prompt

[1768] The server searches its database based on the received search query and returns a list of matching prompts to the terminal.

[1769] Step 7:

[1770] The user selects a prompt

[1771] The user selects the appropriate prompt from the list of returned prompts, and the terminal then sends the information to the server.

[1772] Step 8:

[1773] The server automatically generates the application

[1774] The server analyzes the selected prompts, identifies the required data sources and analysis methods, and automatically generates program code (e.g., Python scripts) based on this information.

[1775] Step 9:

[1776] The server packages and serves the application

[1777] The server packages the automatically generated program code and generates a download link for the user, which is sent back to the device and displayed to the user.

[1778] Step 10:

[1779] Users download and use the application

[1780] Users click the provided link to download the application and run it on their device, for example, to open a Python script in Jupyter Notebook and perform data analysis.

[1781] Step 11:

[1782] Users provide feedback

[1783] After using the application, users enter their evaluation and feedback into the system and submit it. The emotion engine also analyzes the user's text, voice, facial expressions, and other data. The device then sends this information to the server.

[1784] Step 12:

[1785] The server stores and updates feedback and ratings

[1786] The server receives information from the emotion engine, evaluates the credibility of the feedback based on it, and adjusts the prompt evaluation score based on the feedback content and emotional state, and stores the correct score in a database.

[1787] Step 13:

[1788] The server uses an emotion engine to recommend prompts

[1789] The server uses an emotion engine to analyze the user's emotional state in real time and recommends appropriate prompts to the user, for example, recommending a relaxing task to a user who is feeling stressed.

[1790] Step 14:

[1791] The server calculates the reward for the expert.

[1792] The server periodically compiles the evaluation score and the number of times each prompt is used, and calculates the reward for the expert based on that data. The reward is then notified to the expert and paid periodically.

[1793] These processing steps enable the automatic generation of high-quality applications based on prompts provided by experts and the provision of these applications to users. This system benefits both users and experts and promotes the accumulation of high-quality prompts. In addition, the use of an emotion engine makes it possible to provide a more personalized experience according to the user's emotional state.

[1794] Example 2

[1795] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1796] Previous systems were unable to consider the user's emotional state when automatically generating applications based on prompts provided by experts. This limited the improvement of the user experience, and the prompt evaluation and recommendation process relied on subjective evaluation. Furthermore, the reliability of the feedback could be reduced, making it difficult to evaluate the quality of the prompts.

[1797] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1798] In this invention, the server includes: means for receiving prompts provided by an expert; means for saving the prompts in a database and categorizing them; means for receiving a user's search query and searching for and retrieving relevant prompts from the database; means for automatically generating an application based on the retrieved prompts; means for providing the generated application to the user; means for receiving feedback and ratings from the user and saving them in a database and updating the prompt rating score; means for calculating and paying a fee to the expert based on the rating and the number of times the prompts are used; and means for analyzing the user's emotional state, evaluating the credibility of the feedback, and recommending prompts. This allows prompts to be evaluated and recommended based on the user's emotional state, improving the user experience and the quality of prompts through more credible feedback.

[1799] An "expert" is someone who has knowledge and experience in a particular field or purpose and can provide prompts based on that knowledge.

[1800] A "prompt" is text or data containing information or instructions necessary for the automatic generation of an application for a specific domain or purpose.

[1801] A "database" is a system for systematically storing and managing multiple data, and is used to store prompts and feedback.

[1802] "User" means a person who uses the system to search and select prompts and use the application.

[1803] "Emotional state" refers to a user's emotional or mood state, and is determined using text and voice analysis.

[1804] "Feedback" means information, including ratings and comments, provided by a User after using an Application.

[1805] The "evaluation score" is a numerical representation of the quality of the prompt or application based on feedback.

[1806] "Rewards" are monetary or point-based compensation provided to experts who contribute to the system.

[1807] "Application" means software or a program that is automatically generated based on prompts.

[1808] An "emotion engine" is an algorithm or system for analyzing a user's emotional state.

[1809] A "search query" is a keyword or condition that a user enters to search for prompts in a database.

[1810] "Auto-generation" refers to the process by which the system automatically generates an application or program based on prompts.

[1811] "Packaging" refers to the process of assembling the generated program code into an executable format.

[1812] "Credibility" refers to the degree to which the feedback or rating provided is accurate and trustworthy.

[1813] "Recommendation" refers to the act of suggesting appropriate prompts or applications based on the user's emotional state and past behavior.

[1814] The present invention relates to a platform for automatically generating applications based on prompts created by experts and providing those applications to users. This system is characterized by improving the user experience by combining an emotion engine.

[1815] Register an expert prompt

[1816] Server Processing

[1817] Experts enter prompts based on their knowledge and experience through a web interface, and the server receives the prompts, categorizes them, and stores them in a database, such as "prompts for data analysis" and "prompts for natural language processing."

[1818] As a specific example, an expert creates a "stock price prediction model" and registers the prompt in the system.

[1819] User authentication and search prompts

[1820] Terminal handling

[1821] A user accesses the system using their own terminal and enters their ID and password on the login screen. The terminal sends this authentication information to the server. The server verifies the authentication information, and if authentication is successful, the user is shown the home screen. The home screen has a search interface, and users can search for prompts by entering categories or keywords.

[1822] As a concrete example, consider a user who logs in and searches for "stock price predictions." The user enters the search keywords and gets relevant prompts.

[1823] Automatically generate applications based on prompts

[1824] Server Processing

[1825] Once the server receives the prompt selected by the user, it starts the process of automatically generating an application based on that prompt. For example, if a "Stock Analysis Prompt" is selected, the server will identify the required data source (e.g., stock data API) and analysis method, and generate Python scripts and JavaScript code.

[1826] As a specific example, an application is generated based on the prompt, "Generate a Python script for stock price prediction. The data source used will be the Yahoo Finance API, and the analysis method will be the machine learning random forest model."

[1827] Application provision and use

[1828] Terminal handling

[1829] The server generates an application and provides it to the user as a download link. The user can click the link to download the application and run it on their computer. For example, they can open a downloaded Python script in Jupyter Notebook to analyze stock price data.

[1830] Feedback and Emotion Recognition

[1831] User Action

[1832] After using the application, users enter their evaluation and feedback into the system and submit it. The emotion engine analyzes the user's emotions in real time using data such as text input, voice, and facial expressions. For example, when a user enters a text comment, the system analyzes the user's writing style and tone to infer their emotional state.

[1833] Server Processing

[1834] The server receives information from the emotion engine and evaluates the credibility of the feedback. It also adjusts the prompt's rating score based on the feedback and the user's emotional state and stores it in a database. For example, if the user expresses positive emotions, the rating will be set higher.

[1835] Reward System

[1836] Server Processing

[1837] The server periodically compiles the evaluation score and number of times each prompt is used, and calculates the reward for the expert based on that data. The reward is periodically notified to the expert and paid. For example, the data is compiled at the end of the month, and rewards are paid to experts who provide prompts with a rating of 4 or higher and that are used more than a certain number of times. This reward is awarded by bank transfer or as points.

[1838] Utilizing the Emotion Engine

[1839] Server Processing

[1840] The emotion engine is also utilized when the user selects a prompt. The server analyzes the user's emotional state and recommends prompts that suit the user's emotions. For example, if the user is feeling stressed, it can recommend a task that will have a relaxing effect, improving the user experience.

[1841] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1842] Step 1: Register an expert prompt

[1843] Server Processing

[1844] The expert opens an input screen on the web interface and enters the prompt content. The terminal sends this input data to the server. The server receives this data, performs validation (checks the input content, detects prohibited characters, etc.), and then saves the prompt in a database. The input is "prompt information" and the output is "saved prompt data."

[1845] Step 2: User authentication and home screen display

[1846] Terminal handling

[1847] The user enters their ID and password on the login screen, and the device sends this authentication information to the server. The server checks the authentication information against the user information in the database to see if they match. If authentication is successful, a home screen is generated and sent to the device. The input is "authentication information" and the output is "HTML data for the home screen."

[1848] Step 3: Find the prompt

[1849] Terminal handling

[1850] The user enters search keywords and categories on the home screen and clicks the "Search" button. The device sends the search query to the server. The server searches the database for relevant prompts and sends the results to the user's device. The input is the "search query" and the output is a "list of search result prompts."

[1851] Step 4: Auto-generate the application based on prompts

[1852] Server Processing

[1853] The user selects a prompt from the search results and sends that information from their device to the server. The server identifies the required data source (e.g., stock price data API) and analysis method based on the prompt content, and automatically generates Python scripts and JavaScript code. The generated code is packaged and provided as a download link. The input is the "selected prompt information," and the output is the "download link for the generated application."

[1854] Step 5: Serving and Using the Application

[1855] Terminal handling

[1856] When the user clicks the download link provided by the server, the application is downloaded to the device, and the user runs the downloaded application. For example, the user opens a Python script in Jupyter Notebook to analyze stock price data.

[1857] Step 6: Enter feedback and recognize emotions

[1858] User Action

[1859] After using the application, users enter their ratings and comments in a feedback form and send it from their device to the server. The server inputs the received feedback data into an emotion engine and performs text and voice analysis to estimate the user's emotional state. The input is "feedback data" and the output is "emotion analysis results."

[1860] Step 7: Process feedback and store it in a database

[1861] Server Processing

[1862] The server evaluates the credibility of the feedback based on the sentiment analysis results and stores the information in a database. It also corrects and updates the prompt's rating score. The input is the sentiment analysis results and feedback data, and the output is the updated rating score.

[1863] Step 8: Implementing the reward system

[1864] Server Processing

[1865] The server periodically compiles the prompt evaluation scores and number of uses in the database and calculates the rewards to the experts based on that data. The reward information is notified to the experts, and they are paid by bank transfer or as points. The input is "prompt evaluation scores and number of uses," and the output is "reward calculation results and notification."

[1866] Step 9: Prompt recommendation using the emotion engine

[1867] Server Processing

[1868] When a user tries to select a prompt on the home screen, the system analyzes past feedback and current input state to recommend an appropriate prompt. The emotion engine analyzes the user's emotional state in real time and suggests relaxing or stimulating tasks based on that. The input is "user feedback and current input state," and the output is "recommended prompt."

[1869] (Application example 2)

[1870] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1871] Conventional systems that automatically generate applications based on prompts from experts do not take user emotions into account, limiting the improvement of user experience. Furthermore, because no advertising optimization based on user emotions is performed, the effectiveness of advertising is limited. The present invention aims to further improve the user experience by analyzing user emotions in real time and generating optimal advertising based on that analysis.

[1872] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1873] In this invention, the server includes a means for receiving prompts provided by experts, a means for storing the prompts in a data storage device and categorizing them, and a means for receiving a user's search query and retrieving the corresponding prompt from the data storage device, thereby enabling real-time analysis of user sentiment, evaluation of the credibility of the feedback, and generation of optimal advertisements based on the sentiment analysis.

[1874] A "prompt" is a short instruction or question provided by an expert that serves as a cue for performing a specific task or analysis.

[1875] A "data storage device" is a storage device or database for storing, classifying, and managing data.

[1876] A "search query" refers to the operation or content of a user sending a request or question to a system.

[1877] "Auto-generation" refers to the process by which a system automatically creates an application or program based on input it receives.

[1878] "Feedback" refers to opinions and ratings provided by users after using a system or application.

[1879] The "evaluation score" is a numerical representation of the usefulness and performance of the system or prompt based on feedback.

[1880] "Remuneration" means the compensation paid to an expert for providing or using a prompt.

[1881] "Emotion analysis" is the process of detecting and recognizing emotions from user input and behavior and determining their state.

[1882] "Ad generation" refers to the process of automatically creating advertising content based on specific criteria and data.

[1883] The present invention is a system that improves user experience by combining a platform that automatically generates applications based on prompts provided by experts and provides them to users with an emotion engine that recognizes user emotions. The system of the present invention is implemented with the following specific configuration.

[1884] Register an expert prompt

[1885] The server receives prompts provided by experts and stores them in a data storage device. The saved prompts are classified into specific categories so that they can be easily searched and retrieved later. For example, various prompts based on the experts' knowledge, such as prompts for data analysis and natural language processing, are registered.

[1886] User authentication and search prompts

[1887] The device receives the user's authentication information and sends it to the server. The server verifies this information and performs authentication. If authentication is successful, the home screen is displayed. The user can use the search interface on the home screen to search for prompts by entering keywords or categories.

[1888] Automatically generate applications based on prompts

[1889] The server receives the prompt selected by the user and starts the automatic generation of the application based on the prompt. For example, if a prompt for stock price analysis is selected, the server identifies the required data source (e.g., stock price data API) and analysis method, and generates and packages a Python script based on the selected data.

[1890] Application provision and use

[1891] The generated application is provided to the user's device as a download link. The user can click the link to download and run the application. For example, a Python script can be opened in Jupyter Notebook to analyze stock price data.

[1892] Feedback and Emotion Recognition

[1893] After using an application, users enter their evaluation and feedback into the system and submit it. The device analyzes the user's input text, voice, and facial expressions, and recognizes emotions in real time using an emotion engine. For example, if a user enters "This application is very easy to use," the emotion engine will interpret this as a positive emotion.

[1894] Sentiment analysis and ad generation

[1895] The server evaluates the credibility of the feedback based on the emotional data obtained from the emotion engine. It also generates advertisements according to the user's emotions and displays them at the optimal time. For example, if a user is feeling stressed, it can display an advertisement with a relaxing effect.

[1896] Reward System

[1897] The server periodically compiles the evaluation score and the number of times each prompt is used, and calculates and pays a reward to the expert based on that data. For example, an expert who provides a prompt with a high evaluation score and a high number of uses is paid a predetermined reward.

[1898] Examples of concrete examples and prompts

[1899] For example, if a user provides feedback such as "This product is very easy to use, but the price is a little high," the emotional data can be analyzed based on that feedback, and an advertisement can be generated and displayed that highlights promotional offers for the price.

[1900] Examples of prompts provided by experts include:

[1901] Perform sentiment analysis of user feedback and generate ads that highlight the benefits of your product if the responses are mostly positive, or highlight discounts and offers if the responses are mostly negative.

[1902] The above is a detailed description of the mode for carrying out the present invention.

[1903] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1904] Step 1:

[1905] Experts submit prompts through a web interface.

[1906] Input: The text data of the prompt.

[1907] Specific operation: The expert inputs the prompt to be provided and sends it to the system. The server receives the prompt and stores it in the database. When storing the prompt, it classifies it into a specific category.

[1908] Step 2:

[1909] The terminal receives the user's authentication information and sends it to the server.

[1910] Input: User ID and password.

[1911] Specific operation: The user enters their ID and password on the login screen and clicks the submit button. The server verifies the authentication information, and if authentication is successful, the home screen is displayed; if it is unsuccessful, an error message is displayed.

[1912] Step 3:

[1913] The user searches for the prompt in the search interface on the home screen.

[1914] Input: Search query (keywords and / or categories).

[1915] Specific operation: The user enters keywords or categories in the search bar and presses the search button. The server searches the database for the corresponding prompt and displays the results.

[1916] Step 4:

[1917] The server automatically generates an application based on the prompts selected by the user.

[1918] Input: Selected prompt.

[1919] Specific operation: Based on the selected prompt, the server identifies the required data source (e.g., API) and analysis method, and automatically generates program code such as a Python script. The generated code is then packaged.

[1920] Step 5:

[1921] The server provides the generated application to the user.

[1922] Input: The generated program code.

[1923] Specific operation: The server provides the automatically generated application to the user's device as a download link. The user clicks on the link to download the application.

[1924] Step 6:

[1925] Users can use the downloaded application to check the results.

[1926] Input: The downloaded application.

[1927] Specific operation: The user runs the downloaded application (e.g., Python script) in their own environment. For example, they open the script in Jupyter Notebook and check the analysis results.

[1928] Step 7:

[1929] Users provide feedback after using the application and the server analyzes their emotions.

[1930] Input: Feedback text.

[1931] Specific operation: The user inputs feedback about the application and sends it to the server. The server uses an emotion engine to analyze the feedback content and recognize the emotion.

[1932] Step 8:

[1933] The server generates an advertisement based on the emotion data and displays it to the user.

[1934] Input: Parsed emotion data.

[1935] Specific operation: The server automatically generates optimal advertisements (e.g., promotional offers or advertisements with a relaxing effect) based on the analyzed emotional data. The generated advertisements are then displayed on the user's device.

[1936] Step 9:

[1937] The server evaluates the veracity of the feedback and updates the prompt's evaluation score.

[1938] Input: Feedback content and sentiment data.

[1939] Specific operation: The server adjusts the prompt's evaluation score based on the feedback content and emotion data, and stores it in the database. If there are more positive emotions, the score will increase, and if there are more negative emotions, the score will decrease.

[1940] Step 10:

[1941] The server periodically compiles the prompt ratings and usage counts and pays rewards to the experts.

[1942] Input: Prompt rating score and usage count.

[1943] Specific operation: The server periodically calculates the reward for the expert based on the evaluation score and the number of times the expert has been used. The reward is notified to the expert and paid in a predetermined format (bank transfer, points, etc.).

[1944] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1945] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1946] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1947] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1948] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1949] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1950] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1951] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1952] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1953] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1954] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1955] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1956] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1957] 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.

[1958] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1959] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1960] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1961] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1962] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1963] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1964] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1965] The following is further disclosed regarding the above embodiment.

[1966] (Claim 1)

[1967] a means for receiving prompts provided by an expert;

[1968] means for storing said prompts in a database and categorizing them;

[1969] means for receiving a user's search query and retrieving a corresponding prompt from said database;

[1970] means for automatically generating an application based on the obtained prompt;

[1971] a means for providing the generated application to a user;

[1972] means for receiving feedback and ratings from users and storing them in a database and updating the rating scores of said prompts;

[1973] a means for calculating and paying the expert's fees based on the evaluation and the number of uses;

[1974] A system including:

[1975] (Claim 2)

[1976] means for receiving and authenticating a user's authentication information;

[1977] a means for providing a home screen upon successful authentication;

[1978] a means for displaying an error message if authentication fails;

[1979] The system of claim 1 further comprising:

[1980] (Claim 3)

[1981] If the automatically generated application performs one of the following tasks: data analysis, natural language processing, or machine learning,

[1982] means for identifying the particular data sources and analytical techniques involved and generating program code based thereon;

[1983] means for packaging the generated program code and providing it in a format that can be downloaded by users;

[1984] 10. The system of claim 1, comprising:

[1985] "Example 1"

[1986] (Claim 1)

[1987] a means for receiving prompts provided by an expert;

[1988] means for storing and categorizing said prompts in a data storage device;

[1989] means for receiving a user's search query and retrieving a corresponding prompt from said database;

[1990] means for automatically generating software based on the obtained prompts;

[1991] a means for providing the generated software to users;

[1992] means for receiving and storing feedback and ratings from users in a data storage device and for updating the ratings of said prompts;

[1993] a means for calculating and paying the expert's fees based on the evaluation and the number of uses;

[1994] A system including:

[1995] (Claim 2)

[1996] means for receiving and authenticating a user's authentication information;

[1997] a means for providing a home screen upon successful authentication;

[1998] a means for displaying an error message if authentication fails;

[1999] The system of claim 1 further comprising:

[2000] (Claim 3)

[2001] If the automatically generated software performs one of the following tasks: data analysis, natural language processing, or machine learning,

[2002] means for identifying the particular data sources and analytical techniques involved and generating program code based thereon;

[2003] means for packaging the generated program code and providing it in a format that can be downloaded by users;

[2004] 10. The system of claim 1, comprising:

[2005] "Application Example 1"

[2006] (Claim 1)

[2007] a means for receiving prompts provided by an expert;

[2008] means for storing said prompts in a database and categorizing them;

[2009] means for receiving a user's search query and retrieving a corresponding prompt from said database;

[2010] means for automatically generating an application based on the obtained prompt;

[2011] a means for providing the generated application to a user;

[2012] A means for generating a program including operation management of a robot in a factory;

[2013] A means of providing the generated program to smartphones and tablet devices,

[2014] means for receiving feedback and ratings from users and storing them in a database and updating the rating scores of said prompts;

[2015] a means for calculating and paying the expert's fees based on the evaluation and the number of uses;

[2016] A system including:

[2017] (Claim 2)

[2018] means for receiving and authenticating a user's authentication information;

[2019] a means for providing a home screen upon successful authentication;

[2020] a means for displaying an error message if authentication fails;

[2021] The system of claim 1 further comprising:

[2022] (Claim 3)

[2023] If the automatically generated application performs one of the following tasks: data analysis, natural language processing, or machine learning,

[2024] means for identifying the particular data sources and analytical techniques involved and generating program code based thereon;

[2025] means for packaging the generated program code and providing it in a format that can be downloaded by users;

[2026] a means for supporting a user in efficiently operating a factory when the generated program includes a robot control algorithm and a robot flow line design;

[2027] 10. The system of claim 1, comprising:

[2028] "Example 2: Combining Emotion Engines"

[2029] (Claim 1)

[2030] a means for receiving prompts provided by an expert;

[2031] means for storing said prompts in a database and categorizing them;

[2032] means for receiving a user's search query and retrieving a corresponding prompt from said database;

[2033] means for automatically generating an application based on the obtained prompt;

[2034] a means for providing the generated application to a user;

[2035] means for receiving feedback and ratings from users and storing them in a database and updating the rating scores of said prompts;

[2036] a means for calculating and paying the expert's fees based on the evaluation and the number of uses;

[2037] means for analyzing the user's emotional state to assess the veracity of the feedback and recommend prompts;

[2038] A system including:

[2039] (Claim 2)

[2040] means for receiving and authenticating a user's authentication information;

[2041] a means for providing a home screen upon successful authentication;

[2042] a means for displaying an error message if authentication fails;

[2043] The system of claim 1 further comprising:

[2044] (Claim 3)

[2045] If the automatically generated application performs one of the following tasks: data analysis, natural language processing, or machine learning,

[2046] means for identifying the particular data sources and analytical techniques involved and generating program code based thereon;

[2047] means for packaging the generated program code and providing it in a format that can be downloaded by users;

[2048] a means for rating and recommending prompts based on emotional state;

[2049] 10. The system of claim 1, comprising:

[2050] "Application example 2 when combining emotion engines"

[2051] (Claim 1)

[2052] a means for receiving prompts provided by an expert;

[2053] means for storing and categorizing said prompts in a data store;

[2054] means for receiving a user's search query and retrieving a corresponding prompt from the data storage device;

[2055] means for automatically generating an application based on the obtained prompt;

[2056] a means for providing the generated application to a user;

[2057] means for receiving and storing user feedback and ratings in a data store and updating the rating score of said prompt;

[2058] a means for calculating and paying the expert's fees based on the eva...

Claims

1. a means for receiving prompts provided by an expert; means for storing said prompts in a database and categorizing them; means for receiving a user's search query and searching and retrieving a corresponding prompt from the database; means for automatically generating an application based on the obtained prompt; a means for providing the generated application to a user; means for receiving feedback and ratings from users and storing them in a database and updating the rating scores of said prompts; a means for calculating and paying the expert's fees based on the evaluation and the number of uses; A system including:

2. means for receiving and authenticating a user's authentication information; a means for providing a home screen upon successful authentication; a means for displaying an error message if authentication fails; The system of claim 1 further comprising:

3. If the automatically generated application performs one of the following tasks: data analysis, natural language processing, or machine learning, means for identifying the particular data sources and analytical techniques involved and generating program code based thereon; means for packaging the generated program code and providing it in a format that can be downloaded by users; The system of claim 1 , comprising:

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