System

Abstract

To provide a system.SOLUTION: A system includes: first means for receiving a first prompt sentence relating to generation of a text input by a user; second means for inputting the first prompt sentence into a generative AI model to generate the text; and third means for transmitting the generated text to a terminal of the user.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Legacy systems (technical debt) are a major challenge in promoting digital transformation (DX). Legacy systems prevent rapid response to digitalization, and because the systems are complex and black boxes, manual renewal is costly and time-consuming. [Means for solving the problem]

[0005] The present invention provides a reverse engineering means for analyzing the source code of a legacy system and a means for updating the analyzed source code. Furthermore, by providing a system including an AI means for automatically performing these operations, the legacy system can be updated efficiently and digital transformation can be promoted. [Brief description of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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. 2 is a sequence diagram showing a process flow of the data processing system in the first embodiment of the first form example. [Figure 12] FIG. 11 is a sequence diagram showing a process flow of the data processing system in the application example 1 of the first embodiment. [Figure 13] FIG. 11 is a sequence diagram showing a process flow of the data processing system in the second embodiment of the second embodiment. [Figure 14] FIG. 13 is a sequence diagram showing a process flow of the data processing system in application example 2 of embodiment 2. [Figure 15] FIG. 13 is a sequence diagram showing a process flow of a data processing system in a third embodiment of the present invention. [Figure 16] FIG. 13 is a sequence diagram showing a process flow of the data processing system in the third application example of the third embodiment. [Figure 17] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when combined with an emotion engine. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0007] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0011] 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.

[0012] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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 wide area network (WAN) and / or a local area network (LAN).

[0018] 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.

[0019] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. 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.

[0020] 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 (e.g., voice and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs voice according to instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

[0022] FIG. 2 shows an example of main functions of the data processing device 12 and the smart device 14.

[0023] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

[0024] 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.

[0025] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores a reception output program 60. The reception output program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads out the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] The present invention is a system including a reverse engineering means for analyzing the source code of a legacy system, a means for updating the analyzed source code, and an AI means for automatically performing these operations. The reverse engineering means receives the source code of the legacy system as an input and grasps its functions and structure. Specifically, it analyzes the function call relationships, variable usage, control flow, etc. Next, the source code updating means reconstructs the source code in a form that conforms to new technical standards and programming languages ​​based on the analysis results. Specifically, it converts from an old programming language to a new programming language, optimizes inefficient algorithms, deletes redundant code, etc. Finally, the AI ​​means performs these operations automatically. Specifically, it automates the reverse engineering and source code updating as a series of processes, minimizing human intervention. This makes it possible to efficiently update legacy systems and support the promotion of DX.

[0029] The process flow of each embodiment will be described below.

[0030] "Example 1"

[0031] Step 1: Input the source code of the legacy system into the reverse engineering tool. In this step, the source code of the legacy system is provided in a format that can be analyzed. For example, source code written in an old programming language such as COBOL or FORTRAN is the target.

[0032] Step 2: Reverse engineering techniques analyze the source code. In this step, the structure and functions of the source code are understood. Specifically, function call relationships, variable usage, control flow, etc. are analyzed.

[0033] Step 3: Input the analysis results to the source code innovation means: In this step, the information analyzed by the reverse engineering means is passed to the source code innovation means.

[0034] Step 4: The source code modernization tool generates new source code. In this step, the source code is restructured based on the analysis results in a way that makes it compatible with new technical standards and programming languages. Specifically, this involves converting old programming languages ​​to new ones, optimizing inefficient algorithms, and removing redundant code.

[0035] Step 5: AI means performs the above steps automatically. In this step, reverse engineering and source code renewal are automated as a series of processes, minimizing human intervention. This allows legacy systems to be renewed efficiently, helping to promote DX.

[0036] Example 1

[0037] Next, a description will be given of Example 1 of the embodiment 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".

[0038] In conventional systems, the process of generating appropriate text based on the prompt sentence entered by the user was complicated and difficult to perform efficiently. In addition, there was a lack of means to quickly return the generated text to the user, which resulted in a poor user experience. Furthermore, there was no interface for effectively utilizing the generative AI model, which created a high technical hurdle.

[0039] 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.

[0040] In this invention, the server includes a means for receiving a prompt sentence input by a user, a means for passing the received prompt sentence to a generative AI model, a means for the generative AI model to generate text based on the prompt sentence, and a means for returning the generated text to the user. This makes it possible to efficiently generate text based on the prompt sentence input by the user and quickly return it to the user.

[0041] A "user" is a person or organization that utilizes the system to enter prompts and receive generated text.

[0042] A "prompt" is a textual instruction or question that a user enters into a generative AI model.

[0043] A "generative AI model" is an artificial intelligence algorithm or system that generates natural language text based on a prompt.

[0044] The "server" is a computer system whose role is to receive a prompt from a user, pass it to a generative AI model, and return the generated text to the user.

[0045] An "HTTP request" is a request for data communication sent from a user's terminal to a server, and may include a prompt text.

[0046] An "HTTP response" is a data communication response sent from a server to a user's terminal, and may include generated text.

[0047] A "terminal" is a device used by a user to enter prompts and receive generated text, including a personal computer or a smartphone.

[0048] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0049] This invention is a system that generates text using a generative AI model based on a prompt sentence input by a user and returns the text to the user. A specific embodiment for implementing this system is described below.

[0050] Hardware and Software Used

[0051] Hardware: Terminals used by users (personal computers, smartphones, etc.), servers (equipped with high-performance CPUs and GPUs)

[0052] Software: Generative AI models (e.g., GPT-3®), database management systems (e.g., MySQL®), web servers (e.g., Apache®)

[0053] System Overview

[0054] The user uses a terminal to input a prompt sentence into an input form on a web browser. The input prompt sentence is sent to the server as an HTTP request. The server passes the received prompt sentence to the generative AI model, which then generates text based on the prompt sentence. The generated text is returned to the user's terminal via the server and displayed on the web browser.

[0055] Examples

[0056] User input: "Tell me about the city of the future."

[0057] Output of generative AI model: “Cities of the future will be powered by sustainable energy sources and run efficiently using smart technology.”

[0058] Examples of prompt statements

[0059] "Please tell me how AI is being used in the medical field."

[0060] "Please explain the next generation transportation system."

[0061] "Please tell me about the effects of climate change and how to combat them."

[0062] The system can efficiently generate text based on the prompt sentence entered by the user and quickly return it to the user, improving the user experience and effectively utilizing the generative AI model.

[0063] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0064] Step 1:

[0065] The user enters a prompt sentence.

[0066] A user opens a web browser and accesses an input form of the system. For example, the user inputs a prompt such as "Tell me about the city of the future." The input form has a text box where the user can input any text. The input data is the prompt.

[0067] Step 2:

[0068] The terminal sends a prompt to the server.

[0069] When the user clicks the "Send" button, the terminal sends the entered prompt text to the server as an HTTP POST request. This request includes the text data entered by the user. The input data is the prompt text, and the output data is the HTTP request.

[0070] Step 3:

[0071] The server receives the prompt.

[0072] The server receives the HTTP request and extracts the prompt from the request body. The server temporarily stores the prompt for further processing. The input data is the HTTP request, and the output data is the prompt.

[0073] Step 4:

[0074] The server passes the prompt to the generative AI model.

[0075] The server sends an API request to the generative AI model. This request includes the prompt text entered by the user. The server accesses the API endpoint of the generative AI model and passes the prompt text. The input data is the prompt text, and the output data is the API request.

[0076] Step 5:

[0077] A generative AI model generates the text.

[0078] The generative AI model generates text based on the received prompt. For example, it generates text such as "Future cities will be run efficiently using smart technology with sustainable energy sources." The generated text is returned to the server as an API response. The input data is the prompt, and the output data is the generated text.

[0079] Step 6:

[0080] The server receives the generated text.

[0081] The server receives the API response from the generative AI model and extracts the generated text. The server temporarily stores this text for further processing. The input data is the API response, and the output data is the generated text.

[0082] Step 7:

[0083] The server sends the generated text to the terminal.

[0084] The server sends the generated text to the user's terminal as an HTTP response, which contains the generated text. The input data is the generated text, and the output data is the HTTP response.

[0085] Step 8:

[0086] The terminal displays the generated text.

[0087] The user's device displays the received text on a web browser. The user can check the generated text on the browser. For example, the text "Cities of the future will be run efficiently using smart technology, powered by sustainable energy sources." is displayed. The input data is the HTTP response, and the output data is the displayed text.

[0088] (Application example 1)

[0089] Next, a description will be given of Application Example 1 of Form 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."

[0090] The process of analyzing and updating the source code of traditional legacy systems is often done manually, which takes time and effort. In addition, even in content generation using generative AI models, there is a lack of efficient means to automatically generate content based on prompts entered by users, and then display, save, and share it. This leads to issues such as a lack of improvement in the user experience and a decrease in the efficiency of the entire system.

[0091] 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. In this invention, the server includes a reverse engineering means for analyzing the source code of the legacy system, a means for updating the analyzed source code, an artificial intelligence means for automatically performing the above operations, a generation AI model means for generating content based on a prompt sentence input by the user, a means for displaying the generated content, and a means for saving and sharing the generated content. This makes it possible to automate the process of analyzing and updating the source code of the legacy system, and further to efficiently generate, display, save, and share content based on the prompt sentence input by the user.

[0092] A "legacy system" is an information system that was built using old technologies and programming languages ​​and is still in operation today.

[0093] "Source code" means textual code written in a programming language that describes the operation of a program.

[0094] "Reverse engineering methods" are methods for analyzing the structure and functions of existing systems or software and extracting the original design information.

[0095] "Modernization" refers to the restructuring of existing source code to accommodate new technical standards or programming languages.

[0096] "Artificial intelligence means" means means for automatically performing certain tasks using machine learning or data analysis.

[0097] A "prompt" is text that is input into a generative AI model and serves as the basis for the generated content.

[0098] A "generative AI model means" is an artificial intelligence model for generating content such as text, images, and audio based on an input prompt sentence.

[0099] A "means for displaying" is a means for visually presenting the generated content to a user.

[0100] "Storage and sharing means" means the means for storing the generated content in digital form and sharing it with other users and systems.

[0101] The following system configuration and processing procedure will be described as an embodiment of the present invention.

[0102] System Configuration

[0103] This system uses the following hardware and software:

[0104] Hardware: Server, user device (smartphone)

[0105] Software: Python(R), OpenAI(R) API

[0106] Process Overview

[0107] 1. Enter the prompt:

[0108] The user inputs a prompt sentence through a smartphone application, such as "A summer night's story" or "A cityscape of the future."

[0109] 2. Invoke the generative AI model:

[0110] The server receives the input prompt sentence and uses the OpenAI API to call a generative AI model (e.g., GPT-4 (registered trademark)). The generative AI model generates text based on the prompt sentence.

[0111] 3. Content generation:

[0112] A generative AI model generates text based on the prompt, which corresponds to the prompt entered by the user.

[0113] 4. Viewing Content:

[0114] The server sends the generated text to the user's terminal and displays it to the user through a smartphone application.

[0115] 5. Save and Share:

[0116] Users can save the generated content on their smartphones and can also share it with other users via social media.

[0117] Examples

[0118] For example, if a user enters the prompt "A Midsummer Night's Tale," the generative AI model will generate a story based on the prompt, like this:

[0119] "One summer night, under the stars, a boy sets out on a mysterious adventure. He finds a glowing stone in the forest and discovers that it has magical powers."

[0120] The generated story is displayed on the user's smartphone, where they can save it or share it on social media.

[0121] Similarly, if you input the prompt "future cityscape," the generative AI model will generate the following text:

[0122] "In the city of the future, we see floating buildings and self-driving cars. People use the latest technology to live comfortable lives."

[0123] In this way, the system can generate, display, save, and share a variety of content based on the prompt text entered by the user.

[0124] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0125] Step 1:

[0126] A user starts an application on a smartphone and inputs a prompt sentence. The input prompt sentence is sent to the server in text format. The input data is the prompt sentence input by the user, and the output data is the prompt sentence sent to the server.

[0127] Step 2:

[0128] The server analyzes the received prompt text and converts it into an appropriate format to be passed to the generative AI model. The input data is the prompt text sent by the user, and the output data is the prompt text to be passed to the generative AI model. Specific operations include preprocessing the text (removing unnecessary spaces and converting it to a specific format).

[0129] Step 3:

[0130] The server uses the OpenAI API to call the generative AI model and generate content based on the prompt sentence. The input data is the preprocessed prompt sentence, and the output data is the generated content (text). The specific operation is to send an API request and receive the generated text.

[0131] Step 4:

[0132] The server transmits the generated content to the user terminal. The input data is the content received from the generative AI model, and the output data is the content to be transmitted to the user terminal. The specific operation is to convert the text data into an appropriate format and transmit it to the user terminal via the network.

[0133] Step 5:

[0134] The user terminal displays the received content within the application. The input data is the content sent from the server, and the output data is the content displayed to the user. Specifically, the text data is applied to a layout for display on the screen and visually presented to the user.

[0135] Step 6:

[0136] The user can save or share the generated content. The input data is the displayed content, and the output data is a saved file or a shared link. Specifically, by pressing the save button or the share button, the content can be saved in the device or shared with other users via SNS, etc.

[0137] Example 2

[0138] Next, a second embodiment of the second embodiment will be described. 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."

[0139] Conventional systems require users to manually create programs to process or visualize specific data, which is time-consuming and labor-intensive. In addition, creating programs requires specialized knowledge, making it difficult for general users.

[0140] 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.

[0141] In this invention, the server includes a means for receiving an instruction statement input by a user, a generating AI means for generating a program based on the received instruction statement, a means for executing the generated program, and a means for returning the execution result to the user. This enables a user to easily generate a program for data processing and visualization and obtain the execution result without specialized knowledge.

[0142] A "user" is an individual or group that utilizes the system to input instructions and obtain results of data processing and visualization.

[0143] "Instructions" are textual instructions entered by a user into the system, including requests for specific data processing or visualization.

[0144] A "generative AI means" is a means that uses artificial intelligence technology to automatically generate appropriate program code based on an instruction text.

[0145] "Program" means a set of instructions generated by a generative AI means for performing certain data processing or visualization tasks.

[0146] The "execution means" refers to a means for executing the generated program, acquiring data from the database, processing the data, and visualizing it.

[0147] A "database" is a system for systematically storing and managing data, and stores user information and other data.

[0148] "Data processing" is the process of converting, filtering, and aggregating acquired data based on specific conditions.

[0149] "Visualization" is the process of displaying the results of data processing in a visual format such as a graph or chart.

[0150] The "execution results" are the results of data processing and visualization obtained by executing the generated program, and are returned to the user.

[0151] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0152] This invention is a system that automatically generates a program using a generative AI model based on a command statement input by a user, and executes the program to process and visualize data. A specific embodiment of this system will be described below.

[0153] System Configuration

[0154] The system mainly consists of the following components:

[0155] 1. User interface: An interface for users to input instructions. It uses a terminal such as a web browser.

[0156] 2. Server: Receives instructions, generates a program using a generative AI model, and executes the generated program.

[0157] 3. Generative AI model: An artificial intelligence model that generates programs based on instructions. Specifically, we use models that use natural language processing technology (e.g., GPT-4).

[0158] 4. Database: A system for storing data and retrieving it from a server, for example using a relational database management system such as MySQL.

[0159] 5. Data processing library: A software library for processing data. Specifically, we use the Python Pandas library (registered trademark).

[0160] 6. Visualization library: A software library for visualizing data as graphs and charts. Specifically, it uses the Python Matplotlib library (registered trademark).

[0161] Creating and running a program

[0162] A user inputs instructions through the system's web interface, for example, "Generate a program that displays a graph of the age distribution of users." The server sends the instructions to the generative AI model, which generates the appropriate program code.

[0163] The generated program does the following:

[0164] 1. Database connection: The server connects to the database and gets the required data.

[0165] 2. Data processing: The acquired data is converted into a data frame using the Pandas library, and then filtered and aggregated based on certain conditions.

[0166] 3. Data visualization: The processed data is visualized as graphs and charts using the Matplotlib library.

[0167] Examples

[0168] For example, if a user types in "Please generate a program that will graph the age distribution of our users," the server might do the following:

[0169] 1. The server uses a generative AI model to generate a program based on the user's instructions.

[0170] 2. The generated program retrieves user age data from the database and calculates the age distribution using the Pandas library.

[0171] 3. The calculated age distribution is visualized as a bar graph using the Matplotlib library.

[0172] 4. The server saves the generated graph as an image file and returns the file to the user.

[0173] In this way, users can easily create programs for data processing and visualization and obtain execution results, even without specialized knowledge.

[0174] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0175] Step 1:

[0176] The user enters a prompt sentence.

[0177] The user accesses the system's web interface and types in the text box, "Please generate a program that will display a graph showing the age distribution of users." Then, he clicks the "Submit" button.

[0178] Input: The prompt text entered by the user.

[0179] Output: The prompt text sent to the server.

[0180] Step 2:

[0181] The server generates the program using the generative AI model.

[0182] The server receives user input and sends prompt sentences to the generative AI model, which generates appropriate program code based on the prompt sentences.

[0183] Input: The prompt text sent by the user.

[0184] Output: The generated program code.

[0185] Step 3:

[0186] The server executes the generated program.

[0187] The server executes the generated program code. First, it connects to the database and gets the user's age data. Then, it converts the data into a data frame using the Pandas library and calculates the age distribution. Finally, it visualizes the age distribution as a bar graph using the Matplotlib library and saves it as an image file.

[0188] Input: The generated program code.

[0189] Output: Age distribution graph image file.

[0190] Step 4:

[0191] The server returns the execution result to the user.

[0192] The server returns the generated graph image to the user, who can download and view it through the system's web interface.

[0193] Input: Age distribution graph image file.

[0194] Output: The graph image file that is returned to the user.

[0195] (Application example 2)

[0196] Next, a description will be given of Application Example 2 of Form 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."

[0197] The traditional source code analysis and modernization process for legacy systems is often manual, time-consuming and labor-intensive. In addition, there is a lack of efficient means to generate personalized content based on prompts entered by users. This makes it difficult to quickly respond to user needs.

[0198] 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.

[0199] In this invention, the server includes a reverse engineering means for analyzing the source code of the legacy system, a means for updating the analyzed source code, a means including a generative AI model for automatically performing the above operations, a means for generating content based on a prompt sentence input by a user, and a means for displaying the generated content. This makes it possible to automate the source code analysis and updating process of the legacy system and to rapidly generate personalized content according to the needs of the user.

[0200] A "legacy system" is an information system that was built using old technologies or platforms and is still in use today.

[0201] "Source code" means textual code written in a programming language that describes the operation of a program.

[0202] "Reverse engineering" is the process of analyzing the structure and function of an existing system or software to understand its design and operation.

[0203] A "generative AI model" is an artificial intelligence model that generates new content based on given input data (prompt text).

[0204] A "prompt" is text that is input to a generative AI model to cause it to generate a particular output.

[0205] "Content" means any information or data made available to Users and may be in the form of text, images, video or other formats.

[0206] "Modernization" means restructuring existing source code to conform to new technical standards and programming languages.

[0207] "Automation" means that systems or processes are carried out automatically by machines or software with minimal human intervention.

[0208] "Personalization" means customizing content and services according to a user's individual needs and preferences.

[0209] "Display" refers to visually presenting the generated content to a user.

[0210] A system for implementing the present invention is configured as follows: A server includes reverse engineering means for analyzing source code of a legacy system, means for updating the analyzed source code, means including a generative AI model for automatically performing the above operations, means for generating content based on a prompt sentence input by a user, and means for displaying the generated content.

[0211] Program processing explanation

[0212] The server first analyzes the source code of the legacy system using reverse engineering techniques. This analysis allows the system's functions and structure to be understood. It then uses techniques to modernize the analyzed source code, reconstructing it in a way that complies with new technical standards and programming languages. This series of operations is automated using a generative AI model.

[0213] When a user inputs a prompt sentence through a terminal, the server uses a generative AI model to generate content based on the prompt sentence, and the generated content is provided to the user through the display means of the terminal.

[0214] Hardware and Software Used

[0215] Hardware: Servers, user devices (smartphones, tablets, PCs, etc.)

[0216] Software: Python, generative AI models (e.g. GPT-3), reverse engineering tools, source code renovation tools

[0217] Examples

[0218] For example, if a user enters the prompt "Generate a blog post about recent technology trends," the server will use a generative AI model to generate content like this:

[0219] Example prompt:

[0220] Generate blog posts about recent technology trends.

[0221] Examples of generated content:

[0222] Recent technology trends include artificial intelligence (AI), blockchain, 5G communications, and quantum computing. These technologies have the potential to dramatically change our daily lives and the way we do business. For example, AI plays a key role in medical diagnosis and the development of self-driving cars. Blockchain is attracting attention as a technology that will improve the transparency and security of financial transactions. 5G communications will provide faster and more reliable Internet connections, facilitating the widespread adoption of IoT devices. Quantum computing is expected to provide a new means of solving complex problems that cannot be solved by conventional computers.

[0223] In this way, personalized content that meets the user's needs can be quickly generated and provided.

[0224] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0225] Step 1:

[0226] The user enters a prompt through the terminal.

[0227] Input: A prompt entered by the user (e.g. "Generate a blog post about recent technology trends.")

[0228] Output: The prompt text is sent to the server.

[0229] Specific operation: The user inputs a prompt into the input field of the terminal and presses the send button. The terminal sends this prompt to the server.

[0230] Step 2:

[0231] The server receives the prompt text and passes it to the generative AI model.

[0232] Input: Prompt text sent from the terminal

[0233] Output: The prompt is fed into the generative AI model.

[0234] Specific operation: The server analyzes the prompt text received from the terminal and sends a request to the API of the generative AI model.

[0235] Step 3:

[0236] A generative AI model generates content based on the prompt.

[0237] Input: Prompt text passed from the server

[0238] Output: The generated content (e.g. blog post)

[0239] How it works: The generative AI model analyzes the prompt and generates text based on relevant information, which is then returned to the server.

[0240] Step 4:

[0241] The server receives the generated content and transmits it to the terminal.

[0242] Input: Generated content returned from a generative AI model

[0243] Output: Generated content is sent to a device.

[0244] Specific operation: The server converts the content received from the generative AI model into a data format for transmission to the terminal and sends it to the terminal.

[0245] Step 5:

[0246] The device displays the generated content.

[0247] Input: Generated content sent from the server

[0248] Output: The generated content is displayed to the user.

[0249] Specific operation: The device renders the content received from the server in the display area and provides it visually to the user.

[0250] In this way, a series of processes is realized in which content is generated using a generative AI model based on the prompt sentence entered by the user and provided to the user.

[0251] Furthermore, an emotion engine that estimates the emotion of the user may be combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[0252] "Example 1"

[0253] In one embodiment of the present invention, the AI ​​means includes an emotion engine that recognizes the user's emotion. The emotion engine estimates the user's emotional state from the user's facial expression, tone of voice, choice of words, etc. For example, if the user feels irritated, the emotion engine communicates the information to the AI ​​means. The AI ​​means uses this information to adjust the priority of source code renewal. Specifically, if the user feels irritated, the AI ​​means prioritizes renewal of the source code of the part that the user is interested in. In addition, the emotion engine reports the progress of the source code renewal according to the user's emotional state. For example, if the user feels calm, the emotion engine communicates the information to the AI ​​means, and the AI ​​means provides the user with a detailed report of the progress. This provides appropriate feedback according to the user's emotional state, improving the user experience.

[0254] The process flow of each embodiment will be described below.

[0255] "Example 1"

[0256] Step 1: The emotion engine infers the user's emotional state from their facial expressions, tone of voice, choice of words, etc.

[0257] Step 2: The emotion engine communicates the estimated emotional state to the AI ​​means.

[0258] Step 3: The AI ​​means adjusts the priority of source code updating using the information received from the emotion engine. Specifically, if the user feels frustrated, the AI ​​means prioritizes updating the source code of the part that the user is interested in.

[0259] Step 4: The emotion engine reports the progress of the source code update according to the user's emotional state. For example, if the user feels calm, the emotion engine conveys that information to the AI ​​means, and the AI ​​means provides the user with a detailed report of the progress.

[0260] Example 1

[0261] Next, a description will be given of Example 1 of the embodiment 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".

[0262] Conventional methods for analyzing and updating source code of legacy systems required a lot of manual operations and were inefficient. In addition, even text generation systems using generative AI models lacked the means to properly process user input and effectively use the generated text. This made it difficult to respond to user requests quickly and accurately.

[0263] The identification process by the identification processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes a reverse engineering means for analyzing the source code of the legacy system, a means for updating the analyzed source code, a means for receiving a prompt sentence input by a user, a means for preprocessing the received prompt sentence, a means for inputting the preprocessed prompt sentence into a generative AI model and acquiring the generated text, a means for post-processing the generated text, and an AI means for automatically performing the above operations. This enables efficient analysis and updating of the legacy system and automation of text generation using the generative AI model.

[0264] A "legacy system" is an information system that has been in use for a long time but is still in operation today.

[0265] "Source code" refers to textual code that describes the operation of a program.

[0266] "Reverse engineering" is a technique for analyzing the structure and operation of existing systems or software and extracting their design information.

[0267] A "prompt" is text that is entered as an instruction or question into a generative AI model.

[0268] "Preprocessing" refers to the process of converting data into an appropriate format before inputting it into a generative AI model.

[0269] A "generative AI model" is a model that uses artificial intelligence to generate text and data.

[0270] "Post-processing" refers to the process of converting the data output from a generative AI model into a format that is easy for humans to understand.

[0271] "AI means" refers to means of automating specific operations or processes using artificial intelligence technology.

[0272] This invention combines a system for analyzing and updating the source code of a legacy system with a system for generating text based on a prompt sentence entered by a user using a generative AI model. A specific embodiment of this system is shown below.

[0273] The server uses reverse engineering techniques to analyze the source code of the legacy system. The reverse engineering techniques are techniques for analyzing the structure and operation of existing systems and software and extracting their design information. The analyzed source code is reconstructed by modernization techniques in a form that conforms to new technical standards and programming languages.

[0274] A user inputs a prompt into an input field on the device, for example, "Use AI to generate a poem about spring." The device sends this prompt to the server, which then preprocesses the received prompt and converts it into a format suitable for the generative AI model. Preprocessing includes tokenizing and normalizing the text.

[0275] The server inputs the preprocessed prompt sentence into a generative AI model. This generative AI model uses, for example, a general generative AI model. The generative AI model generates new text based on the input prompt sentence. The generated text is returned to the server.

[0276] The server post-processes the generated text and converts it into a human-readable form, which includes decoding and formatting the generated text, and sends the post-processed text to the terminal for display to the user.

[0277] For example, if a user inputs "Use AI to generate a poem about spring," the server inputs this prompt into the generative AI model and generates a poem about spring. The generated poem is displayed to the user.

[0278] This system realizes efficient analysis and renovation of legacy systems and automation of text generation using generative AI models. The hardware used is a high-performance GPU (e.g., a general high-performance GPU), and the software uses Python and TENSORFLOW (registered trademark).

[0279] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0280] Step 1:

[0281] The user enters a prompt sentence.

[0282] The user inputs a prompt for the generative AI model into the input field of the terminal. For example, the user inputs, "Please use AI to generate a poem about spring." The input prompt becomes the input data for the terminal.

[0283] Step 2:

[0284] The terminal sends a prompt to the server.

[0285] The terminal sends the prompt text entered by the user to the server using an HTTP request. The input is the prompt text entered by the user, and the output is the prompt text sent to the server.

[0286] Step 3:

[0287] The server receives the prompt and performs data preprocessing.

[0288] The server receives the prompt sentence sent from the terminal. It tokenizes the received prompt sentence and converts it into a format suitable for the generative AI model. Specifically, it splits the text into words and subwords and converts it into a numerical vector. The input is the prompt sentence received from the terminal, and the output is the preprocessed numerical vector.

[0289] Step 4:

[0290] The server inputs a prompt sentence into the generative AI model and obtains the generated text.

[0291] The server inputs the preprocessed prompt sentence into the generative AI model. The generative AI model generates new text based on the input prompt sentence. The generated text is returned to the server. The input is the preprocessed numeric vector, and the output is the generated text.

[0292] Step 5:

[0293] The server post-processes the generated text and sends it to the terminal.

[0294] The server post-processes the text returned by the generative AI model. Specifically, it decodes the generated text and converts it into a human-readable format. It then sends the generated text to the terminal. The input is the text returned by the generative AI model, and the output is the post-processed text.

[0295] Step 6:

[0296] The terminal displays the generated text to the user.

[0297] The terminal displays the generated text received from the server to the user, who can then review the generated poem or piece of writing. The input is the post-processed text received from the server, and the output is the text displayed to the user.

[0298] (Application example 1)

[0299] Next, a description will be given of Application Example 1 of Form 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."

[0300] The process of analyzing and updating the source code of traditional legacy systems is very time-consuming and labor-intensive if done manually. In addition, there is a lack of efficient means to generate personalized content based on prompts entered by users. This makes it difficult to respond quickly to user needs.

[0301] 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.

[0302] In this invention, the server includes a reverse engineering means for analyzing the source code of the legacy system, a means for updating the analyzed source code, an artificial intelligence means for automatically performing the above operations, a generative AI model means for generating content based on a prompt sentence input by a user, and a means for displaying the generated content to a user. This enables efficient analysis and updating of the legacy system, and further enables rapid generation and provision of personalized content based on the user's prompt sentence.

[0303] "Reverse engineering methods" are methods for analyzing the source code of a legacy system and understanding its functions and structure.

[0304] "Innovation measures" are measures to reconstruct the analyzed source code in a way that complies with new technical standards or programming languages.

[0305] "Artificial intelligence means" means means for automating reverse engineering and source code renovation operations.

[0306] A "generative AI model means" is a means for generating content based on a prompt sentence entered by a user.

[0307] The "display means" refers to a means for displaying the generated content to the user.

[0308] The following system configuration and processing procedure will be described as an embodiment of the present invention.

[0309] The server includes reverse engineering means for analyzing source code of the legacy system, means for updating the analyzed source code, artificial intelligence means for automatically performing the above operations, generative AI model means for generating content based on a prompt sentence input by a user, and means for displaying the generated content to a user.

[0310] Specifically, the server is implemented using a Python program and the OpenAI API. The reverse engineering means analyzes the source code of the legacy system to understand its functions and structure. The analysis results are reconstructed by the modernization means in a form that complies with new technical standards and programming languages. These operations are automated by the artificial intelligence means.

[0311] When a user inputs a prompt sentence using the smartphone, the prompt sentence is sent to the generative AI model means. The generative AI model means generates content based on the prompt sentence using a generative AI model (e.g., text-davinci-003) of OpenAI. The generated content is displayed to the user by the display means.

[0312] For example, if a user inputs "I want to write a blog about my travel memories. Please write in detail about my trip to Okinawa with my family last summer," the generative AI model means will generate a detailed blog post based on this prompt. The generated blog post will be displayed on the smartphone screen for the user to check.

[0313] The system enables efficient analysis and modernization of legacy systems, as well as the rapid generation and delivery of personalized content based on user prompts.

[0314] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0315] Step 1:

[0316] A user inputs a prompt sentence using a smartphone. The input prompt sentence is sent to a server. The input data is the prompt sentence in text format input by the user.

[0317] Step 2:

[0318] The server passes the received prompt sentence to the generative AI model means. The generative AI model means analyzes the prompt sentence and processes the data to generate appropriate content. Specifically, it uses an OpenAI generative AI model (e.g., text-davinci-003 (registered trademark)) to generate text based on the prompt sentence. The input data is the prompt sentence, and the output data is the generated content.

[0319] Step 3:

[0320] The content generated by the generative AI model means is temporarily stored in the server. The server passes the generated content to the display means. The input data is the generated content, and the output data is the content passed to the display means.

[0321] Step 4:

[0322] The display means displays the generated content on the user's smartphone. The user can check the generated content on the screen of the smartphone. The input data is the content passed to the display means, and the output data is the content displayed on the screen of the smartphone.

[0323] Step 5:

[0324] The user can check the displayed content and modify or save it as necessary. The input data is the content displayed on the smartphone screen, and the output data is the content modified or saved by the user's operation.

[0325] In this way, a series of processes is realized in which personalized content is generated using a generative AI model based on the prompt sentence entered by the user and provided to the user.

[0326] Example 2

[0327] Next, a second embodiment of the second embodiment will be described. 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."

[0328] The source code analysis and renovation process of conventional legacy systems is often done manually, which takes time and effort. In addition, even in text generation systems using generative AI models, the process from when the user enters a prompt sentence to when they receive the generated text is complicated, making it difficult to perform efficiently. To solve these issues, it is necessary to automate the source code analysis and renovation process and to streamline the text generation process using generative AI models.

[0329] 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.

[0330] In this invention, the server includes a reverse engineering means for analyzing the source code of the legacy system, a means for updating the analyzed source code, an artificial intelligence means for automatically performing the above operations, a means for a user to input a prompt sentence, a means for a terminal to send the prompt sentence to the server, a means for the server to call a generative AI model, a means for the generative AI model to generate text, a means for the server to send the generated text to the terminal, and a means for the terminal to display the generated text. This makes it possible to automate the source code analysis and updating process and to streamline the text generation process using the generative AI model.

[0331] "Legacy systems" refers to existing, older information systems or software that are typically not aligned with new technologies and standards.

[0332] "Source code" means textual code written in a programming language that describes the operation of a program.

[0333] "Reverse engineering" is a technique for analyzing the structure and function of an existing system or software to understand its design and operation.

[0334] "Artificial intelligence" refers to computer systems and algorithms that mimic human intelligence and can learn, reason, and solve problems.

[0335] A "prompt sentence" is an instruction entered by a user to request a generative AI model to generate specific text.

[0336] "Terminal" refers to a device such as a computer or smartphone operated by a user.

[0337] A "server" is a computer system that provides services to other computers and devices over a network.

[0338] A "generative AI model" is an artificial intelligence model that generates text based on an input prompt.

[0339] "Text generation" is the process by which a generative AI model creates new text based on a given prompt.

[0340] An "HTTP request" is a protocol that allows a client (terminal) to request a server to send data or obtain information.

[0341] The "JSON format" is a lightweight data exchange format for expressing data in text format.

[0342] An "API Request" is a request to other software or services to perform a function through an application program interface.

[0343] "HTML" is a markup language for constructing web pages.

[0344] "UI components" refers to the individual elements or parts that make up a user interface.

[0345] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0346] The present invention is a system for automating the source code analysis and renewal process of a legacy system and for streamlining the text generation process using a generative AI model. A specific embodiment of the system will be described below.

[0347] Hardware and Software Used

[0348] 1. Server

[0349] The server includes reverse engineering and artificial intelligence means for analyzing and updating the source code of the legacy system, and is capable of handling API requests for invoking generative AI models and generating text.

[0350] 2. Terminal

[0351] A terminal is a device where a user inputs a prompt text and sends it to a server. Specifically, this applies to PCs and smartphones.

[0352] 3. Generative AI Models

[0353] A generative AI model is an artificial intelligence model that generates text based on a prompt sentence when called by a server, such as OpenAI's GPT-3.

[0354] Data processing and calculation

[0355] 1. Enter and send a prompt

[0356] A user inputs a prompt through the terminal interface, for example, "Write a short story about a cat." The terminal sends this prompt as an HTTP request to the server.

[0357] 2. Parsing the prompt and calling the generative AI model

[0358] The server analyzes the received prompt sentence and calls the generative AI model. Specifically, it generates an API request including the prompt sentence and sends it to the endpoint of the generative AI model.

[0359] 3. Generating text and sending a response

[0360] The generative AI model generates text based on the input prompt. For example, in response to the prompt "Write a short story about a cat," it generates the text "Once upon a time, there lived a cat in a small village..." The generated text is returned to the server as an API response.

[0361] 4. Viewing the generated text

[0362] The server sends the text received from the generative AI model to the user's device, which displays the received text on the screen so that the user can check it.

[0363] Examples

[0364] Examples of prompt statements

[0365] "Write a short story about a cat."

[0366] "Write a poem about the city of the future."

[0367] "Please explain how AI will change human life."

[0368] In this way, users can use generative AI models to generate various texts. This system automates the source code analysis and renovation process of legacy systems, and streamlines the text generation process using generative AI models.

[0369] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0370] Step 1:

[0371] The user enters a prompt sentence.

[0372] The user inputs a prompt through the terminal interface, for example, "Write a short story about a cat" in the text input field and clicks the send button. The input prompt is temporarily stored in the terminal's memory.

[0373] Step 2:

[0374] The terminal sends a prompt to the server.

[0375] The terminal sends the prompt text entered by the user to the server as an HTTP request. Specifically, the prompt text is packaged as JSON format data and sent to the server via the Internet. The input is the prompt text, and the output is an HTTP request to the server.

[0376] Step 3:

[0377] The server calls the generative AI model.

[0378] The server analyzes the received prompt sentence and calls the generative AI model. Specifically, it generates an API request containing the prompt sentence and sends it to the endpoint of the generative AI model. The input is an HTTP request containing the prompt sentence, and the output is an API request to the generative AI model.

[0379] Step 4:

[0380] A generative AI model generates the text.

[0381] A generative AI model generates text based on a prompt received from the server. For example, in response to the prompt "Write a short story about a cat," the model generates the text "Once upon a time, in a small village, there lived a cat..." The input is the prompt, and the output is the generated text.

[0382] Step 5:

[0383] The server sends the generated text to the terminal.

[0384] The server sends the text received from the generative AI model to the user's terminal. Specifically, it generates an HTTP response containing the generated text and sends it back to the terminal. The input is the generated text, and the output is the HTTP response to the terminal.

[0385] Step 6:

[0386] The terminal displays the generated text.

[0387] The device displays the generated text received from the server on the screen. Specifically, it inserts the received text into HTML and app UI components and displays it in a form that is visible to the user. The input is the generated text, and the output is the text that is displayed to the user.

[0388] (Application example 2)

[0389] Next, a description will be given of Application Example 2 of Form 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."

[0390] Conventional methods for analyzing and updating source code of legacy systems required a lot of manual operations and were inefficient. In addition, there was no technology to generate content using generative AI models based on prompts entered by users, making it difficult to generate personalized content. This meant that content could not be provided according to user needs, and the user experience did not improve.

[0391] 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.

[0392] In this invention, the server includes a reverse engineering means for analyzing the source code of the legacy system, a means for updating the analyzed source code, an artificial intelligence means for automatically performing the above operations, a means for generating content using a generative AI model based on a prompt sentence input by a user, and a means for displaying the generated content. This enables efficient analysis and updating of the legacy system, and further enables personalized content generation according to the needs of the user.

[0393] "Reverse engineering methods" are methods for analyzing the source code of an existing system or software to understand its functions and structure.

[0394] "Moderation measures" refer to measures that, based on the analyzed source code, reconstruct the source code in a manner that conforms to new technical standards or programming languages ​​while maintaining existing functions.

[0395] "Artificial intelligence means" means artificial intelligence technology used to automatically perform a specific operation or process.

[0396] A "prompt sentence" is the text that a user inputs into a generative AI model, and is the sentence that forms the basis for the generated content.

[0397] A "generative AI model" is an artificial intelligence model that generates content such as text and images based on input prompts.

[0398] A "content generation means" is a means for generating content using a generative AI model based on a prompt sentence entered by a user.

[0399] The "display means" refers to a means for visually presenting the generated content to the user.

[0400] A system for implementing the present invention is configured as follows: A server includes reverse engineering means for analyzing source code of a legacy system, means for updating the analyzed source code, artificial intelligence means for automatically performing the above operations, means for generating content using a generative AI model based on a prompt sentence input by a user, and means for displaying the generated content.

[0401] First, the server analyzes the source code of the legacy system using reverse engineering techniques. These techniques analyze the source code of the existing system or software to understand its functions and structure. Next, the analyzed source code is reconstructed using modernization techniques. These techniques reconstruct the source code in a way that conforms to new technical standards and programming languages ​​while maintaining the existing functions.

[0402] Moreover, artificial intelligence means will perform these operations automatically, eliminating the need for manual operations and enabling efficient analysis and renewal.

[0403] Also included is a means for generating content using a generative AI model based on a prompt sentence input by a user. A prompt sentence is a text input by a user to a generative AI model, and is the sentence that serves as the basis for the generated content. A generative AI model is an artificial intelligence model that generates content such as text and images based on the input prompt sentence.

[0404] The generated content is visually presented to the user by a display means, which is for visually presenting the generated content to the user.

[0405] As a concrete example, if a user wants to generate a story on the theme of "space exploration," they might enter the following prompt:

[0406] "Generate a space exploration story about a young scientist and his adventures exploring an unknown planet."

[0407] Given this prompt, a generative AI model might generate a story like this:

[0408] "A young scientist steps onto an unknown planet. His mission is to explore the planet's ecosystem and discover new life forms. The planet's sky glows purple, and strange plants grow on the surface. He carefully steps forward and eventually discovers a huge crystal cave. Inside the cave, there live glowing creatures, who welcome him. Through his adventure on this planet, he discovers the mysteries of the universe and new friends."

[0409] In this way, personalized content can be generated according to the user's needs.

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

[0411] Step 1:

[0412] The user enters a prompt sentence at the terminal.

[0413] Input: A prompt entered by the user (e.g., "Generate a story about space exploration. The protagonist is a young scientist and his adventures exploring an unknown planet.")

[0414] Output: The prompt text is sent to the server.

[0415] Step 2:

[0416] The server receives the prompt text and passes it to the generative AI model.

[0417] Input: The prompt text sent by the user

[0418] Output: The prompt passed to the generative AI model

[0419] Step 3:

[0420] A generative AI model generates content based on the prompt.

[0421] Input: The prompt given to the generative AI model

[0422] Output: Generated content (e.g. a story)

[0423] Step 4:

[0424] The server receives the generated content and passes it to the display means.

[0425] Input: Content output from a generative AI model

[0426] Output: The content that is passed to the display

[0427] Step 5:

[0428] The terminal uses a display means to visually present the generated content to the user.

[0429] Input: Content sent from the server

[0430] Output: The content that is displayed to the user

[0431] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0432] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to 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.

[0433] Another example of generative AI is Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0434] In the above embodiment, an example was given in which the specific process was performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0435] [Second embodiment]

[0436] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0437] 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.

[0438] 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 wide area network (WAN) and / or a local area network (LAN).

[0439] 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.

[0440] 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 the voice according to instructions from the processor 46.

[0441] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0442] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0443] Fig. 4 shows an example of 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.

[0444] 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.

[0445] 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.

[0446] In the smart glasses 214, the 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.

[0447] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0448] "Example 1"

[0449] The present invention is a system including a reverse engineering means for analyzing the source code of a legacy system, a means for updating the analyzed source code, and an AI means for automatically performing these operations. The reverse engineering means receives the source code of the legacy system as an input and grasps its functions and structure. Specifically, it analyzes the function call relationships, variable usage, control flow, etc. Next, the source code updating means reconstructs the source code in a form that conforms to new technical standards and programming languages ​​based on the analysis results. Specifically, it converts from an old programming language to a new programming language, optimizes inefficient algorithms, deletes redundant code, etc. Finally, the AI ​​means performs these operations automatically. Specifically, it automates the reverse engineering and source code updating as a series of processes, minimizing human intervention. This makes it possible to efficiently update legacy systems and support the promotion of DX.

[0450] The process flow of each embodiment will be described below.

[0451] "Example 1"

[0452] Step 1: Input the source code of the legacy system into the reverse engineering tool. In this step, the source code of the legacy system is provided in a format that can be analyzed. For example, source code written in an old programming language such as COBOL or FORTRAN is the target.

[0453] Step 2: Reverse engineering techniques analyze the source code. In this step, the structure and functions of the source code are understood. Specifically, function call relationships, variable usage, control flow, etc. are analyzed.

[0454] Step 3: Input the analysis results to the source code innovation means: In this step, the information analyzed by the reverse engineering means is passed to the source code innovation means.

[0455] Step 4: The source code modernization tool generates new source code. In this step, the source code is restructured based on the analysis results in a way that makes it compatible with new technical standards and programming languages. Specifically, this involves converting old programming languages ​​to new ones, optimizing inefficient algorithms, and removing redundant code.

[0456] Step 5: AI means performs the above steps automatically. In this step, reverse engineering and source code renewal are automated as a series of processes, minimizing human intervention. This allows legacy systems to be renewed efficiently, helping to promote DX.

[0457] Example 1

[0458] Next, a description will be given of Example 1 of Form 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".

[0459] In conventional systems, the process of generating appropriate text based on the prompt sentence entered by the user was complicated and difficult to perform efficiently. In addition, there was a lack of means to quickly return the generated text to the user, which resulted in a poor user experience. Furthermore, there was no interface for effectively utilizing the generative AI model, which created a high technical hurdle.

[0460] 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.

[0461] In this invention, the server includes a means for receiving a prompt sentence input by a user, a means for passing the received prompt sentence to a generative AI model, a means for the generative AI model to generate text based on the prompt sentence, and a means for returning the generated text to the user. This makes it possible to efficiently generate text based on the prompt sentence input by the user and quickly return it to the user.

[0462] A "user" is a person or organization that utilizes the system to enter prompts and receive generated text.

[0463] A "prompt" is a textual instruction or question that a user enters into a generative AI model.

[0464] A "generative AI model" is an artificial intelligence algorithm or system that generates natural language text based on a prompt.

[0465] The "server" is a computer system whose role is to receive a prompt from a user, pass it to a generative AI model, and return the generated text to the user.

[0466] An "HTTP request" is a request for data communication sent from a user's terminal to a server, and may include a prompt text.

[0467] An "HTTP response" is a data communication response sent from a server to a user's terminal, and may include generated text.

[0468] A "terminal" is a device used by a user to enter prompts and receive generated text, including a personal computer or a smartphone.

[0469] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0470] This invention is a system that generates text using a generative AI model based on a prompt sentence input by a user and returns the text to the user. A specific embodiment for implementing this system is described below.

[0471] Hardware and Software Used

[0472] Hardware: Terminals used by users (personal computers, smartphones, etc.), servers (equipped with high-performance CPUs and GPUs)

[0473] Software: Generative AI models (e.g. GPT-3), database management systems (e.g. MySQL), web servers (e.g. Apache)

[0474] System Overview

[0475] The user uses a terminal to input a prompt sentence into an input form on a web browser. The input prompt sentence is sent to the server as an HTTP request. The server passes the received prompt sentence to the generative AI model, which then generates text based on the prompt sentence. The generated text is returned to the user's terminal via the server and displayed on the web browser.

[0476] Examples

[0477] User input: "Tell me about the city of the future."

[0478] Output of generative AI model: “Cities of the future will be powered by sustainable energy sources and run efficiently using smart technology.”

[0479] Examples of prompt statements

[0480] "Please tell me how AI is being used in the medical field."

[0481] "Please explain the next generation transportation system."

[0482] "Please tell me about the effects of climate change and how to combat them."

[0483] The system can efficiently generate text based on the prompt sentence entered by the user and quickly return it to the user, improving the user experience and effectively utilizing the generative AI model.

[0484] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0485] Step 1:

[0486] The user enters a prompt sentence.

[0487] A user opens a web browser and accesses an input form of the system. For example, the user inputs a prompt such as "Tell me about the city of the future." The input form has a text box where the user can input any text. The input data is the prompt.

[0488] Step 2:

[0489] The terminal sends a prompt to the server.

[0490] When the user clicks the "Send" button, the terminal sends the entered prompt text to the server as an HTTP POST request. This request includes the text data entered by the user. The input data is the prompt text, and the output data is the HTTP request.

[0491] Step 3:

[0492] The server receives the prompt.

[0493] The server receives the HTTP request and extracts the prompt from the request body. The server temporarily stores the prompt for further processing. The input data is the HTTP request, and the output data is the prompt.

[0494] Step 4:

[0495] The server passes the prompt to the generative AI model.

[0496] The server sends an API request to the generative AI model. This request includes the prompt text entered by the user. The server accesses the API endpoint of the generative AI model and passes the prompt text. The input data is the prompt text, and the output data is the API request.

[0497] Step 5:

[0498] A generative AI model generates the text.

[0499] The generative AI model generates text based on the received prompt. For example, it generates text such as "Future cities will be run efficiently using smart technology with sustainable energy sources." The generated text is returned to the server as an API response. The input data is the prompt, and the output data is the generated text.

[0500] Step 6:

[0501] The server receives the generated text.

[0502] The server receives the API response from the generative AI model and extracts the generated text. The server temporarily stores this text for further processing. The input data is the API response, and the output data is the generated text.

[0503] Step 7:

[0504] The server sends the generated text to the terminal.

[0505] The server sends the generated text to the user's terminal as an HTTP response, which contains the generated text. The input data is the generated text, and the output data is the HTTP response.

[0506] Step 8:

[0507] The terminal displays the generated text.

[0508] The user's device displays the received text on a web browser. The user can check the generated text on the browser. For example, the text "Cities of the future will be run efficiently using smart technology, powered by sustainable energy sources." is displayed. The input data is the HTTP response, and the output data is the displayed text.

[0509] (Application example 1)

[0510] Next, a description will be given of Application Example 1 of Form 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".

[0511] The process of analyzing and updating the source code of traditional legacy systems is often done manually, which takes time and effort. In addition, even in content generation using generative AI models, there is a lack of efficient means to automatically generate content based on prompts entered by users, and then display, save, and share it. This leads to issues such as a lack of improvement in the user experience and a decrease in the efficiency of the entire system.

[0512] 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. In this invention, the server includes a reverse engineering means for analyzing the source code of the legacy system, a means for updating the analyzed source code, an artificial intelligence means for automatically performing the above operations, a generation AI model means for generating content based on a prompt sentence input by the user, a means for displaying the generated content, and a means for saving and sharing the generated content. This makes it possible to automate the process of analyzing and updating the source code of the legacy system, and further to efficiently generate, display, save, and share content based on the prompt sentence input by the user.

[0513] A "legacy system" is an information system that was built using old technologies and programming languages ​​and is still in operation today.

[0514] "Source code" means textual code written in a programming language that describes the operation of a program.

[0515] "Reverse engineering methods" are methods for analyzing the structure and functions of existing systems or software and extracting the original design information.

[0516] "Modernization" refers to the restructuring of existing source code to accommodate new technical standards or programming languages.

[0517] "Artificial intelligence means" means means for automatically performing certain tasks using machine learning or data analysis.

[0518] A "prompt" is text that is input into a generative AI model and serves as the basis for the generated content.

[0519] A "generative AI model means" is an artificial intelligence model for generating content such as text, images, and audio based on an input prompt sentence.

[0520] A "means for displaying" is a means for visually presenting the generated content to a user.

[0521] "Storage and sharing means" means the means for storing the generated content in digital form and sharing it with other users and systems.

[0522] The following system configuration and processing procedure will be described as an embodiment of the present invention.

[0523] System Configuration

[0524] This system uses the following hardware and software:

[0525] Hardware: Server, user device (smartphone)

[0526] Software: Python, OpenAI API

[0527] Process Overview

[0528] 1. Enter the prompt:

[0529] The user inputs a prompt sentence through a smartphone application, such as "A summer night's story" or "A cityscape of the future."

[0530] 2. Invoke the generative AI model:

[0531] The server receives the input prompt sentence and uses the OpenAI API to call a generative AI model (e.g., GPT-4), which generates text based on the prompt sentence.

[0532] 3. Content generation:

[0533] A generative AI model generates text based on the prompt, which corresponds to the prompt entered by the user.

[0534] 4. Displaying Content:

[0535] The server sends the generated text to the user's terminal and displays it to the user through a smartphone application.

[0536] 5. Save and Share:

[0537] Users can save the generated content on their smartphones and can also share it with other users via social media.

[0538] Examples

[0539] For example, if a user enters the prompt "A Midsummer Night's Tale," the generative AI model will generate a story based on the prompt, like this:

[0540] "One summer night, under the stars, a boy sets out on a mysterious adventure. He finds a glowing stone in the forest and discovers that it has magical powers."

[0541] The generated story is displayed on the user's smartphone, where they can save it or share it on social media.

[0542] Similarly, if you input the prompt "future cityscape," the generative AI model will generate the following text:

[0543] "In the city of the future, we see floating buildings and self-driving cars. People use the latest technology to live comfortable lives."

[0544] In this way, the system can generate, display, save, and share a variety of content based on the prompt text entered by the user.

[0545] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0546] Step 1:

[0547] A user starts an application on a smartphone and inputs a prompt sentence. The input prompt sentence is sent to the server in text format. The input data is the prompt sentence input by the user, and the output data is the prompt sentence sent to the server.

[0548] Step 2:

[0549] The server analyzes the received prompt text and converts it into an appropriate format to be passed to the generative AI model. The input data is the prompt text sent by the user, and the output data is the prompt text to be passed to the generative AI model. Specific operations include preprocessing the text (removing unnecessary spaces and converting it to a specific format).

[0550] Step 3:

[0551] The server uses the OpenAI API to call the generative AI model and generate content based on the prompt sentence. The input data is the preprocessed prompt sentence, and the output data is the generated content (text). The specific operation is to send an API request and receive the generated text.

[0552] Step 4:

[0553] The server transmits the generated content to the user terminal. The input data is the content received from the generative AI model, and the output data is the content to be transmitted to the user terminal. The specific operation is to convert the text data into an appropriate format and transmit it to the user terminal via the network.

[0554] Step 5:

[0555] The user terminal displays the received content within the application. The input data is the content sent from the server, and the output data is the content displayed to the user. Specifically, the text data is applied to a layout for display on the screen and visually presented to the user.

[0556] Step 6:

[0557] The user can save or share the generated content. The input data is the displayed content, and the output data is a saved file or a shared link. Specifically, by pressing the save button or the share button, the content can be saved in the device or shared with other users via SNS, etc.

[0558] Example 2

[0559] Next, a description will be given of Example 2 of the embodiment 2. In the following description, the data processing device 12 is referred to as a "server" and the smart glasses 214 are referred to as a "terminal".

[0560] Conventional systems require users to manually create programs to process or visualize specific data, which is time-consuming and labor-intensive. In addition, creating programs requires specialized knowledge, making it difficult for general users.

[0561] 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.

[0562] In this invention, the server includes a means for receiving an instruction statement input by a user, a generating AI means for generating a program based on the received instruction statement, a means for executing the generated program, and a means for returning the execution result to the user. This enables a user to easily generate a program for data processing and visualization and obtain the execution result without specialized knowledge.

[0563] A "user" is an individual or group that utilizes the system to input instructions and obtain results of data processing and visualization.

[0564] "Instructions" are textual instructions entered by a user into the system, including requests for specific data processing or visualization.

[0565] A "generative AI means" is a means that uses artificial intelligence technology to automatically generate appropriate program code based on an instruction text.

[0566] "Program" means a set of instructions generated by a generative AI means for performing a particular data processing or visualization task.

[0567] The "execution means" refers to a means for executing the generated program, acquiring data from the database, processing the data, and visualizing it.

[0568] A "database" is a system for systematically storing and managing data, and stores user information and other data.

[0569] "Data processing" is the process of converting, filtering, and aggregating acquired data based on specific conditions.

[0570] "Visualization" is the process of displaying the results of data processing in a visual format such as a graph or chart.

[0571] The "execution results" are the results of data processing and visualization obtained by executing the generated program, and are returned to the user.

[0572] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0573] This invention is a system that automatically generates a program using a generative AI model based on a command statement input by a user, and executes the program to process and visualize data. A specific embodiment of this system will be described below.

[0574] System Configuration

[0575] The system mainly consists of the following components:

[0576] 1. User interface: An interface for users to input instructions. It uses a terminal such as a web browser.

[0577] 2. Server: Receives instructions, generates a program using a generative AI model, and executes the generated program.

[0578] 3. Generative AI model: An artificial intelligence model that generates programs based on instructions. Specifically, we use models that use natural language processing technology (e.g., GPT-4).

[0579] 4. Database: A system for storing data and retrieving it from a server, for example using a relational database management system such as MySQL.

[0580] 5. Data processing library: A software library for processing data. Specifically, we use the Python Pandas library.

[0581] 6. Visualization Library: A software library for visualizing data as graphs and charts. Specifically, we use the Python Matplotlib library.

[0582] Creating and running a program

[0583] A user inputs instructions through the system's web interface, for example, "Generate a program that displays a graph of the age distribution of users." The server sends the instructions to the generative AI model, which generates the appropriate program code.

[0584] The generated program does the following:

[0585] 1. Database connection: The server connects to the database and gets the required data.

[0586] 2. Data processing: The acquired data is converted into a data frame using the Pandas library, and then filtered and aggregated based on certain conditions.

[0587] 3. Data visualization: The processed data is visualized as graphs and charts using the Matplotlib library.

[0588] Examples

[0589] For example, if a user types in "Please generate a program that will graph the age distribution of our users," the server might do the following:

[0590] 1. The server uses a generative AI model to generate a program based on the user's instructions.

[0591] 2. The generated program retrieves user age data from the database and calculates the age distribution using the Pandas library.

[0592] 3. The calculated age distribution is visualized as a bar graph using the Matplotlib library.

[0593] 4. The server saves the generated graph as an image file and returns the file to the user.

[0594] In this way, users can easily create programs for data processing and visualization and obtain execution results, even without specialized knowledge.

[0595] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0596] Step 1:

[0597] The user enters a prompt sentence.

[0598] The user accesses the system's web interface and types in the text box, "Please generate a program that will display a graph showing the age distribution of users." Then, he clicks the "Submit" button.

[0599] Input: The prompt text entered by the user.

[0600] Output: The prompt text sent to the server.

[0601] Step 2:

[0602] The server generates the program using the generative AI model.

[0603] The server receives user input and sends prompt sentences to the generative AI model, which generates appropriate program code based on the prompt sentences.

[0604] Input: The prompt text sent by the user.

[0605] Output: The generated program code.

[0606] Step 3:

[0607] The server executes the generated program.

[0608] The server executes the generated program code. First, it connects to the database and gets the user's age data. Then, it converts the data into a data frame using the Pandas library and calculates the age distribution. Finally, it visualizes the age distribution as a bar graph using the Matplotlib library and saves it as an image file.

[0609] Input: The generated program code.

[0610] Output: Age distribution graph image file.

[0611] Step 4:

[0612] The server returns the execution result to the user.

[0613] The server returns the generated graph image to the user, who can download and view it through the system's web interface.

[0614] Input: Age distribution graph image file.

[0615] Output: The graph image file that is returned to the user.

[0616] (Application example 2)

[0617] Next, a description will be given of Application Example 2 of Form 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".

[0618] The traditional source code analysis and modernization process for legacy systems is often manual, time-consuming and labor-intensive. In addition, there is a lack of efficient means to generate personalized content based on prompts entered by users. This makes it difficult to quickly respond to user needs.

[0619] 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.

[0620] In this invention, the server includes a reverse engineering means for analyzing the source code of the legacy system, a means for updating the analyzed source code, a means including a generative AI model for automatically performing the above operations, a means for generating content based on a prompt sentence input by a user, and a means for displaying the generated content. This makes it possible to automate the source code analysis and updating process of the legacy system and to rapidly generate personalized content according to the needs of the user.

[0621] A "legacy system" is an information system that was built using old technologies or platforms and is still in use today.

[0622] "Source code" means textual code written in a programming language that describes the operation of a program.

[0623] "Reverse engineering" is the process of analyzing the structure and function of an existing system or software to understand its design and operation.

[0624] A "generative AI model" is an artificial intelligence model that generates new content based on given input data (prompt text).

[0625] A "prompt" is text that is input to a generative AI model to cause it to generate a particular output.

[0626] "Content" means any information or data made available to Users and may be in the form of text, images, video or other formats.

[0627] "Modernization" means restructuring existing source code to conform to new technical standards and programming languages.

[0628] "Automation" means that systems or processes are carried out automatically by machines or software with minimal human intervention.

[0629] "Personalization" means customizing content and services according to a user's individual needs and preferences.

[0630] "Display" refers to visually presenting the generated content to a user.

[0631] A system for implementing the present invention is configured as follows: A server includes reverse engineering means for analyzing source code of a legacy system, means for updating the analyzed source code, means including a generative AI model for automatically performing the above operations, means for generating content based on a prompt sentence input by a user, and means for displaying the generated content.

[0632] Program processing explanation

[0633] The server first analyzes the source code of the legacy system using reverse engineering techniques. This analysis allows the system's functions and structure to be understood. It then uses techniques to modernize the analyzed source code, reconstructing it in a way that complies with new technical standards and programming languages. This series of operations is automated using a generative AI model.

[0634] When a user inputs a prompt sentence through a terminal, the server uses a generative AI model to generate content based on the prompt sentence, and the generated content is provided to the user through the display means of the terminal.

[0635] Hardware and Software Used

[0636] Hardware: Servers, user devices (smartphones, tablets, PCs, etc.)

[0637] Software: Python, generative AI models (e.g. GPT-3), reverse engineering tools, source code renovation tools

[0638] Examples

[0639] For example, if a user enters the prompt "Generate a blog post about recent technology trends," the server will use a generative AI model to generate content like this:

[0640] Example prompt:

[0641] Generate blog posts about recent technology trends.

[0642] Examples of generated content:

[0643] Recent technology trends include artificial intelligence (AI), blockchain, 5G communications, and quantum computing. These technologies have the potential to dramatically change our daily lives and the way we do business. For example, AI plays a key role in medical diagnosis and the development of self-driving cars. Blockchain is attracting attention as a technology that will improve the transparency and security of financial transactions. 5G communications will provide faster and more reliable Internet connections, facilitating the widespread adoption of IoT devices. Quantum computing is expected to provide a new means of solving complex problems that cannot be solved by conventional computers.

[0644] In this way, personalized content that meets the user's needs can be quickly generated and provided.

[0645] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0646] Step 1:

[0647] The user enters a prompt through the terminal.

[0648] Input: A prompt entered by the user (e.g. "Generate a blog post about recent technology trends.")

[0649] Output: The prompt text is sent to the server.

[0650] Specific operation: The user inputs a prompt into the input field of the terminal and presses the send button. The terminal sends this prompt to the server.

[0651] Step 2:

[0652] The server receives the prompt text and passes it to the generative AI model.

[0653] Input: Prompt text sent from the terminal

[0654] Output: The prompt is fed into the generative AI model.

[0655] Specific operation: The server analyzes the prompt text received from the terminal and sends a request to the API of the generative AI model.

[0656] Step 3:

[0657] A generative AI model generates content based on the prompt.

[0658] Input: Prompt text passed from the server

[0659] Output: The generated content (e.g. blog post)

[0660] How it works: The generative AI model analyzes the prompt and generates text based on relevant information, which is then returned to the server.

[0661] Step 4:

[0662] The server receives the generated content and transmits it to the terminal.

[0663] Input: Generated content returned from a generative AI model

[0664] Output: Generated content is sent to a device.

[0665] Specific operation: The server converts the content received from the generative AI model into a data format for transmission to the terminal and sends it to the terminal.

[0666] Step 5:

[0667] The device displays the generated content.

[0668] Input: Generated content sent from the server

[0669] Output: The generated content is displayed to the user.

[0670] Specific operation: The device renders the content received from the server in the display area and provides it visually to the user.

[0671] In this way, a series of processes is realized in which content is generated using a generative AI model based on the prompt sentence entered by the user and provided to the user.

[0672] In addition, an emotion engine that estimates the emotion of the user may be further combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[0673] "Example 1"

[0674] In one embodiment of the present invention, the AI ​​means includes an emotion engine that recognizes the user's emotion. The emotion engine estimates the user's emotional state from the user's facial expression, tone of voice, choice of words, etc. For example, if the user feels irritated, the emotion engine communicates the information to the AI ​​means. The AI ​​means uses this information to adjust the priority of source code renewal. Specifically, if the user feels irritated, the AI ​​means prioritizes renewal of the source code of the part that the user is interested in. In addition, the emotion engine reports the progress of the source code renewal according to the user's emotional state. For example, if the user feels calm, the emotion engine communicates the information to the AI ​​means, and the AI ​​means provides the user with a detailed report of the progress. This provides appropriate feedback according to the user's emotional state, improving the user experience.

[0675] The process flow of each embodiment will be described below.

[0676] "Example 1"

[0677] Step 1: The emotion engine infers the user's emotional state from their facial expressions, tone of voice, choice of words, etc.

[0678] Step 2: The emotion engine communicates the estimated emotional state to the AI ​​means.

[0679] Step 3: The AI ​​means adjusts the priority of source code updating using the information received from the emotion engine. Specifically, if the user feels frustrated, the AI ​​means prioritizes updating the source code of the part that the user is interested in.

[0680] Step 4: The emotion engine reports the progress of the source code update according to the user's emotional state. For example, if the user feels calm, the emotion engine conveys that information to the AI ​​means, and the AI ​​means provides the user with a detailed report of the progress.

[0681] Example 1

[0682] Next, a description will be given of Example 1 of Form 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".

[0683] Conventional methods for analyzing and updating source code of legacy systems required a lot of manual operations and were inefficient. In addition, even text generation systems using generative AI models lacked the means to properly process user input and effectively use the generated text. This made it difficult to respond to user requests quickly and accurately.

[0684] The identification process by the identification processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes a reverse engineering means for analyzing the source code of the legacy system, a means for updating the analyzed source code, a means for receiving a prompt sentence input by a user, a means for preprocessing the received prompt sentence, a means for inputting the preprocessed prompt sentence into a generative AI model and acquiring the generated text, a means for post-processing the generated text, and an AI means for automatically performing the above operations. This enables efficient analysis and updating of the legacy system and automation of text generation using the generative AI model.

[0685] A "legacy system" is an information system that has been in use for a long time but is still in operation today.

[0686] "Source code" refers to textual code that describes the operation of a program.

[0687] "Reverse engineering" is a technique for analyzing the structure and operation of existing systems or software and extracting their design information.

[0688] A "prompt" is text that is entered as an instruction or question into a generative AI model.

[0689] "Preprocessing" refers to the process of converting data into an appropriate format before inputting it into a generative AI model.

[0690] A "generative AI model" is a model that uses artificial intelligence to generate text and data.

[0691] "Post-processing" refers to the process of converting the data output from a generative AI model into a format that is easy for humans to understand.

[0692] "AI means" refers to means of automating specific operations or processes using artificial intelligence technology.

[0693] This invention combines a system for analyzing and updating the source code of a legacy system with a system for generating text based on a prompt sentence entered by a user using a generative AI model. A specific embodiment of this system is shown below.

[0694] The server uses reverse engineering techniques to analyze the source code of the legacy system. The reverse engineering techniques are techniques for analyzing the structure and operation of existing systems and software and extracting their design information. The analyzed source code is reconstructed by modernization techniques in a form that conforms to new technical standards and programming languages.

[0695] A user inputs a prompt into an input field on the device, for example, "Use AI to generate a poem about spring." The device sends this prompt to the server, which then preprocesses the received prompt and converts it into a format suitable for the generative AI model. Preprocessing includes tokenizing and normalizing the text.

[0696] The server inputs the preprocessed prompt sentence into a generative AI model. This generative AI model uses, for example, a general generative AI model. The generative AI model generates new text based on the input prompt sentence. The generated text is returned to the server.

[0697] The server post-processes the generated text and converts it into a human-readable form, which includes decoding and formatting the generated text, and sends the post-processed text to the terminal for display to the user.

[0698] For example, if a user inputs "Use AI to generate a poem about spring," the server inputs this prompt into the generative AI model and generates a poem about spring. The generated poem is displayed to the user.

[0699] This system realizes efficient analysis and renovation of legacy systems and automation of text generation using generative AI models. The hardware used is a high-performance GPU (e.g., a general high-performance GPU), and the software uses Python and TensorFlow.

[0700] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0701] Step 1:

[0702] The user enters a prompt sentence.

[0703] The user inputs a prompt for the generative AI model into the input field of the terminal. For example, the user inputs, "Please use AI to generate a poem about spring." The input prompt becomes the input data for the terminal.

[0704] Step 2:

[0705] The terminal sends a prompt to the server.

[0706] The terminal sends the prompt text entered by the user to the server using an HTTP request. The input is the prompt text entered by the user, and the output is the prompt text sent to the server.

[0707] Step 3:

[0708] The server receives the prompt and performs data preprocessing.

[0709] The server receives the prompt sentence sent from the terminal. It tokenizes the received prompt sentence and converts it into a format suitable for the generative AI model. Specifically, it splits the text into words and subwords and converts it into a numerical vector. The input is the prompt sentence received from the terminal, and the output is the preprocessed numerical vector.

[0710] Step 4:

[0711] The server inputs a prompt sentence into the generative AI model and obtains the generated text.

[0712] The server inputs the preprocessed prompt sentence into the generative AI model. The generative AI model generates new text based on the input prompt sentence. The generated text is returned to the server. The input is the preprocessed numeric vector, and the output is the generated text.

[0713] Step 5:

[0714] The server post-processes the generated text and sends it to the terminal.

[0715] The server post-processes the text returned by the generative AI model. Specifically, it decodes the generated text and converts it into a human-readable format. It then sends the generated text to the terminal. The input is the text returned by the generative AI model, and the output is the post-processed text.

[0716] Step 6:

[0717] The terminal displays the generated text to the user.

[0718] The terminal displays the generated text received from the server to the user, who can then review the generated poem or piece of writing. The input is the post-processed text received from the server, and the output is the text displayed to the user.

[0719] (Application example 1)

[0720] Next, a description will be given of Application Example 1 of Form 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".

[0721] The process of analyzing and updating the source code of traditional legacy systems is very time-consuming and labor-intensive if done manually. In addition, there is a lack of efficient means to generate personalized content based on prompts entered by users. This makes it difficult to respond quickly to user needs.

[0722] 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.

[0723] In this invention, the server includes a reverse engineering means for analyzing the source code of the legacy system, a means for updating the analyzed source code, an artificial intelligence means for automatically performing the above operations, a generative AI model means for generating content based on a prompt sentence input by a user, and a means for displaying the generated content to a user. This enables efficient analysis and updating of the legacy system, and further enables rapid generation and provision of personalized content based on the user's prompt sentence.

[0724] "Reverse engineering methods" are methods for analyzing the source code of a legacy system and understanding its functions and structure.

[0725] "Innovation measures" are measures to reconstruct the analyzed source code in a way that complies with new technical standards or programming languages.

[0726] "Artificial intelligence means" means means for automating reverse engineering and source code renovation operations.

[0727] A "generative AI model means" is a means for generating content based on a prompt sentence entered by a user.

[0728] The "display means" refers to a means for displaying the generated content to the user.

[0729] The following system configuration and processing procedure will be described as an embodiment of the present invention.

[0730] The server includes reverse engineering means for analyzing source code of the legacy system, means for updating the analyzed source code, artificial intelligence means for automatically performing the above operations, generative AI model means for generating content based on a prompt sentence input by a user, and means for displaying the generated content to a user.

[0731] Specifically, the server is implemented using a Python program and the OpenAI API. The reverse engineering means analyzes the source code of the legacy system to understand its functions and structure. The analysis results are reconstructed by the modernization means in a form that complies with new technical standards and programming languages. These operations are automated by the artificial intelligence means.

[0732] When a user inputs a prompt sentence using the smartphone, the prompt sentence is sent to the generative AI model means. The generative AI model means generates content based on the prompt sentence using a generative AI model (e.g., text-davinci-003) of OpenAI. The generated content is displayed to the user by the display means.

[0733] For example, if a user inputs "I want to write a blog about my travel memories. Please write in detail about my trip to Okinawa with my family last summer," the generative AI model means will generate a detailed blog post based on this prompt. The generated blog post will be displayed on the smartphone screen for the user to check.

[0734] The system enables efficient analysis and modernization of legacy systems, as well as the rapid generation and delivery of personalized content based on user prompts.

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

[0736] Step 1:

[0737] A user inputs a prompt sentence using a smartphone. The input prompt sentence is sent to a server. The input data is the prompt sentence in text format input by the user.

[0738] Step 2:

[0739] The server passes the received prompt sentence to the generative AI model means. The generative AI model means analyzes the prompt sentence and processes the data to generate appropriate content. Specifically, it uses OpenAI's generative AI model (e.g., text-davinci-003) to generate text based on the prompt sentence. The input data is the prompt sentence, and the output data is the generated content.

[0740] Step 3:

[0741] The content generated by the generative AI model means is temporarily stored in the server. The server passes the generated content to the display means. The input data is the generated content, and the output data is the content passed to the display means.

[0742] Step 4:

[0743] The display means displays the generated content on the user's smartphone. The user can check the generated content on the screen of the smartphone. The input data is the content passed to the display means, and the output data is the content displayed on the screen of the smartphone.

[0744] Step 5:

[0745] The user can check the displayed content and modify or save it as necessary. The input data is the content displayed on the smartphone screen, and the output data is the content modified or saved by the user's operation.

[0746] In this way, a series of processes is realized in which personalized content is generated using a generative AI model based on the prompt sentence entered by the user and provided to the user.

[0747] Example 2

[0748] Next, a description will be given of Example 2 of the embodiment 2. In the following description, the data processing device 12 is referred to as a "server" and the smart glasses 214 are referred to as a "terminal".

[0749] The source code analysis and renovation process of conventional legacy systems is often done manually, which takes time and effort. In addition, even in text generation systems using generative AI models, the process from when the user enters a prompt sentence to when they receive the generated text is complicated, making it difficult to perform efficiently. To solve these issues, it is necessary to automate the source code analysis and renovation process and to streamline the text generation process using generative AI models.

[0750] 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.

[0751] In this invention, the server includes a reverse engineering means for analyzing the source code of the legacy system, a means for updating the analyzed source code, an artificial intelligence means for automatically performing the above operations, a means for a user to input a prompt sentence, a means for a terminal to send the prompt sentence to the server, a means for the server to call a generative AI model, a means for the generative AI model to generate text, a means for the server to send the generated text to the terminal, and a means for the terminal to display the generated text. This makes it possible to automate the source code analysis and updating process and to streamline the text generation process using the generative AI model.

[0752] "Legacy systems" refers to existing, older information systems or software that are typically not aligned with new technologies and standards.

[0753] "Source code" means textual code written in a programming language that describes the operation of a program.

[0754] "Reverse engineering" is a technique for analyzing the structure and function of an existing system or software to understand its design and operation.

[0755] "Artificial intelligence" refers to computer systems and algorithms that mimic human intelligence and can learn, reason, and solve problems.

[0756] A "prompt sentence" is an instruction entered by a user to request a generative AI model to generate specific text.

[0757] "Terminal" refers to a device such as a computer or smartphone operated by a user.

[0758] A "server" is a computer system that provides services to other computers and devices over a network.

[0759] A "generative AI model" is an artificial intelligence model that generates text based on an input prompt.

[0760] "Text generation" is the process by which a generative AI model creates new text based on a given prompt.

[0761] An "HTTP request" is a protocol that allows a client (terminal) to request a server to send data or obtain information.

[0762] The "JSON format" is a lightweight data exchange format for expressing data in text format.

[0763] An "API Request" is a request to other software or services to perform a function through an application program interface.

[0764] "HTML" is a markup language for constructing web pages.

[0765] "UI components" refers to the individual elements or parts that make up a user interface.

[0766] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0767] The present invention is a system for automating the source code analysis and renewal process of a legacy system and for streamlining the text generation process using a generative AI model. A specific embodiment of the system will be described below.

[0768] Hardware and Software Used

[0769] 1. Server

[0770] The server includes reverse engineering and artificial intelligence means for analyzing and updating the source code of the legacy system, and is capable of handling API requests for invoking generative AI models and generating text.

[0771] 2. Terminal

[0772] A terminal is a device where a user inputs a prompt text and sends it to a server. Specifically, this applies to PCs and smartphones.

[0773] 3. Generative AI Models

[0774] A generative AI model is an artificial intelligence model that generates text based on a prompt sentence when called by a server, such as OpenAI's GPT-3.

[0775] Data processing and calculation

[0776] 1. Enter and send a prompt

[0777] A user inputs a prompt through the terminal interface, for example, "Write a short story about a cat." The terminal sends this prompt as an HTTP request to the server.

[0778] 2. Parsing the prompt and calling the generative AI model

[0779] The server analyzes the received prompt sentence and calls the generative AI model. Specifically, it generates an API request including the prompt sentence and sends it to the endpoint of the generative AI model.

[0780] 3. Generating text and sending a response

[0781] The generative AI model generates text based on the input prompt. For example, in response to the prompt "Write a short story about a cat," it generates the text "Once upon a time, there lived a cat in a small village..." The generated text is returned to the server as an API response.

[0782] 4. Viewing the generated text

[0783] The server sends the text received from the generative AI model to the user's device, which displays the received text on the screen so that the user can check it.

[0784] Examples

[0785] Examples of prompt statements

[0786] "Write a short story about a cat."

[0787] "Write a poem about the city of the future."

[0788] "Please explain how AI will change human life."

[0789] In this way, users can use generative AI models to generate various texts. This system automates the source code analysis and renovation process of legacy systems, and streamlines the text generation process using generative AI models.

[0790] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0791] Step 1:

[0792] The user enters a prompt sentence.

[0793] The user inputs a prompt through the terminal interface, for example, "Write a short story about a cat" in the text input field and clicks the send button. The input prompt is temporarily stored in the terminal's memory.

[0794] Step 2:

[0795] The terminal sends a prompt to the server.

[0796] The terminal sends the prompt text entered by the user to the server as an HTTP request. Specifically, the prompt text is packaged as JSON format data and sent to the server via the Internet. The input is the prompt text, and the output is an HTTP request to the server.

[0797] Step 3:

[0798] The server calls the generative AI model.

[0799] The server analyzes the received prompt sentence and calls the generative AI model. Specifically, it generates an API request containing the prompt sentence and sends it to the endpoint of the generative AI model. The input is an HTTP request containing the prompt sentence, and the output is an API request to the generative AI model.

[0800] Step 4:

[0801] A generative AI model generates the text.

[0802] A generative AI model generates text based on a prompt received from the server. For example, in response to the prompt "Write a short story about a cat," the model generates the text "Once upon a time, in a small village, there lived a cat..." The input is the prompt, and the output is the generated text.

[0803] Step 5:

[0804] The server sends the generated text to the terminal.

[0805] The server sends the text received from the generative AI model to the user's terminal. Specifically, it generates an HTTP response containing the generated text and sends it back to the terminal. The input is the generated text, and the output is the HTTP response to the terminal.

[0806] Step 6:

[0807] The terminal displays the generated text.

[0808] The device displays the generated text received from the server on the screen. Specifically, it inserts the received text into HTML and app UI components and displays it in a form that is visible to the user. The input is the generated text, and the output is the text that is displayed to the user.

[0809] (Application example 2)

[0810] Next, a description will be given of Application Example 2 of Form 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".

[0811] Conventional methods for analyzing and updating source code of legacy systems required a lot of manual operations and were inefficient. In addition, there was no technology to generate content using generative AI models based on prompts entered by users, making it difficult to generate personalized content. This meant that content could not be provided according to user needs, and the user experience did not improve.

[0812] 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.

[0813] In this invention, the server includes a reverse engineering means for analyzing the source code of the legacy system, a means for updating the analyzed source code, an artificial intelligence means for automatically performing the above operations, a means for generating content using a generative AI model based on a prompt sentence input by a user, and a means for displaying the generated content. This enables efficient analysis and updating of the legacy system, and further enables personalized content generation according to the needs of the user.

[0814] "Reverse engineering methods" are methods for analyzing the source code of an existing system or software to understand its functions and structure.

[0815] "Moderation measures" refer to measures that, based on the analyzed source code, reconstruct the source code in a manner that conforms to new technical standards or programming languages ​​while maintaining existing functions.

[0816] "Artificial intelligence means" means artificial intelligence technology used to automatically perform a specific operation or process.

[0817] A "prompt sentence" is the text that a user inputs into a generative AI model, and is the sentence that forms the basis for the generated content.

[0818] A "generative AI model" is an artificial intelligence model that generates content such as text and images based on input prompts.

[0819] A "content generation means" is a means for generating content using a generative AI model based on a prompt sentence entered by a user.

[0820] The "display means" refers to a means for visually presenting the generated content to the user.

[0821] A system for implementing the present invention is configured as follows: A server includes reverse engineering means for analyzing source code of a legacy system, means for updating the analyzed source code, artificial intelligence means for automatically performing the above operations, means for generating content using a generative AI model based on a prompt sentence input by a user, and means for displaying the generated content.

[0822] First, the server analyzes the source code of the legacy system using reverse engineering techniques. These techniques analyze the source code of the existing system or software to understand its functions and structure. Next, the analyzed source code is reconstructed using modernization techniques. These techniques reconstruct the source code in a way that conforms to new technical standards and programming languages ​​while maintaining the existing functions.

[0823] Moreover, artificial intelligence means will perform these operations automatically, eliminating the need for manual operations and enabling efficient analysis and renewal.

[0824] Also included is a means for generating content using a generative AI model based on a prompt sentence input by a user. A prompt sentence is a text input by a user to a generative AI model, and is the sentence that serves as the basis for the generated content. A generative AI model is an artificial intelligence model that generates content such as text and images based on the input prompt sentence.

[0825] The generated content is visually presented to the user by a display means, which is for visually presenting the generated content to the user.

[0826] As a concrete example, if a user wants to generate a story on the theme of "space exploration," they might enter the following prompt:

[0827] "Generate a space exploration story about a young scientist and his adventures exploring an unknown planet."

[0828] Given this prompt, a generative AI model might generate a story like this:

[0829] "A young scientist steps onto an unknown planet. His mission is to explore the planet's ecosystem and discover new life forms. The planet's sky glows purple, and strange plants grow on the surface. He carefully steps forward and eventually discovers a huge crystal cave. Inside the cave, there live glowing creatures, who welcome him. Through his adventure on this planet, he discovers the mysteries of the universe and new friends."

[0830] In this way, personalized content can be generated according to the user's needs.

[0831] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0832] Step 1:

[0833] The user enters a prompt sentence at the terminal.

[0834] Input: A prompt entered by the user (e.g., "Generate a story about space exploration. The protagonist is a young scientist and his adventures exploring an unknown planet.")

[0835] Output: The prompt text is sent to the server.

[0836] Step 2:

[0837] The server receives the prompt text and passes it to the generative AI model.

[0838] Input: The prompt text sent by the user

[0839] Output: The prompt passed to the generative AI model

[0840] Step 3:

[0841] A generative AI model generates content based on the prompt.

[0842] Input: The prompt given to the generative AI model

[0843] Output: Generated content (e.g. a story)

[0844] Step 4:

[0845] The server receives the generated content and passes it to the display means.

[0846] Input: Content output from a generative AI model

[0847] Output: The content that is passed to the display

[0848] Step 5:

[0849] The terminal uses a display means to visually present the generated content to the user.

[0850] Input: Content sent from the server

[0851] Output: The content that is displayed to the user

[0852] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0853] 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> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to 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.

[0854] Another example of generative AI is Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0855] 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 smart glasses 214.

[0856] [Third embodiment]

[0857] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0858] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0859] 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 wide area network (WAN) and / or a local area network (LAN).

[0860] 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.

[0861] 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 the voice according to instructions from the processor 46.

[0862] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0863] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0864] Fig. 6 shows an example of 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.

[0865] 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.

[0866] 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.

[0867] In the headset type terminal 314, the 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.

[0868] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0869] "Example 1"

[0870] The present invention is a system including a reverse engineering means for analyzing the source code of a legacy system, a means for updating the analyzed source code, and an AI means for automatically performing these operations. The reverse engineering means receives the source code of the legacy system as an input and grasps its functions and structure. Specifically, it analyzes the function call relationships, variable usage, control flow, etc. Next, the source code updating means reconstructs the source code in a form that conforms to new technical standards and programming languages ​​based on the analysis results. Specifically, it converts from an old programming language to a new programming language, optimizes inefficient algorithms, deletes redundant code, etc. Finally, the AI ​​means performs these operations automatically. Specifically, it automates the reverse engineering and source code updating as a series of processes, minimizing human intervention. This makes it possible to efficiently update legacy systems and support the promotion of DX.

[0871] The process flow of each embodiment will be described below.

[0872] "Example 1"

[0873] Step 1: Input the source code of the legacy system into the reverse engineering tool. In this step, the source code of the legacy system is provided in a format that can be analyzed. For example, source code written in an old programming language such as COBOL or FORTRAN is the target.

[0874] Step 2: Reverse engineering techniques analyze the source code. In this step, the structure and functions of the source code are understood. Specifically, function call relationships, variable usage, control flow, etc. are analyzed.

[0875] Step 3: Input the analysis results to the source code innovation means: In this step, the information analyzed by the reverse engineering means is passed to the source code innovation means.

[0876] Step 4: The source code modernization tool generates new source code. In this step, the source code is restructured based on the analysis results in a way that makes it compatible with new technical standards and programming languages. Specifically, this involves converting old programming languages ​​to new ones, optimizing inefficient algorithms, and removing redundant code.

[0877] Step 5: AI means performs the above steps automatically. In this step, reverse engineering and source code renewal are automated as a series of processes, minimizing human intervention. This allows legacy systems to be renewed efficiently, helping to promote DX.

[0878] Example 1

[0879] Next, a description will be given of Example 1 of the embodiment 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".

[0880] In conventional systems, the process of generating appropriate text based on the prompt sentence entered by the user was complicated and difficult to perform efficiently. In addition, there was a lack of means to quickly return the generated text to the user, which resulted in a poor user experience. Furthermore, there was no interface for effectively utilizing the generative AI model, which created a high technical hurdle.

[0881] 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.

[0882] In this invention, the server includes a means for receiving a prompt sentence input by a user, a means for passing the received prompt sentence to a generative AI model, a means for the generative AI model to generate text based on the prompt sentence, and a means for returning the generated text to the user. This makes it possible to efficiently generate text based on the prompt sentence input by the user and quickly return it to the user.

[0883] A "user" is a person or organization that utilizes the system to enter prompts and receive generated text.

[0884] A "prompt" is a textual instruction or question that a user enters into a generative AI model.

[0885] A "generative AI model" is an artificial intelligence algorithm or system that generates natural language text based on a prompt.

[0886] The "server" is a computer system whose role is to receive a prompt from a user, pass it to a generative AI model, and return the generated text to the user.

[0887] An "HTTP request" is a request for data communication sent from a user's terminal to a server, and may include a prompt text.

[0888] An "HTTP response" is a data communication response sent from a server to a user's terminal, and may include generated text.

[0889] A "terminal" is a device used by a user to enter prompts and receive generated text, including a personal computer or a smartphone.

[0890] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0891] This invention is a system that generates text using a generative AI model based on a prompt sentence input by a user and returns the text to the user. A specific embodiment for implementing this system is described below.

[0892] Hardware and Software Used

[0893] Hardware: Terminals used by users (personal computers, smartphones, etc.), servers (equipped with high-performance CPUs and GPUs)

[0894] Software: Generative AI models (e.g. GPT-3), database management systems (e.g. MySQL), web servers (e.g. Apache)

[0895] System Overview

[0896] The user uses a terminal to input a prompt sentence into an input form on a web browser. The input prompt sentence is sent to the server as an HTTP request. The server passes the received prompt sentence to the generative AI model, which then generates text based on the prompt sentence. The generated text is returned to the user's terminal via the server and displayed on the web browser.

[0897] Examples

[0898] User input: "Tell me about the city of the future."

[0899] Output of generative AI model: “Cities of the future will be powered by sustainable energy sources and run efficiently using smart technology.”

[0900] Examples of prompt statements

[0901] "Please tell me how AI is being used in the medical field."

[0902] "Please explain the next generation transportation system."

[0903] "Please tell me about the effects of climate change and how to combat them."

[0904] The system can efficiently generate text based on the prompt sentence entered by the user and quickly return it to the user, improving the user experience and effectively utilizing the generative AI model.

[0905] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0906] Step 1:

[0907] The user enters a prompt sentence.

[0908] A user opens a web browser and accesses an input form of the system. For example, the user inputs a prompt such as "Tell me about the city of the future." The input form has a text box where the user can input any text. The input data is the prompt.

[0909] Step 2:

[0910] The terminal sends a prompt to the server.

[0911] When the user clicks the "Send" button, the terminal sends the entered prompt text to the server as an HTTP POST request. This request includes the text data entered by the user. The input data is the prompt text, and the output data is the HTTP request.

[0912] Step 3:

[0913] The server receives the prompt.

[0914] The server receives the HTTP request and extracts the prompt from the request body. The server temporarily stores the prompt for further processing. The input data is the HTTP request, and the output data is the prompt.

[0915] Step 4:

[0916] The server passes the prompt to the generative AI model.

[0917] The server sends an API request to the generative AI model. This request includes the prompt text entered by the user. The server accesses the API endpoint of the generative AI model and passes the prompt text. The input data is the prompt text, and the output data is the API request.

[0918] Step 5:

[0919] A generative AI model generates the text.

[0920] The generative AI model generates text based on the received prompt. For example, it generates text such as "Future cities will be run efficiently using smart technology with sustainable energy sources." The generated text is returned to the server as an API response. The input data is the prompt, and the output data is the generated text.

[0921] Step 6:

[0922] The server receives the generated text.

[0923] The server receives the API response from the generative AI model and extracts the generated text. The server temporarily stores this text for further processing. The input data is the API response, and the output data is the generated text.

[0924] Step 7:

[0925] The server sends the generated text to the terminal.

[0926] The server sends the generated text to the user's terminal as an HTTP response, which contains the generated text. The input data is the generated text, and the output data is the HTTP response.

[0927] Step 8:

[0928] The terminal displays the generated text.

[0929] The user's device displays the received text on a web browser. The user can check the generated text on the browser. For example, the text "Cities of the future will be run efficiently using smart technology, powered by sustainable energy sources." is displayed. The input data is the HTTP response, and the output data is the displayed text.

[0930] (Application example 1)

[0931] Next, a description will be given of Application Example 1 of Form 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".

[0932] The process of analyzing and updating the source code of traditional legacy systems is often done manually, which takes time and effort. In addition, even in content generation using generative AI models, there is a lack of efficient means to automatically generate content based on prompts entered by users, and then display, save, and share it. This leads to issues such as a lack of improvement in the user experience and a decrease in the efficiency of the entire system.

[0933] 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. In this invention, the server includes a reverse engineering means for analyzing the source code of the legacy system, a means for updating the analyzed source code, an artificial intelligence means for automatically performing the above operations, a generation AI model means for generating content based on a prompt sentence input by the user, a means for displaying the generated content, and a means for saving and sharing the generated content. This makes it possible to automate the process of analyzing and updating the source code of the legacy system, and further to efficiently generate, display, save, and share content based on the prompt sentence input by the user.

[0934] A "legacy system" is an information system that was built using old technologies and programming languages ​​and is still in operation today.

[0935] "Source code" means textual code written in a programming language that describes the operation of a program.

[0936] "Reverse engineering methods" are methods for analyzing the structure and functions of existing systems or software and extracting the original design information.

[0937] "Modernization" refers to the restructuring of existing source code to accommodate new technical standards or programming languages.

[0938] "Artificial intelligence means" means means for automatically performing certain tasks using machine learning or data analysis.

[0939] A "prompt" is text that is input into a generative AI model and serves as the basis for the generated content.

[0940] A "generative AI model means" is an artificial intelligence model for generating content such as text, images, and audio based on an input prompt sentence.

[0941] A "means for displaying" is a means for visually presenting the generated content to a user.

[0942] "Storage and sharing means" means the means for storing the generated content in digital form and sharing it with other users and systems.

[0943] The following system configuration and processing procedure will be described as an embodiment of the present invention.

[0944] System Configuration

[0945] This system uses the following hardware and software:

[0946] Hardware: Server, user device (smartphone)

[0947] Software: Python, OpenAI API

[0948] Process Overview

[0949] 1. Enter the prompt:

[0950] The user inputs a prompt sentence through a smartphone application, such as "A summer night's story" or "A cityscape of the future."

[0951] 2. Invoke the generative AI model:

[0952] The server receives the input prompt sentence and uses the OpenAI API to call a generative AI model (e.g., GPT-4), which generates text based on the prompt sentence.

[0953] 3. Content generation:

[0954] A generative AI model generates text based on the prompt, which corresponds to the prompt entered by the user.

[0955] 4. Viewing Content:

[0956] The server sends the generated text to the user's terminal and displays it to the user through a smartphone application.

[0957] 5. Save and Share:

[0958] Users can save the generated content on their smartphones and can also share it with other users via social media.

[0959] Examples

[0960] For example, if a user enters the prompt "A Midsummer Night's Tale," the generative AI model will generate a story based on the prompt, like this:

[0961] "One summer night, under the stars, a boy sets out on a mysterious adventure. He finds a glowing stone in the forest and discovers that it has magical powers."

[0962] The generated story is displayed on the user's smartphone, where they can save it or share it on social media.

[0963] Similarly, if you input the prompt "future cityscape," the generative AI model will generate the following text:

[0964] "In the city of the future, we see floating buildings and self-driving cars. People use the latest technology to live comfortable lives."

[0965] In this way, the system can generate, display, save, and share a variety of content based on the prompt text entered by the user.

[0966] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0967] Step 1:

[0968] A user starts an application on a smartphone and inputs a prompt sentence. The input prompt sentence is sent to the server in text format. The input data is the prompt sentence input by the user, and the output data is the prompt sentence sent to the server.

[0969] Step 2:

[0970] The server analyzes the received prompt text and converts it into an appropriate format to be passed to the generative AI model. The input data is the prompt text sent by the user, and the output data is the prompt text to be passed to the generative AI model. Specific operations include preprocessing the text (removing unnecessary spaces and converting it to a specific format).

[0971] Step 3:

[0972] The server uses the OpenAI API to call the generative AI model and generate content based on the prompt sentence. The input data is the preprocessed prompt sentence, and the output data is the generated content (text). The specific operation is to send an API request and receive the generated text.

[0973] Step 4:

[0974] The server transmits the generated content to the user terminal. The input data is the content received from the generative AI model, and the output data is the content to be transmitted to the user terminal. The specific operation is to convert the text data into an appropriate format and transmit it to the user terminal via the network.

[0975] Step 5:

[0976] The user terminal displays the received content within the application. The input data is the content sent from the server, and the output data is the content displayed to the user. Specifically, the text data is applied to a layout for display on the screen and visually presented to the user.

[0977] Step 6:

[0978] The user can save or share the generated content. The input data is the displayed content, and the output data is a saved file or a shared link. Specifically, by pressing the save button or the share button, the content can be saved in the device or shared with other users via SNS, etc.

[0979] Example 2

[0980] Next, a second embodiment of the second form will be described. 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".

[0981] Conventional systems require users to manually create programs to process or visualize specific data, which is time-consuming and labor-intensive. In addition, creating programs requires specialized knowledge, making it difficult for general users.

[0982] 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.

[0983] In this invention, the server includes a means for receiving an instruction statement input by a user, a generating AI means for generating a program based on the received instruction statement, a means for executing the generated program, and a means for returning the execution result to the user. This enables a user to easily generate a program for data processing and visualization and obtain the execution result without specialized knowledge.

[0984] A "user" is an individual or group that utilizes the system to input instructions and obtain results of data processing and visualization.

[0985] "Instructions" are textual instructions entered by a user into the system, including requests for specific data processing or visualization.

[0986] A "generative AI means" is a means that uses artificial intelligence technology to automatically generate appropriate program code based on an instruction text.

[0987] "Program" means a set of instructions generated by a generative AI means for performing a particular data processing or visualization task.

[0988] The "execution means" refers to a means for executing the generated program, acquiring data from the database, processing the data, and visualizing it.

[0989] A "database" is a system for systematically storing and managing data, and stores user information and other data.

[0990] "Data processing" is the process of converting, filtering, and aggregating acquired data based on specific conditions.

[0991] "Visualization" is the process of displaying the results of data processing in a visual format such as a graph or chart.

[0992] The "execution results" are the results of data processing and visualization obtained by executing the generated program, and are returned to the user.

[0993] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0994] This invention is a system that automatically generates a program using a generative AI model based on a command statement input by a user, and executes the program to process and visualize data. A specific embodiment of this system will be described below.

[0995] System Configuration

[0996] The system mainly consists of the following components:

[0997] 1. User interface: An interface for users to input instructions. It uses a terminal such as a web browser.

[0998] 2. Server: Receives instructions, generates a program using a generative AI model, and executes the generated program.

[0999] 3. Generative AI model: An artificial intelligence model that generates programs based on instructions. Specifically, we use models that use natural language processing technology (e.g., GPT-4).

[1000] 4. Database: A system for storing data and retrieving it from a server, for example using a relational database management system such as MySQL.

[1001] 5. Data processing library: A software library for processing data. Specifically, we use the Python Pandas library.

[1002] 6. Visualization Library: A software library for visualizing data as graphs and charts. Specifically, we use the Python Matplotlib library.

[1003] Creating and running a program

[1004] A user inputs instructions through the system's web interface, for example, "Generate a program that displays a graph of the age distribution of users." The server sends the instructions to the generative AI model, which generates the appropriate program code.

[1005] The generated program does the following:

[1006] 1. Database connection: The server connects to the database and gets the required data.

[1007] 2. Data processing: The acquired data is converted into a data frame using the Pandas library, and then filtered and aggregated based on certain conditions.

[1008] 3. Data visualization: The processed data is visualized as graphs and charts using the Matplotlib library.

[1009] Examples

[1010] For example, if a user types in "Please generate a program that will graph the age distribution of our users," the server might do the following:

[1011] 1. The server uses a generative AI model to generate a program based on the user's instructions.

[1012] 2. The generated program retrieves user age data from the database and calculates the age distribution using the Pandas library.

[1013] 3. The calculated age distribution is visualized as a bar graph using the Matplotlib library.

[1014] 4. The server saves the generated graph as an image file and returns the file to the user.

[1015] In this way, users can easily create programs for data processing and visualization and obtain execution results, even without specialized knowledge.

[1016] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1017] Step 1:

[1018] The user enters a prompt sentence.

[1019] The user accesses the system's web interface and types in the text box, "Please generate a program that will display a graph showing the age distribution of users." Then, he clicks the "Submit" button.

[1020] Input: The prompt text entered by the user.

[1021] Output: The prompt text sent to the server.

[1022] Step 2:

[1023] The server generates the program using the generative AI model.

[1024] The server receives user input and sends prompt sentences to the generative AI model, which generates appropriate program code based on the prompt sentences.

[1025] Input: The prompt text sent by the user.

[1026] Output: The generated program code.

[1027] Step 3:

[1028] The server executes the generated program.

[1029] The server executes the generated program code. First, it connects to the database and gets the user's age data. Then, it converts the data into a data frame using the Pandas library and calculates the age distribution. Finally, it visualizes the age distribution as a bar graph using the Matplotlib library and saves it as an image file.

[1030] Input: The generated program code.

[1031] Output: Age distribution graph image file.

[1032] Step 4:

[1033] The server returns the execution result to the user.

[1034] The server returns the generated graph image to the user, who can download and view it through the system's web interface.

[1035] Input: Age distribution graph image file.

[1036] Output: The graph image file that is returned to the user.

[1037] (Application example 2)

[1038] Next, a description will be given of Application Example 2 of Form 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".

[1039] The traditional source code analysis and modernization process for legacy systems is often manual, time-consuming and labor-intensive. In addition, there is a lack of efficient means to generate personalized content based on prompts entered by users. This makes it difficult to quickly respond to user needs.

[1040] 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.

[1041] In this invention, the server includes a reverse engineering means for analyzing the source code of the legacy system, a means for updating the analyzed source code, a means including a generative AI model for automatically performing the above operations, a means for generating content based on a prompt sentence input by a user, and a means for displaying the generated content. This makes it possible to automate the source code analysis and updating process of the legacy system and to rapidly generate personalized content according to the needs of the user.

[1042] A "legacy system" is an information system that was built using old technologies or platforms and is still in use today.

[1043] "Source code" means textual code written in a programming language that describes the operation of a program.

[1044] "Reverse engineering" is the process of analyzing the structure and function of an existing system or software to understand its design and operation.

[1045] A "generative AI model" is an artificial intelligence model that generates new content based on given input data (prompt text).

[1046] A "prompt" is text that is input to a generative AI model to cause it to generate a particular output.

[1047] "Content" means any information or data made available to Users and may be in the form of text, images, video or other formats.

[1048] "Modernization" means restructuring existing source code to conform to new technical standards and programming languages.

[1049] "Automation" means that systems or processes are carried out automatically by machines or software with minimal human intervention.

[1050] "Personalization" means customizing content and services according to a user's individual needs and preferences.

[1051] "Display" refers to visually presenting the generated content to a user.

[1052] A system for implementing the present invention is configured as follows: A server includes reverse engineering means for analyzing source code of a legacy system, means for updating the analyzed source code, means including a generative AI model for automatically performing the above operations, means for generating content based on a prompt sentence input by a user, and means for displaying the generated content.

[1053] Program processing explanation

[1054] The server first analyzes the source code of the legacy system using reverse engineering techniques. This analysis allows the system's functions and structure to be understood. It then uses techniques to modernize the analyzed source code, reconstructing it in a way that complies with new technical standards and programming languages. This series of operations is automated using a generative AI model.

[1055] When a user inputs a prompt sentence through a terminal, the server uses a generative AI model to generate content based on the prompt sentence, and the generated content is provided to the user through the display means of the terminal.

[1056] Hardware and Software Used

[1057] Hardware: Servers, user devices (smartphones, tablets, PCs, etc.)

[1058] Software: Python, generative AI models (e.g. GPT-3), reverse engineering tools, source code renovation tools

[1059] Examples

[1060] For example, if a user enters the prompt "Generate a blog post about recent technology trends," the server will use a generative AI model to generate content like this:

[1061] Example prompt:

[1062] Generate blog posts about recent technology trends.

[1063] Examples of generated content:

[1064] Recent technology trends include artificial intelligence (AI), blockchain, 5G communications, and quantum computing. These technologies have the potential to dramatically change our daily lives and the way we do business. For example, AI plays a key role in medical diagnosis and the development of self-driving cars. Blockchain is attracting attention as a technology that will improve the transparency and security of financial transactions. 5G communications will provide faster and more reliable Internet connections, facilitating the widespread adoption of IoT devices. Quantum computing is expected to provide a new means of solving complex problems that cannot be solved by conventional computers.

[1065] In this way, personalized content that meets the user's needs can be quickly generated and provided.

[1066] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1067] Step 1:

[1068] The user enters a prompt through the terminal.

[1069] Input: A prompt entered by the user (e.g. "Generate a blog post about recent technology trends.")

[1070] Output: The prompt text is sent to the server.

[1071] Specific operation: The user inputs a prompt into the input field of the terminal and presses the send button. The terminal sends this prompt to the server.

[1072] Step 2:

[1073] The server receives the prompt text and passes it to the generative AI model.

[1074] Input: Prompt text sent from the terminal

[1075] Output: The prompt is fed into the generative AI model.

[1076] Specific operation: The server analyzes the prompt text received from the terminal and sends a request to the API of the generative AI model.

[1077] Step 3:

[1078] A generative AI model generates content based on the prompt.

[1079] Input: Prompt text passed from the server

[1080] Output: The generated content (e.g. blog post)

[1081] How it works: The generative AI model analyzes the prompt and generates text based on relevant information, which is then returned to the server.

[1082] Step 4:

[1083] The server receives the generated content and transmits it to the terminal.

[1084] Input: Generated content returned from a generative AI model

[1085] Output: Generated content is sent to a device.

[1086] Specific operation: The server converts the content received from the generative AI model into a data format for transmission to the terminal and sends it to the terminal.

[1087] Step 5:

[1088] The device displays the generated content.

[1089] Input: Generated content sent from the server

[1090] Output: The generated content is displayed to the user.

[1091] Specific operation: The device renders the content received from the server in the display area and provides it visually to the user.

[1092] In this way, a series of processes is realized in which content is generated using a generative AI model based on the prompt sentence entered by the user and provided to the user.

[1093] In addition, 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.

[1094] "Example 1"

[1095] In one embodiment of the present invention, the AI ​​means includes an emotion engine that recognizes the user's emotion. The emotion engine estimates the user's emotional state from the user's facial expression, tone of voice, choice of words, etc. For example, if the user feels irritated, the emotion engine communicates the information to the AI ​​means. The AI ​​means uses this information to adjust the priority of source code renewal. Specifically, if the user feels irritated, the AI ​​means prioritizes renewal of the source code of the part that the user is interested in. In addition, the emotion engine reports the progress of the source code renewal according to the user's emotional state. For example, if the user feels calm, the emotion engine communicates the information to the AI ​​means, and the AI ​​means provides the user with a detailed report of the progress. This provides appropriate feedback according to the user's emotional state, improving the user experience.

[1096] The process flow of each embodiment will be described below.

[1097] "Example 1"

[1098] Step 1: The emotion engine infers the user's emotional state from their facial expressions, tone of voice, choice of words, etc.

[1099] Step 2: The emotion engine communicates the estimated emotional state to the AI ​​means.

[1100] Step 3: The AI ​​means adjusts the priority of source code updating using the information received from the emotion engine. Specifically, if the user feels frustrated, the AI ​​means prioritizes updating the source code of the part that the user is interested in.

[1101] Step 4: The emotion engine reports the progress of the source code update according to the user's emotional state. For example, if the user feels calm, the emotion engine conveys that information to the AI ​​means, and the AI ​​means provides the user with a detailed report of the progress.

[1102] Example 1

[1103] Next, a description will be given of Example 1 of the embodiment 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".

[1104] Conventional methods for analyzing and updating source code of legacy systems required a lot of manual operations and were inefficient. In addition, even text generation systems using generative AI models lacked the means to properly process user input and effectively use the generated text. This made it difficult to respond to user requests quickly and accurately.

[1105] The identification process by the identification processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes a reverse engineering means for analyzing the source code of the legacy system, a means for updating the analyzed source code, a means for receiving a prompt sentence input by a user, a means for preprocessing the received prompt sentence, a means for inputting the preprocessed prompt sentence into a generative AI model and acquiring the generated text, a means for post-processing the generated text, and an AI means for automatically performing the above operations. This enables efficient analysis and updating of the legacy system and automation of text generation using the generative AI model.

[1106] A "legacy system" is an information system that has been in use for a long time but is still in operation today.

[1107] "Source code" refers to textual code that describes the operation of a program.

[1108] "Reverse engineering" is a technique for analyzing the structure and operation of existing systems or software and extracting their design information.

[1109] A "prompt" is text that is entered as an instruction or question into a generative AI model.

[1110] "Preprocessing" refers to the process of converting data into an appropriate format before inputting it into a generative AI model.

[1111] A "generative AI model" is a model that uses artificial intelligence to generate text and data.

[1112] "Post-processing" refers to the process of converting the data output from a generative AI model into a format that is easy for humans to understand.

[1113] "AI means" refers to means of automating specific operations or processes using artificial intelligence technology.

[1114] This invention combines a system for analyzing and updating the source code of a legacy system with a system for generating text based on a prompt sentence entered by a user using a generative AI model. A specific embodiment of this system is shown below.

[1115] The server uses reverse engineering techniques to analyze the source code of the legacy system. The reverse engineering techniques are techniques for analyzing the structure and operation of existing systems and software and extracting their design information. The analyzed source code is reconstructed by modernization techniques in a form that conforms to new technical standards and programming languages.

[1116] A user inputs a prompt into an input field on the device, for example, "Use AI to generate a poem about spring." The device sends this prompt to the server, which then preprocesses the received prompt and converts it into a format suitable for the generative AI model. Preprocessing includes tokenizing and normalizing the text.

[1117] The server inputs the preprocessed prompt sentence into a generative AI model. This generative AI model uses, for example, a general generative AI model. The generative AI model generates new text based on the input prompt sentence. The generated text is returned to the server.

[1118] The server post-processes the generated text and converts it into a human-readable form, which includes decoding and formatting the generated text, and sends the post-processed text to the terminal for display to the user.

[1119] For example, if a user inputs "Use AI to generate a poem about spring," the server inputs this prompt into the generative AI model and generates a poem about spring. The generated poem is displayed to the user.

[1120] This system realizes efficient analysis and renovation of legacy systems and automation of text generation using generative AI models. The hardware used is a high-performance GPU (e.g., a general high-performance GPU), and the software uses Python and TensorFlow.

[1121] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1122] Step 1:

[1123] The user enters a prompt sentence.

[1124] The user inputs a prompt for the generative AI model into the input field of the terminal. For example, the user inputs, "Please use AI to generate a poem about spring." The input prompt becomes the input data for the terminal.

[1125] Step 2:

[1126] The terminal sends a prompt to the server.

[1127] The terminal sends the prompt text entered by the user to the server using an HTTP request. The input is the prompt text entered by the user, and the output is the prompt text sent to the server.

[1128] Step 3:

[1129] The server receives the prompt and performs data preprocessing.

[1130] The server receives the prompt sentence sent from the terminal. It tokenizes the received prompt sentence and converts it into a format suitable for the generative AI model. Specifically, it splits the text into words and subwords and converts it into a numerical vector. The input is the prompt sentence received from the terminal, and the output is the preprocessed numerical vector.

[1131] Step 4:

[1132] The server inputs a prompt sentence into the generative AI model and obtains the generated text.

[1133] The server inputs the preprocessed prompt sentence into the generative AI model. The generative AI model generates new text based on the input prompt sentence. The generated text is returned to the server. The input is the preprocessed numeric vector, and the output is the generated text.

[1134] Step 5:

[1135] The server post-processes the generated text and sends it to the terminal.

[1136] The server post-processes the text returned by the generative AI model. Specifically, it decodes the generated text and converts it into a human-readable format. It then sends the generated text to the terminal. The input is the text returned by the generative AI model, and the output is the post-processed text.

[1137] Step 6:

[1138] The terminal displays the generated text to the user.

[1139] The terminal displays the generated text received from the server to the user, who can then review the generated poem or piece of writing. The input is the post-processed text received from the server, and the output is the text displayed to the user.

[1140] (Application example 1)

[1141] Next, a description will be given of Application Example 1 of Form 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".

[1142] The process of analyzing and updating the source code of traditional legacy systems is very time-consuming and labor-intensive if done manually. In addition, there is a lack of efficient means to generate personalized content based on prompts entered by users. This makes it difficult to respond quickly to user needs.

[1143] 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.

[1144] In this invention, the server includes a reverse engineering means for analyzing the source code of the legacy system, a means for updating the analyzed source code, an artificial intelligence means for automatically performing the above operations, a generative AI model means for generating content based on a prompt sentence input by a user, and a means for displaying the generated content to a user. This enables efficient analysis and updating of the legacy system, and further enables rapid generation and provision of personalized content based on the user's prompt sentence.

[1145] "Reverse engineering methods" are methods for analyzing the source code of a legacy system and understanding its functions and structure.

[1146] "Innovation measures" are measures to reconstruct the analyzed source code in a way that complies with new technical standards or programming languages.

[1147] "Artificial intelligence means" means means for automating reverse engineering and source code renovation operations.

[1148] A "generative AI model means" is a means for generating content based on a prompt sentence entered by a user.

[1149] The "display means" refers to a means for displaying the generated content to the user.

[1150] The following system configuration and processing procedure will be described as an embodiment of the present invention.

[1151] The server includes reverse engineering means for analyzing source code of the legacy system, means for updating the analyzed source code, artificial intelligence means for automatically performing the above operations, generative AI model means for generating content based on a prompt sentence input by a user, and means for displaying the generated content to a user.

[1152] Specifically, the server is implemented using a Python program and the OpenAI API. The reverse engineering means analyzes the source code of the legacy system to understand its functions and structure. The analysis results are reconstructed by the modernization means in a form that complies with new technical standards and programming languages. These operations are automated by the artificial intelligence means.

[1153] When a user inputs a prompt sentence using the smartphone, the prompt sentence is sent to the generative AI model means. The generative AI model means generates content based on the prompt sentence using a generative AI model (e.g., text-davinci-003) of OpenAI. The generated content is displayed to the user by the display means.

[1154] For example, if a user inputs "I want to write a blog about my travel memories. Please write in detail about my trip to Okinawa with my family last summer," the generative AI model means will generate a detailed blog post based on this prompt. The generated blog post will be displayed on the smartphone screen for the user to check.

[1155] The system enables efficient analysis and modernization of legacy systems, as well as the rapid generation and delivery of personalized content based on user prompts.

[1156] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1157] Step 1:

[1158] A user inputs a prompt sentence using a smartphone. The input prompt sentence is sent to a server. The input data is the prompt sentence in text format input by the user.

[1159] Step 2:

[1160] The server passes the received prompt sentence to the generative AI model means. The generative AI model means analyzes the prompt sentence and processes the data to generate appropriate content. Specifically, it uses OpenAI's generative AI model (e.g., text-davinci-003) to generate text based on the prompt sentence. The input data is the prompt sentence, and the output data is the generated content.

[1161] Step 3:

[1162] The content generated by the generative AI model means is temporarily stored in the server. The server passes the generated content to the display means. The input data is the generated content, and the output data is the content passed to the display means.

[1163] Step 4:

[1164] The display means displays the generated content on the user's smartphone. The user can check the generated content on the screen of the smartphone. The input data is the content passed to the display means, and the output data is the content displayed on the screen of the smartphone.

[1165] Step 5:

[1166] The user can check the displayed content and modify or save it as necessary. The input data is the content displayed on the smartphone screen, and the output data is the content modified or saved by the user's operation.

[1167] In this way, a series of processes is realized in which personalized content is generated using a generative AI model based on the prompt sentence entered by the user and provided to the user.

[1168] Example 2

[1169] Next, a second embodiment of the second form will be described. 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".

[1170] The source code analysis and renovation process of conventional legacy systems is often done manually, which takes time and effort. In addition, even in text generation systems using generative AI models, the process from when the user enters a prompt sentence to when they receive the generated text is complicated, making it difficult to perform efficiently. To solve these issues, it is necessary to automate the source code analysis and renovation process and to streamline the text generation process using generative AI models.

[1171] 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.

[1172] In this invention, the server includes a reverse engineering means for analyzing the source code of the legacy system, a means for updating the analyzed source code, an artificial intelligence means for automatically performing the above operations, a means for a user to input a prompt sentence, a means for a terminal to send the prompt sentence to the server, a means for the server to call a generative AI model, a means for the generative AI model to generate text, a means for the server to send the generated text to the terminal, and a means for the terminal to display the generated text. This makes it possible to automate the source code analysis and updating process and to streamline the text generation process using the generative AI model.

[1173] "Legacy systems" refers to existing, older information systems or software that are typically not aligned with new technologies and standards.

[1174] "Source code" means textual code written in a programming language that describes the operation of a program.

[1175] "Reverse engineering" is a technique for analyzing the structure and function of an existing system or software to understand its design and operation.

[1176] "Artificial intelligence" refers to computer systems and algorithms that mimic human intelligence and can learn, reason, and solve problems.

[1177] A "prompt sentence" is an instruction entered by a user to request a generative AI model to generate specific text.

[1178] "Terminal" refers to a device such as a computer or smartphone operated by a user.

[1179] A "server" is a computer system that provides services to other computers and devices over a network.

[1180] A "generative AI model" is an artificial intelligence model that generates text based on an input prompt.

[1181] "Text generation" is the process by which a generative AI model creates new text based on a given prompt.

[1182] An "HTTP request" is a protocol that allows a client (terminal) to request a server to send data or obtain information.

[1183] The "JSON format" is a lightweight data exchange format for expressing data in text format.

[1184] An "API Request" is a request to other software or services to perform a function through an application program interface.

[1185] "HTML" is a markup language for constructing web pages.

[1186] "UI components" refers to the individual elements or parts that make up a user interface.

[1187] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[1188] The present invention is a system for automating the source code analysis and renewal process of a legacy system and for streamlining the text generation process using a generative AI model. A specific embodiment of the system will be described below.

[1189] Hardware and Software Used

[1190] 1. Server

[1191] The server includes reverse engineering and artificial intelligence means for analyzing and updating the source code of the legacy system, and is capable of handling API requests for invoking generative AI models and generating text.

[1192] 2. Terminal

[1193] A terminal is a device where a user inputs a prompt text and sends it to a server. Specifically, this applies to PCs and smartphones.

[1194] 3. Generative AI Models

[1195] A generative AI model is an artificial intelligence model that generates text based on a prompt sentence when called by a server, such as OpenAI's GPT-3.

[1196] Data processing and calculation

[1197] 1. Enter and send a prompt

[1198] A user inputs a prompt through the terminal interface, for example, "Write a short story about a cat." The terminal sends this prompt as an HTTP request to the server.

[1199] 2. Parsing the prompt and calling the generative AI model

[1200] The server analyzes the received prompt sentence and calls the generative AI model. Specifically, it generates an API request including the prompt sentence and sends it to the endpoint of the generative AI model.

[1201] 3. Generating text and sending a response

[1202] The generative AI model generates text based on the input prompt. For example, in response to the prompt "Write a short story about a cat," it generates the text "Once upon a time, there lived a cat in a small village..." The generated text is returned to the server as an API response.

[1203] 4. Viewing the generated text

[1204] The server sends the text received from the generative AI model to the user's device, which displays the received text on the screen so that the user can check it.

[1205] Examples

[1206] Examples of prompt statements

[1207] "Write a short story about a cat."

[1208] "Write a poem about the city of the future."

[1209] "Please explain how AI will change human life."

[1210] In this way, users can use generative AI models to generate various texts. This system automates the source code analysis and renovation process of legacy systems, and streamlines the text generation process using generative AI models.

[1211] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1212] Step 1:

[1213] The user enters a prompt sentence.

[1214] The user inputs a prompt through the terminal interface, for example, "Write a short story about a cat" in the text input field and clicks the send button. The input prompt is temporarily stored in the terminal's memory.

[1215] Step 2:

[1216] The terminal sends a prompt to the server.

[1217] The terminal sends the prompt text entered by the user to the server as an HTTP request. Specifically, the prompt text is packaged as JSON format data and sent to the server via the Internet. The input is the prompt text, and the output is an HTTP request to the server.

[1218] Step 3:

[1219] The server calls the generative AI model.

[1220] The server analyzes the received prompt sentence and calls the generative AI model. Specifically, it generates an API request containing the prompt sentence and sends it to the endpoint of the generative AI model. The input is an HTTP request containing the prompt sentence, and the output is an API request to the generative AI model.

[1221] Step 4:

[1222] A generative AI model generates the text.

[1223] A generative AI model generates text based on a prompt received from the server. For example, in response to the prompt "Write a short story about a cat," the model generates the text "Once upon a time, in a small village, there lived a cat..." The input is the prompt, and the output is the generated text.

[1224] Step 5:

[1225] The server sends the generated text to the terminal.

[1226] The server sends the text received from the generative AI model to the user's terminal. Specifically, it generates an HTTP response containing the generated text and sends it back to the terminal. The input is the generated text, and the output is the HTTP response to the terminal.

[1227] Step 6:

[1228] The terminal displays the generated text.

[1229] The device displays the generated text received from the server on the screen. Specifically, it inserts the received text into HTML and app UI components and displays it in a form that is visible to the user. The input is the generated text, and the output is the text that is displayed to the user.

[1230] (Application example 2)

[1231] Next, a description will be given of Application Example 2 of Form 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".

[1232] Conventional methods for analyzing and updating source code of legacy systems required a lot of manual operations and were inefficient. In addition, there was no technology to generate content using generative AI models based on prompts entered by users, making it difficult to generate personalized content. This meant that content could not be provided according to user needs, and the user experience did not improve.

[1233] 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.

[1234] In this invention, the server includes a reverse engineering means for analyzing the source code of the legacy system, a means for updating the analyzed source code, an artificial intelligence means for automatically performing the above operations, a means for generating content using a generative AI model based on a prompt sentence input by a user, and a means for displaying the generated content. This enables efficient analysis and updating of the legacy system, and further enables personalized content generation according to the needs of the user.

[1235] "Reverse engineering methods" are methods for analyzing the source code of an existing system or software to understand its functions and structure.

[1236] "Moderation measures" refer to measures that, based on the analyzed source code, reconstruct the source code in a manner that conforms to new technical standards or programming languages ​​while maintaining existing functions.

[1237] "Artificial intelligence means" means artificial intelligence technology used to automatically perform a specific operation or process.

[1238] A "prompt sentence" is the text that a user inputs into a generative AI model, and is the sentence that forms the basis for the generated content.

[1239] A "generative AI model" is an artificial intelligence model that generates content such as text and images based on input prompts.

[1240] A "content generation means" is a means for generating content using a generative AI model based on a prompt sentence entered by a user.

[1241] The "display means" refers to a means for visually presenting the generated content to the user.

[1242] A system for implementing the present invention is configured as follows: A server includes reverse engineering means for analyzing source code of a legacy system, means for updating the analyzed source code, artificial intelligence means for automatically performing the above operations, means for generating content using a generative AI model based on a prompt sentence input by a user, and means for displaying the generated content.

[1243] First, the server analyzes the source code of the legacy system using reverse engineering techniques. These techniques analyze the source code of the existing system or software to understand its functions and structure. Next, the analyzed source code is reconstructed using modernization techniques. These techniques reconstruct the source code in a way that conforms to new technical standards and programming languages ​​while maintaining the existing functions.

[1244] Moreover, artificial intelligence means will perform these operations automatically, eliminating the need for manual operations and enabling efficient analysis and renewal.

[1245] Also included is a means for generating content using a generative AI model based on a prompt sentence input by a user. A prompt sentence is a text input by a user to a generative AI model, and is the sentence that serves as the basis for the generated content. A generative AI model is an artificial intelligence model that generates content such as text and images based on the input prompt sentence.

[1246] The generated content is visually presented to the user by a display means, which is for visually presenting the generated content to the user.

[1247] As a concrete example, if a user wants to generate a story on the theme of "space exploration," they might enter the following prompt:

[1248] "Generate a space exploration story about a young scientist and his adventures exploring an unknown planet."

[1249] Given this prompt, a generative AI model might generate a story like this:

[1250] "A young scientist steps onto an unknown planet. His mission is to explore the planet's ecosystem and discover new life forms. The planet's sky glows purple, and strange plants grow on the surface. He carefully steps forward and eventually discovers a huge crystal cave. Inside the cave, there live glowing creatures, who welcome him. Through his adventure on this planet, he discovers the mysteries of the universe and new friends."

[1251] In this way, personalized content can be generated according to the user's needs.

[1252] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1253] Step 1:

[1254] The user enters a prompt sentence at the terminal.

[1255] Input: A prompt entered by the user (e.g., "Generate a story about space exploration. The protagonist is a young scientist and his adventures exploring an unknown planet.")

[1256] Output: The prompt text is sent to the server.

[1257] Step 2:

[1258] The server receives the prompt text and passes it to the generative AI model.

[1259] Input: The prompt text sent by the user

[1260] Output: The prompt passed to the generative AI model

[1261] Step 3:

[1262] A generative AI model generates content based on the prompt.

[1263] Input: The prompt given to the generative AI model

[1264] Output: Generated content (e.g. a story)

[1265] Step 4:

[1266] The server receives the generated content and passes it to the display means.

[1267] Input: Content output from a generative AI model

[1268] Output: The content that is passed to the display

[1269] Step 5:

[1270] The terminal uses a display means to visually present the generated content to the user.

[1271] Input: Content sent from the server

[1272] Output: The content that is displayed to the user

[1273] 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 voice indicating a user input for 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.

[1274] 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> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to 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.

[1275] Another example of generative AI is Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1276] 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.

[1277] [Fourth embodiment]

[1278] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1279] 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.

[1280] 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 wide area network (WAN) and / or a local area network (LAN).

[1281] 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. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1282] 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 the voice according to instructions from the processor 46.

[1283] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[1284] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[1285] The control target 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, legs, etc. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, legs, etc. 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.

[1286] Fig. 8 shows an example of 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.

[1287] 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.

[1288] 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.

[1289] In the robot 414, the 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.

[1290] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1291] "Example 1"

[1292] The present invention is a system including a reverse engineering means for analyzing the source code of a legacy system, a means for updating the analyzed source code, and an AI means for automatically performing these operations. The reverse engineering means receives the source code of the legacy system as an input and grasps its functions and structure. Specifically, it analyzes the function call relationships, variable usage, control flow, etc. Next, the source code updating means reconstructs the source code in a form that conforms to new technical standards and programming languages ​​based on the analysis results. Specifically, it converts from an old programming language to a new programming language, optimizes inefficient algorithms, deletes redundant code, etc. Finally, the AI ​​means performs these operations automatically. Specifically, it automates the reverse engineering and source code updating as a series of processes, minimizing human intervention. This makes it possible to efficiently update legacy systems and support the promotion of DX.

[1293] The process flow of each embodiment will be described below.

[1294] "Example 1"

[1295] Step 1: Input the source code of the legacy system into the reverse engineering tool. In this step, the source code of the legacy system is provided in a format that can be analyzed. For example, source code written in an old programming language such as COBOL or FORTRAN is the target.

[1296] Step 2: Reverse engineering techniques analyze the source code. In this step, the structure and functions of the source code are understood. Specifically, function call relationships, variable usage, control flow, etc. are analyzed.

[1297] Step 3: Input the analysis results to the source code innovation means: In this step, the information analyzed by the reverse engineering means is passed to the source code innovation means.

[1298] Step 4: The source code modernization tool generates new source code. In this step, the source code is restructured based on the analysis results in a way that makes it compatible with new technical standards and programming languages. Specifically, this involves converting old programming languages ​​to new ones, optimizing inefficient algorithms, and removing redundant code.

[1299] Step 5: AI means performs the above steps automatically. In this step, reverse engineering and source code renewal are automated as a series of processes, minimizing human intervention. This allows legacy systems to be renewed efficiently, helping to promote DX.

[1300] Example 1

[1301] Next, a description will be given of Example 1 of the embodiment 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".

[1302] In conventional systems, the process of generating appropriate text based on the prompt sentence entered by the user was complicated and difficult to perform efficiently. In addition, there was a lack of means to quickly return the generated text to the user, which resulted in a poor user experience. Furthermore, there was no interface for effectively utilizing the generative AI model, which created a high technical hurdle.

[1303] 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.

[1304] In this invention, the server includes a means for receiving a prompt sentence input by a user, a means for passing the received prompt sentence to a generative AI model, a means for the generative AI model to generate text based on the prompt sentence, and a means for returning the generated text to the user. This makes it possible to efficiently generate text based on the prompt sentence input by the user and quickly return it to the user.

[1305] A "user" is a person or organization that utilizes the system to enter prompts and receive generated text.

[1306] A "prompt" is a textual instruction or question that a user enters into a generative AI model.

[1307] A "generative AI model" is an artificial intelligence algorithm or system that generates natural language text based on a prompt.

[1308] The "server" is a computer system whose role is to receive a prompt from a user, pass it to a generative AI model, and return the generated text to the user.

[1309] An "HTTP request" is a request for data communication sent from a user's terminal to a server, and may include a prompt text.

[1310] An "HTTP response" is a data communication response sent from a server to a user's terminal, and may include generated text.

[1311] A "terminal" is a device used by a user to enter prompts and receive generated text, including a personal computer or a smartphone.

[1312] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[1313] This invention is a system that generates text using a generative AI model based on a prompt sentence input by a user and returns the text to the user. A specific embodiment for implementing this system is described below.

[1314] Hardware and Software Used

[1315] Hardware: Terminals used by users (personal computers, smartphones, etc.), servers (equipped with high-performance CPUs and GPUs)

[1316] Software: Generative AI models (e.g. GPT-3), database management systems (e.g. MySQL), web servers (e.g. Apache)

[1317] System Overview

[1318] The user uses a terminal to input a prompt sentence into an input form on a web browser. The input prompt sentence is sent to the server as an HTTP request. The server passes the received prompt sentence to the generative AI model, which then generates text based on the prompt sentence. The generated text is returned to the user's terminal via the server and displayed on the web browser.

[1319] Examples

[1320] User input: "Tell me about the city of the future."

[1321] Output of generative AI model: “Cities of the future will be powered by sustainable energy sources and run efficiently using smart technology.”

[1322] Examples of prompt statements

[1323] "Please tell me how AI is being used in the medical field."

[1324] "Please explain the next generation transportation system."

[1325] "Please tell me about the effects of climate change and how to combat them."

[1326] The system can efficiently generate text based on the prompt sentence entered by the user and quickly return it to the user, improving the user experience and effectively utilizing the generative AI model.

[1327] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1328] Step 1:

[1329] The user enters a prompt sentence.

[1330] A user opens a web browser and accesses an input form of the system. For example, the user inputs a prompt such as "Tell me about the city of the future." The input form has a text box where the user can input any text. The input data is the prompt.

[1331] Step 2:

[1332] The terminal sends a prompt to the server.

[1333] When the user clicks the "Send" button, the terminal sends the entered prompt text to the server as an HTTP POST request. This request includes the text data entered by the user. The input data is the prompt text, and the output data is the HTTP request.

[1334] Step 3:

[1335] The server receives the prompt.

[1336] The server receives the HTTP request and extracts the prompt from the request body. The server temporarily stores the prompt for further processing. The input data is the HTTP request, and the output data is the prompt.

[1337] Step 4:

[1338] The server passes the prompt to the generative AI model.

[1339] The server sends an API request to the generative AI model. This request includes the prompt text entered by the user. The server accesses the API endpoint of the generative AI model and passes the prompt text. The input data is the prompt text, and the output data is the API request.

[1340] Step 5:

[1341] A generative AI model generates the text.

[1342] The generative AI model generates text based on the received prompt. For example, it generates text such as "Future cities will be run efficiently using smart technology with sustainable energy sources." The generated text is returned to the server as an API response. The input data is the prompt, and the output data is the generated text.

[1343] Step 6:

[1344] The server receives the generated text.

[1345] The server receives the API response from the generative AI model and extracts the generated text. The server temporarily stores this text for further processing. The input data is the API response, and the output data is the generated text.

[1346] Step 7:

[1347] The server sends the generated text to the terminal.

[1348] The server sends the generated text to the user's terminal as an HTTP response, which contains the generated text. The input data is the generated text, and the output data is the HTTP response.

[1349] Step 8:

[1350] The terminal displays the generated text.

[1351] The user's device displays the received text on a web browser. The user can check the generated text on the browser. For example, the text "Cities of the future will be run efficiently using smart technology, powered by sustainable energy sources." is displayed. The input data is the HTTP response, and the output data is the displayed text.

[1352] (Application example 1)

[1353] Next, a description will be given of Application Example 1 of Form 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".

[1354] The process of analyzing and updating the source code of traditional legacy systems is often done manually, which takes time and effort. In addition, even in content generation using generative AI models, there is a lack of efficient means to automatically generate content based on prompts entered by users, and then display, save, and share it. This leads to issues such as a lack of improvement in the user experience and a decrease in the efficiency of the entire system.

[1355] 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. In this invention, the server includes a reverse engineering means for analyzing the source code of the legacy system, a means for updating the analyzed source code, an artificial intelligence means for automatically performing the above operations, a generation AI model means for generating content based on a prompt sentence input by the user, a means for displaying the generated content, and a means for saving and sharing the generated content. This makes it possible to automate the process of analyzing and updating the source code of the legacy system, and further to efficiently generate, display, save, and share content based on the prompt sentence input by the user.

[1356] A "legacy system" is an information system that was built using old technologies and programming languages ​​and is still in operation today.

[1357] "Source code" means textual code written in a programming language that describes the operation of a program.

[1358] "Reverse engineering methods" are methods for analyzing the structure and functions of existing systems or software and extracting the original design information.

[1359] "Modernization" refers to the restructuring of existing source code to accommodate new technical standards or programming languages.

[1360] "Artificial intelligence means" means means for automatically performing certain tasks using machine learning or data analysis.

[1361] A "prompt" is text that is input into a generative AI model and serves as the basis for the generated content.

[1362] A "generative AI model means" is an artificial intelligence model for generating content such as text, images, and audio based on an input prompt sentence.

[1363] A "means for displaying" is a means for visually presenting the generated content to a user.

[1364] "Storage and sharing means" means the means for storing the generated content in digital form and sharing it with other users and systems.

[1365] The following system configuration and processing procedure will be described as an embodiment of the present invention.

[1366] System Configuration

[1367] This system uses the following hardware and software:

[1368] Hardware: Server, user device (smartphone)

[1369] Software: Python, OpenAI API

[1370] Process Overview

[1371] 1. Enter the prompt:

[1372] The user inputs a prompt sentence through a smartphone application, such as "A summer night's story" or "A cityscape of the future."

[1373] 2. Invoke the generative AI model:

[1374] The server receives the input prompt sentence and uses the OpenAI API to call a generative AI model (e.g., GPT-4), which generates text based on the prompt sentence.

[1375] 3. Content generation:

[1376] A generative AI model generates text based on the prompt, which corresponds to the prompt entered by the user.

[1377] 4. Viewing Content:

[1378] The server sends the generated text to the user's terminal and displays it to the user through a smartphone application.

[1379] 5. Save and Share:

[1380] Users can save the generated content on their smartphones and can also share it with other users via social media.

[1381] Examples

[1382] For example, if a user enters the prompt "A Midsummer Night's Tale," the generative AI model will generate a story based on the prompt, like this:

[1383] "One summer night, under the stars, a boy sets out on a mysterious adventure. He finds a glowing stone in the forest and discovers that it has magical powers."

[1384] The generated story is displayed on the user's smartphone, where they can save it or share it on social media.

[1385] Similarly, if you input the prompt "future cityscape," the generative AI model will generate the following text:

[1386] "In the city of the future, we see floating buildings and self-driving cars. People use the latest technology to live comfortable lives."

[1387] In this way, the system can generate, display, save, and share a variety of content based on the prompt text entered by the user.

[1388] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1389] Step 1:

[1390] A user starts an application on a smartphone and inputs a prompt sentence. The input prompt sentence is sent to the server in text format. The input data is the prompt sentence input by the user, and the output data is the prompt sentence sent to the server.

[1391] Step 2:

[1392] The server analyzes the received prompt text and converts it into an appropriate format to be passed to the generative AI model. The input data is the prompt text sent by the user, and the output data is the prompt text to be passed to the generative AI model. Specific operations include preprocessing the text (removing unnecessary spaces and converting it to a specific format).

[1393] Step 3:

[1394] The server uses the OpenAI API to call the generative AI model and generate content based on the prompt sentence. The input data is the preprocessed prompt sentence, and the output data is the generated content (text). The specific operation is to send an API request and receive the generated text.

[1395] Step 4:

[1396] The server transmits the generated content to the user terminal. The input data is the content received from the generative AI model, and the output data is the content to be transmitted to the user terminal. The specific operation is to convert the text data into an appropriate format and transmit it to the user terminal via the network.

[1397] Step 5:

[1398] The user terminal displays the received content within the application. The input data is the content sent from the server, and the output data is the content displayed to the user. Specifically, the text data is applied to a layout for display on the screen and visually presented to the user.

[1399] Step 6:

[1400] The user can save or share the generated content. The input data is the displayed content, and the output data is a saved file or a shared link. Specifically, by pressing the save button or the share button, the content can be saved in the device or shared with other users via SNS, etc.

[1401] Example 2

[1402] Next, a second embodiment of the second embodiment will be described. 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".

[1403] Conventional systems require users to manually create programs to process or visualize specific data, which is time-consuming and labor-intensive. In addition, creating programs requires specialized knowledge, making it difficult for general users.

[1404] 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.

[1405] In this invention, the server includes a means for receiving an instruction statement input by a user, a generating AI means for generating a program based on the received instruction statement, a means for executing the generated program, and a means for returning the execution result to the user. This enables a user to easily generate a program for data processing and visualization and obtain the execution result without specialized knowledge.

[1406] A "user" is an individual or group that utilizes the system to input instructions and obtain results of data processing and visualization.

[1407] "Instructions" are textual instructions entered by a user into the system, including requests for specific data processing or visualization.

[1408] A "generative AI means" is a means that uses artificial intelligence technology to automatically generate appropriate program code based on an instruction text.

[1409] "Program" means a set of instructions generated by a generative AI means for performing certain data processing or visualization tasks.

[1410] The "execution means" refers to a means for executing the generated program, acquiring data from the database, processing the data, and visualizing it.

[1411] A "database" is a system for systematically storing and managing data, and stores user information and other data.

[1412] "Data processing" is the process of converting, filtering, and aggregating acquired data based on specific conditions.

[1413] "Visualization" is the process of displaying the results of data processing in a visual format such as a graph or chart.

[1414] The "execution results" are the results of data processing and visualization obtained by executing the generated program, and are returned to the user.

[1415] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[1416] This invention is a system that automatically generates a program using a generative AI model based on a command statement input by a user, and executes the program to process and visualize data. A specific embodiment of this system will be described below.

[1417] System Configuration

[1418] The system mainly consists of the following components:

[1419] 1. User interface: An interface for users to input instructions. It uses a terminal such as a web browser.

[1420] 2. Server: Receives instructions, generates a program using a generative AI model, and executes the generated program.

[1421] 3. Generative AI model: An artificial intelligence model that generates programs based on instructions. Specifically, we use models that use natural language processing technology (e.g., GPT-4).

[1422] 4. Database: A system for storing data and retrieving it from a server, for example using a relational database management system such as MySQL.

[1423] 5. Data processing library: A software library for processing data. Specifically, we use the Python Pandas library.

[1424] 6. Visualization Library: A software library for visualizing data as graphs and charts. Specifically, we use the Python Matplotlib library.

[1425] Creating and running a program

[1426] A user inputs instructions through the system's web interface, for example, "Generate a program that displays a graph of the age distribution of users." The server sends the instructions to the generative AI model, which generates the appropriate program code.

[1427] The generated program does the following:

[1428] 1. Database connection: The server connects to the database and gets the required data.

[1429] 2. Data processing: The acquired data is converted into a data frame using the Pandas library, and then filtered and aggregated based on certain conditions.

[1430] 3. Data visualization: The processed data is visualized as graphs and charts using the Matplotlib library.

[1431] Examples

[1432] For example, if a user types in "Please generate a program that will graph the age distribution of our users," the server might do the following:

[1433] 1. The server uses a generative AI model to generate a program based on the user's instructions.

[1434] 2. The generated program retrieves user age data from the database and calculates the age distribution using the Pandas library.

[1435] 3. The calculated age distribution is visualized as a bar graph using the Matplotlib library.

[1436] 4. The server saves the generated graph as an image file and returns the file to the user.

[1437] In this way, users can easily create programs for data processing and visualization and obtain execution results, even without specialized knowledge.

[1438] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1439] Step 1:

[1440] The user enters a prompt sentence.

[1441] The user accesses the system's web interface and types in the text box, "Please generate a program that will display a graph showing the age distribution of users." Then, he clicks the "Submit" button.

[1442] Input: The prompt text entered by the user.

[1443] Output: The prompt text sent to the server.

[1444] Step 2:

[1445] The server generates the program using the generative AI model.

[1446] The server receives user input and sends prompt sentences to the generative AI model, which generates appropriate program code based on the prompt sentences.

[1447] Input: The prompt text sent by the user.

[1448] Output: The generated program code.

[1449] Step 3:

[1450] The server executes the generated program.

[1451] The server executes the generated program code. First, it connects to the database and gets the user's age data. Then, it converts the data into a data frame using the Pandas library and calculates the age distribution. Finally, it visualizes the age distribution as a bar graph using the Matplotlib library and saves it as an image file.

[1452] Input: The generated program code.

[1453] Output: Age distribution graph image file.

[1454] Step 4:

[1455] The server returns the execution result to the user.

[1456] The server returns the generated graph image to the user, who can download and view it through the system's web interface.

[1457] Input: Age distribution graph image file.

[1458] Output: The graph image file that is returned to the user.

[1459] (Application example 2)

[1460] Next, a description will be given of Application Example 2 of Form 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".

[1461] The traditional source code analysis and modernization process for legacy systems is often manual, time-consuming and labor-intensive. In addition, there is a lack of efficient means to generate personalized content based on prompts entered by users. This makes it difficult to quickly respond to user needs.

[1462] 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.

[1463] In this invention, the server includes a reverse engineering means for analyzing the source code of the legacy system, a means for updating the analyzed source code, a means including a generative AI model for automatically performing the above operations, a means for generating content based on a prompt sentence input by a user, and a means for displaying the generated content. This makes it possible to automate the source code analysis and updating process of the legacy system and to rapidly generate personalized content according to the needs of the user.

[1464] A "legacy system" is an information system that was built using old technologies or platforms and is still in use today.

[1465] "Source code" means textual code written in a programming language that describes the operation of a program.

[1466] "Reverse engineering" is the process of analyzing the structure and function of an existing system or software to understand its design and operation.

[1467] A "generative AI model" is an artificial intelligence model that generates new content based on given input data (prompt text).

[1468] A "prompt" is text that is input to a generative AI model to cause it to generate a particular output.

[1469] "Content" means any information or data made available to Users and may be in the form of text, images, video or other formats.

[1470] "Modernization" means restructuring existing source code to conform to new technical standards and programming languages.

[1471] "Automation" means that systems or processes are carried out automatically by machines or software with minimal human intervention.

[1472] "Personalization" means customizing content and services according to a user's individual needs and preferences.

[1473] "Display" refers to visually presenting the generated content to a user.

[1474] A system for implementing the present invention is configured as follows: A server includes reverse engineering means for analyzing source code of a legacy system, means for updating the analyzed source code, means including a generative AI model for automatically performing the above operations, means for generating content based on a prompt sentence input by a user, and means for displaying the generated content.

[1475] Program processing explanation

[1476] The server first analyzes the source code of the legacy system using reverse engineering techniques. This analysis allows the system's functions and structure to be understood. It then uses techniques to modernize the analyzed source code, reconstructing it in a way that complies with new technical standards and programming languages. This series of operations is automated using a generative AI model.

[1477] When a user inputs a prompt sentence through a terminal, the server uses a generative AI model to generate content based on the prompt sentence, and the generated content is provided to the user through the display means of the terminal.

[1478] Hardware and Software Used

[1479] Hardware: Servers, user devices (smartphones, tablets, PCs, etc.)

[1480] Software: Python, generative AI models (e.g. GPT-3), reverse engineering tools, source code renovation tools

[1481] Examples

[1482] For example, if a user enters the prompt "Generate a blog post about recent technology trends," the server will use a generative AI model to generate content like this:

[1483] Example prompt:

[1484] Generate blog posts about recent technology trends.

[1485] Examples of generated content:

[1486] Recent technology trends include artificial intelligence (AI), blockchain, 5G communications, and quantum computing. These technologies have the potential to dramatically change our daily lives and the way we do business. For example, AI plays a key role in medical diagnosis and the development of self-driving cars. Blockchain is attracting attention as a technology that will improve the transparency and security of financial transactions. 5G communications will provide faster and more reliable Internet connections, facilitating the widespread adoption of IoT devices. Quantum computing is expected to provide a new means of solving complex problems that cannot be solved by conventional computers.

[1487] In this way, personalized content that meets the user's needs can be quickly generated and provided.

[1488] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1489] Step 1:

[1490] The user enters a prompt through the terminal.

[1491] Input: A prompt entered by the user (e.g. "Generate a blog post about recent technology trends.")

[1492] Output: The prompt text is sent to the server.

[1493] Specific operation: The user inputs a prompt into the input field of the terminal and presses the send button. The terminal sends this prompt to the server.

[1494] Step 2:

[1495] The server receives the prompt text and passes it to the generative AI model.

[1496] Input: Prompt text sent from the terminal

[1497] Output: The prompt is fed into the generative AI model.

[1498] Specific operation: The server analyzes the prompt text received from the terminal and sends a request to the API of the generative AI model.

[1499] Step 3:

[1500] A generative AI model generates content based on the prompt.

[1501] Input: Prompt text passed from the server

[1502] Output: The generated content (e.g. blog post)

[1503] How it works: The generative AI model analyzes the prompt and generates text based on relevant information, which is then returned to the server.

[1504] Step 4:

[1505] The server receives the generated content and transmits it to the terminal.

[1506] Input: Generated content returned from a generative AI model

[1507] Output: Generated content is sent to a device.

[1508] Specific operation: The server converts the content received from the generative AI model into a data format for transmission to the terminal and sends it to the terminal.

[1509] Step 5:

[1510] The device displays the generated content.

[1511] Input: Generated content sent from the server

[1512] Output: The generated content is displayed to the user.

[1513] Specific operation: The device renders the content received from the server in the display area and provides it visually to the user.

[1514] In this way, a series of processes is realized in which content is generated using a generative AI model based on the prompt sentence entered by the user and provided to the user.

[1515] In addition, an emotion engine that estimates the emotion of the user may be further combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[1516] "Example 1"

[1517] In one embodiment of the present invention, the AI ​​means includes an emotion engine that recognizes the user's emotion. The emotion engine estimates the user's emotional state from the user's facial expression, tone of voice, choice of words, etc. For example, if the user feels irritated, the emotion engine communicates the information to the AI ​​means. The AI ​​means uses this information to adjust the priority of source code renewal. Specifically, if the user feels irritated, the AI ​​means prioritizes renewal of the source code of the part that the user is interested in. In addition, the emotion engine reports the progress of the source code renewal according to the user's emotional state. For example, if the user feels calm, the emotion engine communicates the information to the AI ​​means, and the AI ​​means provides the user with a detailed report of the progress. This provides appropriate feedback according to the user's emotional state, improving the user experience.

[1518] The process flow of each embodiment will be described below.

[1519] "Example 1"

[1520] Step 1: The emotion engine infers the user's emotional state from their facial expressions, tone of voice, choice of words, etc.

[1521] Step 2: The emotion engine communicates the estimated emotional state to the AI ​​means.

[1522] Step 3: The AI ​​means adjusts the priority of source code updating using the information received from the emotion engine. Specifically, if the user feels frustrated, the AI ​​means prioritizes updating the source code of the part that the user is interested in.

[1523] Step 4: The emotion engine reports the progress of the source code update according to the user's emotional state. For example, if the user feels calm, the emotion engine conveys that information to the AI ​​means, and the AI ​​means provides the user with a detailed report of the progress.

[1524] Example 1

[1525] Next, a description will be given of Example 1 of the embodiment 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".

[1526] Conventional methods for analyzing and updating source code of legacy systems required a lot of manual operations and were inefficient. In addition, even text generation systems using generative AI models lacked the means to properly process user input and effectively use the generated text. This made it difficult to respond to user requests quickly and accurately.

[1527] The identification process by the identification processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes a reverse engineering means for analyzing the source code of the legacy system, a means for updating the analyzed source code, a means for receiving a prompt sentence input by a user, a means for preprocessing the received prompt sentence, a means for inputting the preprocessed prompt sentence into a generative AI model and acquiring the generated text, a means for post-processing the generated text, and an AI means for automatically performing the above operations. This enables efficient analysis and updating of the legacy system and automation of text generation using the generative AI model.

[1528] A "legacy system" is an information system that has been in use for a long time but is still in operation today.

[1529] "Source code" refers to textual code that describes the operation of a program.

[1530] "Reverse engineering" is a technique for analyzing the structure and operation of existing systems or software and extracting their design information.

[1531] A "prompt" is text that is entered as an instruction or question into a generative AI model.

[1532] "Preprocessing" refers to the process of converting data into an appropriate format before inputting it into a generative AI model.

[1533] A "generative AI model" is a model that uses artificial intelligence to generate text and data.

[1534] "Post-processing" refers to the process of converting the data output from a generative AI model into a format that is easy for humans to understand.

[1535] "AI means" refers to means of automating specific operations or processes using artificial intelligence technology.

[1536] This invention combines a system for analyzing and updating the source code of a legacy system with a system for generating text based on a prompt sentence entered by a user using a generative AI model. A specific embodiment of this system is shown below.

[1537] The server uses reverse engineering techniques to analyze the source code of the legacy system. The reverse engineering techniques are techniques for analyzing the structure and operation of existing systems and software and extracting their design information. The analyzed source code is reconstructed by modernization techniques in a form that conforms to new technical standards and programming languages.

[1538] A user inputs a prompt into an input field on the device, for example, "Use AI to generate a poem about spring." The device sends this prompt to the server, which then preprocesses the received prompt and converts it into a format suitable for the generative AI model. Preprocessing includes tokenizing and normalizing the text.

[1539] The server inputs the preprocessed prompt sentence into a generative AI model. This generative AI model uses, for example, a general generative AI model. The generative AI model generates new text based on the input prompt sentence. The generated text is returned to the server.

[1540] The server post-processes the generated text and converts it into a human-readable form, which includes decoding and formatting the generated text, and sends the post-processed text to the terminal for display to the user.

[1541] For example, if a user inputs "Use AI to generate a poem about spring," the server inputs this prompt into the generative AI model and generates a poem about spring. The generated poem is displayed to the user.

[1542] This system realizes efficient analysis and renovation of legacy systems and automation of text generation using generative AI models. The hardware used is a high-performance GPU (e.g., a general high-performance GPU), and the software uses Python and TensorFlow.

[1543] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1544] Step 1:

[1545] The user enters a prompt sentence.

[1546] The user inputs a prompt for the generative AI model into the input field of the terminal. For example, the user inputs, "Please use AI to generate a poem about spring." The input prompt becomes the input data for the terminal.

[1547] Step 2:

[1548] The terminal sends a prompt to the server.

[1549] The terminal sends the prompt text entered by the user to the server using an HTTP request. The input is the prompt text entered by the user, and the output is the prompt text sent to the server.

[1550] Step 3:

[1551] The server receives the prompt and performs data preprocessing.

[1552] The server receives the prompt sentence sent from the terminal. It tokenizes the received prompt sentence and converts it into a format suitable for the generative AI model. Specifically, it splits the text into words and subwords and converts it into a numerical vector. The input is the prompt sentence received from the terminal, and the output is the preprocessed numerical vector.

[1553] Step 4:

[1554] The server inputs a prompt sentence into the generative AI model and obtains the generated text.

[1555] The server inputs the preprocessed prompt sentence into the generative AI model. The generative AI model generates new text based on the input prompt sentence. The generated text is returned to the server. The input is the preprocessed numeric vector, and the output is the generated text.

[1556] Step 5:

[1557] The server post-processes the generated text and sends it to the terminal.

[1558] The server post-processes the text returned by the generative AI model. Specifically, it decodes the generated text and converts it into a human-readable format. It then sends the generated text to the terminal. The input is the text returned by the generative AI model, and the output is the post-processed text.

[1559] Step 6:

[1560] The terminal displays the generated text to the user.

[1561] The terminal displays the generated text received from the server to the user, who can then review the generated poem or piece of writing. The input is the post-processed text received from the server, and the output is the text displayed to the user.

[1562] (Application example 1)

[1563] Next, a description will be given of Application Example 1 of Form 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".

[1564] The process of analyzing and updating the source code of traditional legacy systems is very time-consuming and labor-intensive if done manually. In addition, there is a lack of efficient means to generate personalized content based on prompts entered by users. This makes it difficult to respond quickly to user needs.

[1565] 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.

[1566] In this invention, the server includes a reverse engineering means for analyzing the source code of the legacy system, a means for updating the analyzed source code, an artificial intelligence means for automatically performing the above operations, a generative AI model means for generating content based on a prompt sentence input by a user, and a means for displaying the generated content to a user. This enables efficient analysis and updating of the legacy system, and further enables rapid generation and provision of personalized content based on the user's prompt sentence.

[1567] "Reverse engineering methods" are methods for analyzing the source code of a legacy system and understanding its functions and structure.

[1568] "Innovation measures" are measures to reconstruct the analyzed source code in a way that complies with new technical standards or programming languages.

[1569] "Artificial intelligence means" means means for automating reverse engineering and source code renovation operations.

[1570] A "generative AI model means" is a means for generating content based on a prompt sentence entered by a user.

[1571] The "display means" refers to a means for displaying the generated content to the user.

[1572] The following system configuration and processing procedure will be described as an embodiment of the present invention.

[1573] The server includes reverse engineering means for analyzing source code of the legacy system, means for updating the analyzed source code, artificial intelligence means for automatically performing the above operations, generative AI model means for generating content based on a prompt sentence input by a user, and means for displaying the generated content to a user.

[1574] Specifically, the server is implemented using a Python program and the OpenAI API. The reverse engineering means analyzes the source code of the legacy system to understand its functions and structure. The analysis results are reconstructed by the modernization means in a form that complies with new technical standards and programming languages. These operations are automated by the artificial intelligence means.

[1575] When a user inputs a prompt sentence using the smartphone, the prompt sentence is sent to the generative AI model means. The generative AI model means generates content based on the prompt sentence using a generative AI model (e.g., text-davinci-003) of OpenAI. The generated content is displayed to the user by the display means.

[1576] For example, if a user inputs "I want to write a blog about my travel memories. Please write in detail about my trip to Okinawa with my family last summer," the generative AI model means will generate a detailed blog post based on this prompt. The generated blog post will be displayed on the smartphone screen for the user to check.

[1577] The system enables efficient analysis and modernization of legacy systems, as well as the rapid generation and delivery of personalized content based on user prompts.

[1578] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1579] Step 1:

[1580] A user inputs a prompt sentence using a smartphone. The input prompt sentence is sent to a server. The input data is the prompt sentence in text format input by the user.

[1581] Step 2:

[1582] The server passes the received prompt sentence to the generative AI model means. The generative AI model means analyzes the prompt sentence and processes the data to generate appropriate content. Specifically, it uses OpenAI's generative AI model (e.g., text-davinci-003) to generate text based on the prompt sentence. The input data is the prompt sentence, and the output data is the generated content.

[1583] Step 3:

[1584] The content generated by the generative AI model means is temporarily stored in the server. The server passes the generated content to the display means. The input data is the generated content, and the output data is the content passed to the display means.

[1585] Step 4:

[1586] The display means displays the generated content on the user's smartphone. The user can check the generated content on the screen of the smartphone. The input data is the content passed to the display means, and the output data is the content displayed on the screen of the smartphone.

[1587] Step 5:

[1588] The user can check the displayed content and modify or save it as necessary. The input data is the content displayed on the smartphone screen, and the output data is the content modified or saved by the user's operation.

[1589] In this way, a series of processes is realized in which personalized content is generated using a generative AI model based on the prompt sentence entered by the user and provided to the user.

[1590] Example 2

[1591] Next, a second embodiment of the second embodiment will be described. 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".

[1592] The source code analysis and renovation process of conventional legacy systems is often done manually, which takes time and effort. In addition, even in text generation systems using generative AI models, the process from when the user enters a prompt sentence to when they receive the generated text is complicated, making it difficult to perform efficiently. To solve these issues, it is necessary to automate the source code analysis and renovation process and to streamline the text generation process using generative AI models.

[1593] 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.

[1594] In this invention, the server includes a reverse engineering means for analyzing the source code of the legacy system, a means for updating the analyzed source code, an artificial intelligence means for automatically performing the above operations, a means for a user to input a prompt sentence, a means for a terminal to send the prompt sentence to the server, a means for the server to call a generative AI model, a means for the generative AI model to generate text, a means for the server to send the generated text to the terminal, and a means for the terminal to display the generated text. This makes it possible to automate the source code analysis and updating process and to streamline the text generation process using the generative AI model.

[1595] "Legacy systems" refers to existing, older information systems or software that are typically not aligned with new technologies and standards.

[1596] "Source code" means textual code written in a programming language that describes the operation of a program.

[1597] "Reverse engineering" is a technique for analyzing the structure and function of an existing system or software to understand its design and operation.

[1598] "Artificial intelligence" refers to computer systems and algorithms that mimic human intelligence and can learn, reason, and solve problems.

[1599] A "prompt sentence" is an instruction entered by a user to request a generative AI model to generate specific text.

[1600] "Terminal" refers to a device such as a computer or smartphone operated by a user.

[1601] A "server" is a computer system that provides services to other computers and devices over a network.

[1602] A "generative AI model" is an artificial intelligence model that generates text based on an input prompt.

[1603] "Text generation" is the process by which a generative AI model creates new text based on a given prompt.

[1604] An "HTTP request" is a protocol that allows a client (terminal) to request a server to send data or obtain information.

[1605] The "JSON format" is a lightweight data exchange format for expressing data in text format.

[1606] An "API Request" is a request to other software or services to perform a function through an application program interface.

[1607] "HTML" is a markup language for constructing web pages.

[1608] "UI components" refers to the individual elements or parts that make up a user interface.

[1609] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[1610] The present invention is a system for automating the source code analysis and renewal process of a legacy system and for streamlining the text generation process using a generative AI model. A specific embodiment of the system will be described below.

[1611] Hardware and Software Used

[1612] 1. Server

[1613] The server includes reverse engineering and artificial intelligence means for analyzing and updating the source code of the legacy system, and is capable of handling API requests for invoking generative AI models and generating text.

[1614] 2. Terminal

[1615] A terminal is a device where a user inputs a prompt text and sends it to a server. Specifically, this applies to PCs and smartphones.

[1616] 3. Generative AI Models

[1617] A generative AI model is an artificial intelligence model that generates text based on a prompt sentence when called by a server, such as OpenAI's GPT-3.

[1618] Data processing and calculation

[1619] 1. Enter and send a prompt

[1620] A user inputs a prompt through the terminal interface, for example, "Write a short story about a cat." The terminal sends this prompt as an HTTP request to the server.

[1621] 2. Parsing the prompt and calling the generative AI model

[1622] The server analyzes the received prompt sentence and calls the generative AI model. Specifically, it generates an API request including the prompt sentence and sends it to the endpoint of the generative AI model.

[1623] 3. Generating text and sending a response

[1624] The generative AI model generates text based on the input prompt. For example, in response to the prompt "Write a short story about a cat," it generates the text "Once upon a time, there lived a cat in a small village..." The generated text is returned to the server as an API response.

[1625] 4. Viewing the generated text

[1626] The server sends the text received from the generative AI model to the user's device, which displays the received text on the screen so that the user can check it.

[1627] Examples

[1628] Examples of prompt statements

[1629] "Write a short story about a cat."

[1630] "Write a poem about the city of the future."

[1631] "Please explain how AI will change human life."

[1632] In this way, users can use generative AI models to generate various texts. This system automates the source code analysis and renovation process of legacy systems, and streamlines the text generation process using generative AI models.

[1633] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1634] Step 1:

[1635] The user enters a prompt sentence.

[1636] The user inputs a prompt through the terminal interface, for example, "Write a short story about a cat" in the text input field and clicks the send button. The input prompt is temporarily stored in the terminal's memory.

[1637] Step 2:

[1638] The terminal sends a prompt to the server.

[1639] The terminal sends the prompt text entered by the user to the server as an HTTP request. Specifically, the prompt text is packaged as JSON format data and sent to the server via the Internet. The input is the prompt text, and the output is an HTTP request to the server.

[1640] Step 3:

[1641] The server calls the generative AI model.

[1642] The server analyzes the received prompt sentence and calls the generative AI model. Specifically, it generates an API request containing the prompt sentence and sends it to the endpoint of the generative AI model. The input is an HTTP request containing the prompt sentence, and the output is an API request to the generative AI model.

[1643] Step 4:

[1644] A generative AI model generates the text.

[1645] A generative AI model generates text based on a prompt received from the server. For example, in response to the prompt "Write a short story about a cat," the model generates the text "Once upon a time, in a small village, there lived a cat..." The input is the prompt, and the output is the generated text.

[1646] Step 5:

[1647] The server sends the generated text to the terminal.

[1648] The server sends the text received from the generative AI model to the user's terminal. Specifically, it generates an HTTP response containing the generated text and sends it back to the terminal. The input is the generated text, and the output is the HTTP response to the terminal.

[1649] Step 6:

[1650] The terminal displays the generated text.

[1651] The device displays the generated text received from the server on the screen. Specifically, it inserts the received text into HTML and app UI components and displays it in a form that is visible to the user. The input is the generated text, and the output is the text that is displayed to the user.

[1652] (Application example 2)

[1653] Next, a description will be given of Application Example 2 of Form 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".

[1654] Conventional methods for analyzing and updating source code of legacy systems required a lot of manual operations and were inefficient. In addition, there was no technology to generate content using generative AI models based on prompts entered by users, making it difficult to generate personalized content. This meant that content could not be provided according to user needs, and the user experience did not improve.

[1655] 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.

[1656] In this invention, the server includes a reverse engineering means for analyzing the source code of the legacy system, a means for updating the analyzed source code, an artificial intelligence means for automatically performing the above operations, a means for generating content using a generative AI model based on a prompt sentence input by a user, and a means for displaying the generated content. This enables efficient analysis and updating of the legacy system, and further enables personalized content generation according to the needs of the user.

[1657] "Reverse engineering methods" are methods for analyzing the source code of an existing system or software to understand its functions and structure.

[1658] "Moderation measures" refer to measures that, based on the analyzed source code, reconstruct the source code in a manner that conforms to new technical standards or programming languages ​​while maintaining existing functions.

[1659] "Artificial intelligence means" means artificial intelligence technology used to automatically perform a specific operation or process.

[1660] A "prompt sentence" is the text that a user inputs into a generative AI model, and is the sentence that forms the basis for the generated content.

[1661] A "generative AI model" is an artificial intelligence model that generates content such as text and images based on input prompts.

[1662] A "content generation means" is a means for generating content using a generative AI model based on a prompt sentence entered by a user.

[1663] The "display means" refers to a means for visually presenting the generated content to the user.

[1664] A system for implementing the present invention is configured as follows: A server includes reverse engineering means for analyzing source code of a legacy system, means for updating the analyzed source code, artificial intelligence means for automatically performing the above operations, means for generating content using a generative AI model based on a prompt sentence input by a user, and means for displaying the generated content.

[1665] First, the server analyzes the source code of the legacy system using reverse engineering techniques. These techniques analyze the source code of the existing system or software to understand its functions and structure. Next, the analyzed source code is reconstructed using modernization techniques. These techniques reconstruct the source code in a way that conforms to new technical standards and programming languages ​​while maintaining the existing functions.

[1666] Moreover, artificial intelligence means will perform these operations automatically, eliminating the need for manual operations and enabling efficient analysis and renewal.

[1667] Also included is a means for generating content using a generative AI model based on a prompt sentence input by a user. A prompt sentence is a text input by a user to a generative AI model, and is the sentence that serves as the basis for the generated content. A generative AI model is an artificial intelligence model that generates content such as text and images based on the input prompt sentence.

[1668] The generated content is visually presented to the user by a display means, which is for visually presenting the generated content to the user.

[1669] As a concrete example, if a user wants to generate a story on the theme of "space exploration," they might enter the following prompt:

[1670] "Generate a space exploration story about a young scientist and his adventures exploring an unknown planet."

[1671] Given this prompt, a generative AI model might generate a story like this:

[1672] "A young scientist steps onto an unknown planet. His mission is to explore the planet's ecosystem and discover new life forms. The planet's sky glows purple, and strange plants grow on the surface. He carefully steps forward and eventually discovers a huge crystal cave. Inside the cave, there live glowing creatures, who welcome him. Through his adventure on this planet, he discovers the mysteries of the universe and new friends."

[1673] In this way, personalized content can be generated according to the user's needs.

[1674] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1675] Step 1:

[1676] The user enters a prompt sentence at the terminal.

[1677] Input: A prompt entered by the user (e.g., "Generate a story about space exploration. The protagonist is a young scientist and his adventures exploring an unknown planet.")

[1678] Output: The prompt text is sent to the server.

[1679] Step 2:

[1680] The server receives the prompt text and passes it to the generative AI model.

[1681] Input: The prompt text sent by the user

[1682] Output: The prompt passed to the generative AI model

[1683] Step 3:

[1684] A generative AI model generates content based on the prompt.

[1685] Input: The prompt given to the generative AI model

[1686] Output: Generated content (e.g. a story)

[1687] Step 4:

[1688] The server receives the generated content and passes it to the display means.

[1689] Input: Content output from a generative AI model

[1690] Output: The content that is passed to the display

[1691] Step 5:

[1692] The terminal uses a display means to visually present the generated content to the user.

[1693] Input: Content sent from the server

[1694] Output: The content that is displayed to the user

[1695] 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 a voice indicating a user input for 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.

[1696] 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> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to 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.

[1697] Another example of generative AI is Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1698] In the above embodiment, an example was given in which the specific process was performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the robot 414.

[1699] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[1700] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1701] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[1702] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1703] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. For robots, cars, motorcycles, etc., emotions can also be created based on various balances such as posture and battery level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated based on, for example, the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). In the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. In addition, in the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[1704] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[1705] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[1706] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[1707] 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 Universal Serial Bus (USB) 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.

[1708] In addition, 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 upon request from the data processing device 12.

[1709] 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.

[1710] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing the specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[1711] The hardware resource that executes the specific process may be one of these various processors, or may be 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 process may be a single processor.

[1712] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[1713] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[1714] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 description and illustrations, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above description and illustrations omit explanations of technical common sense that do not require explanation in order to enable the implementation of the technology of the present disclosure.

[1715] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or standard was specifically and individually indicated to be incorporated by reference.

[1716] The following is further disclosed regarding the above embodiment.

[1717] (Claim 1)

[1718] A system that includes reverse engineering means for analyzing the source code of a legacy system, means for updating the analyzed source code, and AI means for automating the above operations.

[1719] (Claim 2)

[1720] 2. The system of claim 1, wherein the reverse engineering means includes means for understanding a function and structure of a legacy system.

[1721] (Claim 3)

[1722] 2. The system of claim 1, wherein the source code modernization means comprises means for reconstructing the source code in a manner that conforms to new technical standards or programming languages ​​while maintaining functionality of the legacy system.

[1723] (Claim 4)

[1724] 2. The system of claim 1, wherein the AI ​​means includes an emotion engine that recognizes emotions of a user.

[1725] (Claim 5)

[1726] 5. The system of claim 4, wherein the emotional engine includes means for adjusting a priority of source code innovation depending on an emotional state of a user.

[1727] (Claim 6)

[1728] 5. The system of claim 4, wherein the emotional engine includes means for reporting a progress of source code renovation according to an emotional state of a user.

[1729] "Example 1"

[1730] (Claim 1) means for receiving a prompt text input by a user; A means of passing the received prompt sentence to a generative AI model; A means for the generative AI model to generate text based on the prompt; and a means for returning the generated text to the user; A system including:

[1731] (Claim 2) 2. The system of claim 1, wherein the generative AI model comprises means for generating natural language text based on a prompt sentence.

[1732] (Claim 3) 2. The system of claim 1, wherein the means for receiving the prompt sentence comprises means for receiving the prompt sentence as an HTTP request from the user's terminal.

[1733] "Application example 1"

[1734] (Claim 1) A reverse engineering method for analyzing the source code of a legacy system; A means of updating the analyzed source code; An artificial intelligence means for automatically performing the above operations; A generative AI model means for generating content based on a prompt input by a user; a means for displaying the generated content; A means to store and share the generated content; A system including:

[1735] (Claim 2) 2. The system of claim 1, wherein the reverse engineering means includes means for understanding a function and structure of a legacy system.

[1736] (Claim 3) 2. The system of claim 1, wherein the source code modernization means comprises means for reconstructing the source code in a manner that conforms to new technical standards or programming languages ​​while maintaining functionality of the legacy system.

[1737] "Example 2"

[1738] (Claim 1) means for receiving instructions input by a user; A generating AI means for generating a program based on the received instruction text; means for executing the generated program; a means for returning the results of the execution to the user; and A system including:

[1739] (Claim 2) 2. The system of claim 1, wherein the generating AI means includes means for generating a program that retrieves data from a database based on an instruction sentence, processes the data, and visualizes it.

[1740] (Claim 3) 2. The system according to claim 1, wherein the execution means includes means for executing the generated program, acquiring data from a database, processing the data, and visualizing the data.

[1741] "Application example 2"

[1742] (Claim 1) A reverse engineering method for analyzing the source code of a legacy system; A means of updating the analyzed source code; A means including a generative AI model for automatically performing the above operations; a means for generating content based on a prompt input by a user; a means for displaying the generated content; A system including:

[1743] (Claim 2) 2. The system of claim 1, wherein the reverse engineering means includes means for understanding a function and structure of a legacy system.

[1744] (Claim 3) 2. The system of claim 1, wherein the source code modernization means comprises means for reconstructing the source code in a manner that conforms to new technical standards or programming languages ​​while maintaining functionality of the legacy system.

[1745] "Example 1 of combining emotion engines"

[1746] (Claim 1) A reverse engineering method for analyzing the source code of a legacy system; A means of updating the analyzed source code; means for receiving a prompt text input by a user; means for preprocessing received prompt text; a means for inputting the preprocessed prompt sentence into a generative AI model and obtaining a generated text; means for post-processing the generated text; AI means to automatically perform the above operations, A system including:

[1747] (Claim 2) 2. The system of claim 1, wherein the reverse engineering means includes means for understanding a function and structure of a legacy system.

[1748] (Claim 3) 2. The system of claim 1, wherein the source code modernization means comprises means for reconstructing the source code in a manner that conforms to new technical standards or programming languages ​​while maintaining functionality of the legacy system.

[1749] "Application example 1 when combining emotion engines"

[1750] (Claim 1) A reverse engineering method for analyzing the source code of a legacy system; A means of updating the analyzed source code; An artificial intelligence means for automatically performing the above operations; A generative AI model means for generating content based on a prompt input by a user; a means for displaying the generated content to a user; A system including:

[1751] (Claim 2) 2. The system of claim 1, wherein the reverse engineering means includes means for understanding a function and structure of a legacy system.

[1752] (Claim 3) 2. The system of claim 1, wherein the source code modernization means comprises means for reconstructing the source code in a manner that conforms to new technical standards or programming languages ​​while maintaining functionality of the legacy system.

[1753] "Example 2 of combining emotion engines"

[1754] (Claim 1) A reverse engineering method for analyzing the source code of a legacy system; A means of updating the analyzed source code; An artificial intelligence means for automatically performing the above operations; a means for a user to input a prompt sentence; A means for the terminal to send a prompt sentence to the server; A means for the server to call the generative AI model; A means by which the generative AI model generates text; and means for the server to transmit the generated text to the terminal; a means for the terminal to display the generated text; A system including:

[1755] (Claim 2) 2. The system of claim 1, wherein the reverse engineering means includes means for understanding a function and structure of a legacy system.

[1756] (Claim 3) 2. The system of claim 1, wherein the source code modernization means comprises means for reconstructing the source code in a manner that conforms to new technical standards or programming languages ​​while maintaining functionality of the legacy system.

[1757] "Application example 2 when combining emotion engines"

[1758] (Claim 1) A reverse engineering method for analyzing the source code of a legacy system; A means of updating the analyzed source code; An artificial intelligence means for automatically performing the above operations; A means for generating content using a generative AI model based on a prompt input by a user; a means for displaying the generated content; A system including:

[1759] (Claim 2) 2. The system of claim 1, wherein the reverse engineering means includes means for understanding a function and structure of a legacy system.

[1760] (Claim 3) 2. The system of claim 1, wherein the source code modernization means comprises means for reconstructing the source code in a manner that conforms to new technical standards or programming languages ​​while maintaining functionality of the legacy system. [Explanation of symbols]

[1761] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a first means for receiving a first prompt sentence relating to the generation of text input by a user; a second means for inputting the first prompt sentence into a generative AI model to generate the text; a third means for transmitting the generated text to a terminal of the user; A system including:

2. the first means receives a second prompt sentence related to the generation of the program input by the user; the second means inputs the second prompt sentence into a generative AI model to generate the program; The third means transmits the generated program to a terminal of the user. The system of claim 1 .

3. the first means receives a third prompt sentence related to the generation of the content input by the user; The second means inputs the third prompt sentence into a generative AI model to generate the content; The third means transmits the generated content to a terminal of the user.

3. A system according to claim 1 or 2.

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