system

Abstract

Provide a system. A system for receiving a system modification request from a user; means for analyzing the system modification request and identifying the modification content; means for generating a modification plan based on the modification content; means for executing system modifications based on the generated modification plan; A means of automatically verifying the modified system; means for generating a new system specification based on the modification content; means for providing said new system specification to a user; A system including:

Description

[Technical Field]

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

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

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

[0004] Previously, system modifications and specification explanations relied primarily on system experts and vendors with specialized knowledge, resulting in the problem of increasing dependency on individual personnel. This made it difficult to respond without a specific person in charge, resulting in issues such as a decline in overall business efficiency and delays in the promotion of DX. In addition, checking system operation after modifications and explaining new specifications took time and effort, leading to the occurrence of errors and insufficient explanations. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems with a system that includes a means for accepting system modification requests from users, a means for analyzing the system modification requests and identifying the modification content, a means for generating a modification plan based on the modification content, a means for executing the system modification based on the generated modification plan, a means for automatically verifying the modified system, a means for generating new system specifications based on the modification content, and a means for providing the new system specifications to users. This allows even non-experts to perform system modifications, preventing dependency on individual skills. Furthermore, the automation of system operation checks and specification explanations improves business efficiency and promotes the promotion of digital transformation.

[0006] "User" means an individual or group that inputs a request for modification to the system.

[0007] "Means for accepting system modification requests" refers to the interface or function that inputs modification instructions and requests from users.

[0008] "Means for analyzing system modification requests and identifying the modification content" refers to the function of analyzing the received request content and identifying the specific changes and targets.

[0009] "Means for generating a modification plan based on the modification content" refers to a function that plans the actual steps for modifying the system based on the analyzed modification content.

[0010] The "means for executing system modifications based on the generated modification plan" refers to a function for automatically performing actual modification work on the system based on the generated modification plan.

[0011] "Means for automatically verifying the system after modification" refers to the function of automatically testing whether the system operates correctly after modification is completed.

[0012] "Means for generating new system specifications" refers to a function that automatically creates new system specifications based on the modification content.

[0013] "Means for providing new system specifications to users" refers to the function of providing the generated new specifications to users and explaining the modifications.

[0014] "Natural language processing" refers to technology for analyzing user requests and understanding their intent and content.

[0015] "Database schema modification" refers to the act of changing the system's database structure as needed.

[0016] "Back-end logic modification" refers to the act of modifying the server-side operation of a system.

[0017] "User interface modification" refers to the act of modifying the display and operation of the front-end part of a system. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

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

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

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

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

[0023] 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.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

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

[0028] 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.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] 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.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0035] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0036] 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.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] To implement the present invention, a system called White Boxer is first introduced. Next, the operation of this system will be described in detail.

[0040] System program and processing explanation

[0041] User request input

[0042] The server provides a dedicated interface for users to input system modification requests. For example, a user may input a request such as "I want to add a 'customer rank' field to the customer management system."

[0043] Parsing the request

[0044] The server analyzes the received request. This analysis uses a natural language processing engine to understand the content of the input request and identify the necessary modifications. Specifically, the server recognizes the addition of a new field called "Customer Rank."

[0045] Identifying the scope of impact and creating a plan

[0046] The server identifies how adding the new field will impact the entire system, including the need for database schema changes, back-end logic modifications, and user interface updates. The server then generates a specific remediation plan for implementing these changes.

[0047] Implementing the renovation plan

[0048] The server automatically executes system modifications based on the generated modification plan. The modifications are listed below, including specific examples.

[0049] Database schema modification: The server executes the SQL command to add a new field, customer_rank.

[0050] Backend logic modifications: The server updates its API endpoints and business logic to accommodate the new fields.

[0051] User interface modifications: The server adds new input fields to the user interface, for example, a text box or dropdown for entering "Customer Rank" in a customer information input form.

[0052] System verification after modification

[0053] The server runs automated tests to verify that the modified system operates correctly.

[0054] Unit testing: The server runs tests for newly added features and changes.

[0055] Integration testing: The server runs tests to ensure the whole system works together correctly.

[0056] Generate and provide specifications

[0057] The server automatically generates a new system specification based on the modifications. This specification includes details of each modified item. For example, it may include a specific explanation such as, "A new field, 'Customer Rank', has been added to the customer management system."

[0058] The terminal displays the specifications to the user, explaining how to use the newly added field and its impact on the entire system. The terminal notifies the user, "The customer rank field has been added. This allows you to manage customer rank information."

[0059] In this way, the system of the present invention can automate the entire process from requesting system modifications to implementing, verifying, and providing specifications, thereby preventing dependency on individual personnel and significantly improving work efficiency.

[0060] The processing flow will be explained below.

[0061] Step 1:

[0062] The user inputs a system modification request through the White Boxer interface. For example, the user inputs an instruction such as "I want to add a 'Customer Rank' field to the customer management system."

[0063] Step 2:

[0064] The server analyzes the received system modification request. During this analysis, it uses a natural language processing engine to understand the request and identify the necessary modifications. Specifically, it recognizes the addition of a new field, "Customer Rank."

[0065] Step 3:

[0066] Based on the analysis results, the server identifies the extent of the impact on the entire system, and identifies changes to the database schema, back-end logic, and user interface.

[0067] Step 4:

[0068] The server generates a specific repair plan based on the extent of the impact, for example, a plan to add a new column, customer_rank, to the database and update the corresponding API endpoints and UI components.

[0069] Step 5:

[0070] The server executes the modification of the system based on the generated modification plan.

[0071] Database modification: The server executes an SQL command to add a new column, customer_rank.

[0072] Backend logic modifications: The server updates its API endpoints and business logic to accommodate the new fields.

[0073] User interface modifications: The server adds "customer rank" to UI components, for example adding a new text box or dropdown to an input form.

[0074] Step 6:

[0075] The server runs automated tests to ensure the modified system works correctly.

[0076] Unit testing: The server runs tests to ensure that each modified part functions correctly.

[0077] Integration testing: The server runs tests to ensure the entire system works together correctly.

[0078] Step 7:

[0079] The server automatically generates a new system specification based on the modifications, which includes details of each modified item.

[0080] Step 8:

[0081] The terminal provides the generated new specification to the user, notifies the user that "The customer rank field has been added. This allows you to manage customer rank information," and displays the details of the specification.

[0082] Example 1

[0083] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0084] The traditional system modification process requires manual requirement analysis and modification plan creation, which requires a significant amount of time and effort. It is also difficult to identify the scope of impact of the modifications, making it prone to human error. Furthermore, verifying modifications and updating specifications after modifications are also time-consuming tasks, resulting in a decline in overall business efficiency.

[0085] 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.

[0086] In this invention, the server includes means for accepting a system modification request from a user, means for analyzing the system modification request using natural language processing to identify the modification content, means for identifying the scope of impact based on the modification content and generating a modification plan, means for executing the system modification based on the generated modification plan, means for automatically verifying the system after modification (including unit testing and integration testing), means for generating a new system specification based on the modification content, and means for providing the new system specification to the user. This eliminates the need for manual analysis and modification plan generation and automates the entire process, enabling efficient and error-free system modifications.

[0087] "Means for accepting system modification requests from users" refers to the interface through which users input requests for changes or additions to the system and the process by which the server receives them.

[0088] "Means for analyzing system modification requests using natural language processing to identify the modification content" refers to the process of analyzing the request content received from the user using natural language processing technology and automatically extracting the specific modification content.

[0089] "Means for identifying the scope of impact and generating a repair plan" refers to the process of assessing the impact on the entire system based on the identified repair content and formulating a detailed plan for the repair work based on that.

[0090] "Means for executing system modifications" refers to the execution process for modifying the database structure, back-end logic, and user interface according to the generated modification plan.

[0091] "Means for automatically verifying the system" refers to the process of automatically running unit tests and integration tests after a system has been modified to verify that the modified parts and the entire system are operating correctly.

[0092] The "means for generating new system specifications" refers to a process for automatically creating specifications that comprehensively describe the changes and new features of the modified system.

[0093] "Means for providing new system specifications to users" refers to the process of displaying or sharing the generated specifications with users.

[0094] "Database structural modifications" refers to making changes to the system's database schema, including adding new fields and modifying existing fields.

[0095] "Back-end logic modifications" refers to modifying a system's business logic and API endpoints, which updates the way data is processed and the server-side functionality.

[0096] "User interface modifications" refers to changes or improvements to the graphical interface through which a user interacts with a system, including adding new input fields or modifying existing UI components.

[0097] "Unit testing" refers to automated testing that verifies that individual program modules or functions work correctly.

[0098] "Integration testing" refers to automated testing that verifies that different program modules work together correctly as a whole system and behave as expected.

[0099] "Generative artificial intelligence model" refers to a machine learning algorithm used to analyze natural language input from a user and generate appropriate responses or modifications.

[0100] A "prompt sentence" refers to a natural language command or request sentence input to a generative artificial intelligence model.

[0101] This invention relates to a system that automates the system modification process. Specifically, it is a system that automates the process of analyzing modification requests from users, identifying the scope of impact, creating and executing a modification plan, and finally verifying the system and creating and providing specifications.

[0102] System Overview

[0103] First, the system provides a dedicated interface for accepting modification requests from users. This interface is implemented as a web form or a desktop application user interface. Users use this interface to input modification requests.

[0104] For example, consider the case where a user inputs a request such as "I want to add a 'customer rank' field to the customer management system." This request is sent to the server and received by the server.

[0105] Parsing the request

[0106] The server analyzes the received modification request using a natural language processing engine (e.g., Google's BERT or OpenAI's GPT-3). It receives the request as text, analyzes it using a generative artificial intelligence model, and identifies the specific modification content.

[0107] Identifying the impact area and generating a repair plan

[0108] The server then evaluates the impact of the identified modifications on the entire system, which may include changing the database structure, modifying back-end logic, and updating the user interface.The server then generates a specific modification plan based on this information.

[0109] For example, the rework plan for adding a new field, customer_rank, would look like this:

[0110] Modify the database schema: Generate the SQL command to add a new field, customer_rank, to the database (e.g., ALTER TABLE customer ADD COLUMN customer_rank VARCHAR(255);).

[0111] Modifying backend logic: If the system uses Spring Boot, add new API endpoints or modify existing endpoints.

[0112] Update the user interface: If it's a web application using React, add a new input field.

[0113] Implementing the renovation plan

[0114] The server executes the system modification based on the generated modification plan. This process includes the following steps:

[0115] Executes SQL commands to make database schema changes.

[0116] Execute code to modify API endpoints and business logic.

[0117] Executes code to add a new field to the user interface.

[0118] System verification after modification

[0119] After the modifications are applied, the server automatically validates the system. First, it runs unit tests on the newly added features and modified parts. This uses a test framework such as JUnit to check the operation of individual program modules. Next, it runs integration tests to check that the entire system works correctly. This is automated testing at the UI level using a test tool such as Selenium.

[0120] Generate and provide specifications

[0121] Finally, the server automatically generates a new system specification based on the modifications. This specification includes details of each modified item and how to use it. The generated specification is sent to the terminal, which displays it to the user.

[0122] For example, the description for the new field customer_rank might read, "A new field, 'Customer Rank', has been added to the customer management system. This allows you to manage customer rank information."

[0123] Prompt Sentence Examples

[0124] An example of a specific prompt for a generative artificial intelligence model might be, "Generate code to add a 'customer rank' field to a customer management system. This should include database schema changes, backend logic modifications, and user interface updates."

[0125] The system of the present invention automates the system modification process, which was previously performed manually, enabling efficient and error-free modifications, which significantly improves business efficiency and reduces the risks associated with system modifications.

[0126] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0127] Step 1:

[0128] Entering a Request

[0129] User: Enters a modification request using a dedicated interface on the terminal. In this example, the user enters a request such as "I want to add a 'Customer Rank' field to the customer management system."

[0130] Input: Text data of the modification request.

[0131] Output: Sends a modification request to the server.

[0132] Specifically, the user fills out a modification request in a web form and clicks the "Submit" button.

[0133] Step 2:

[0134] Receiving a request

[0135] Server: Receives modification requests sent by users.

[0136] Input: Text data of the modification request from the user.

[0137] Output: The internal data structure of the modification request (e.g., in JSON format).

[0138] As an initial process, an HTTP request is received and the entered modification request is temporarily stored in a database.

[0139] Step 3:

[0140] Parsing the request

[0141] Server: Analyzes the received modification request using a natural language processing engine. Using a generative AI model (e.g., BERT or GPT-3), it identifies the intent of the request and the modification content.

[0142] Input: The internal data structure of the modification request (in JSON format).

[0143] Output: Identification of the modification (e.g. adding a new field customer_rank).

[0144] Specifically, a prompt sentence is input to the generative artificial intelligence model and the analysis results are obtained.

[0145] Step 4:

[0146] Identifying the impact area and generating a repair plan

[0147] Server: Identify the impact on the entire system based on the modifications. Automatically assess whether database schema changes, backend logic modifications, and user interface updates are required.

[0148] Input: Identification results of the repairs.

[0149] Output: A detailed improvement plan (SQL commands, API fixes, UI updates).

[0150] In this step, the database model and source code repository are analyzed to automatically extract areas that require modification.

[0151] Step 5:

[0152] Implementing the renovation plan

[0153] Server: Executes system modifications based on the generated modification plan.

[0154] Modify the database schema: Run an SQL command (e.g., ALTER TABLE customer ADD COLUMN customer_rank VARCHAR(255);) to add a new field.

[0155] Modify backend logic: Update API endpoints and business logic using Spring Boot or similar.

[0156] Update the user interface: Add new input fields to the UI using React or another front-end framework.

[0157] Input: Specific renovation plans.

[0158] Output: Modified system.

[0159] Specific actions include modifying endpoint code in a Spring Boot application or adding new React components.

[0160] Step 6:

[0161] System Verification

[0162] Server: Run automated tests to ensure the modified system works correctly.

[0163] Unit testing: Using JUnit or similar software to check the operation of individual program modules.

[0164] Integration testing: Use Selenium or similar tools to check the operation of the entire system.

[0165] Input: Modified system.

[0166] Output: Test results.

[0167] Specifically, the test script is executed and success / failure data is logged.

[0168] Step 7:

[0169] Generate and provide specifications

[0170] Server: Automatically generate a new system specification based on the modifications. This specification includes details of each modified item and how to use it.

[0171] Input: Modification details and test results.

[0172] Output: An automatically generated specification.

[0173] Terminal: Displays the generated specification to the user.

[0174] As a specific action, the following message is written about the new field customer_rank: "A new field 'Customer Rank' has been added to the customer management system. This allows you to manage customer rank information," and the message is notified to the user.

[0175] (Application example 1)

[0176] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0177] System modification work at logistics facilities is complex and requires manual work by engineers with specialized knowledge, which is time-consuming and costly. There is a demand for automation of this work, but currently there is a lack of technology to process this request quickly and accurately. A system is needed to solve this problem and improve the operational efficiency of logistics facilities.

[0178] 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.

[0179] In this invention, the server includes means for accepting a system modification request from a user, means for analyzing the system modification request to identify the modification content, means for generating a modification plan based on the modification content, means for executing the system modification based on the generated modification plan, means for automatically verifying the system after modification, means for generating a new system specification based on the modification content, means for providing the new system specification to the user, and means that is installed on a robot in the logistics facility and processes modification requests for the logistics system. This makes it possible to quickly and accurately automate system modification work in the logistics facility.

[0180] "User" refers to the person who submits a system modification request, and includes staff and managers at the logistics facility.

[0181] A "system modification request" refers to a request to add new functions or make changes to the system.

[0182] "Analysis" refers to the process of understanding the content of the system modification request and identifying the necessary modifications.

[0183] "Modification contents" refers to the specific items of system changes identified based on the system modification request.

[0184] "Renovation Plan" means a detailed plan for carrying out the identified renovations.

[0185] "Generation" refers to the process of creating and building a renovation plan.

[0186] "Execution" refers to carrying out specific system modification work based on the generated modification plan.

[0187] "Verification" refers to the process of verifying whether a modified system operates correctly.

[0188] "System Specification" means a document that contains detailed specifications for the modified system.

[0189] "Logistics facility" refers to a facility that stores and distributes goods and materials.

[0190] "Robot" refers to a mechanical device that performs automated tasks in logistics facilities.

[0191] "Natural language processing" refers to the technology for analyzing natural language and understanding its meaning.

[0192] "Database schema modification" refers to the process of changing the structure of a database.

[0193] "Back-end logic modification" refers to the process of changing the logic and algorithms that operate behind the system.

[0194] "User interface modification" refers to the work of changing the user interface of a system.

[0195] "AI model" refers to the algorithms and structure of artificial intelligence.

[0196] "Prompt sentence" refers to the input sentence used by an AI model to understand the request and generate an appropriate response.

[0197] To implement this invention, a server must first install the "AutoLogiMod" application on the robots in the logistics facility. This application automatically accepts and analyzes system modification requests, creates and executes modification plans, verifies the modified system, and creates and provides specifications.

[0198] The system's program is configured as follows: The server uses a natural language processing engine (NLP engine) to analyze the system modification request entered by the user and identify its contents. Based on the identified modification contents, the server generates a modification plan. This modification plan includes modifications to the database schema, corrections to back-end logic, and updates to the user interface. The server automatically executes the generated modification plan and modifies the system. The modified system is verified through unit testing and integration testing to confirm correct operation. Finally, the server automatically generates a new system specification based on the modification contents and provides it to the user.

[0199] The hardware used mainly consists of robots (with embedded computers) installed in logistics facilities and associated servers, while the software used includes Python programs, natural language processing engines (libraries like NLTK or SpaCy), SQL parsing engines (sqlparse), and AI-based plan generation modules.

[0200] For example, if a user inputs a system modification request such as "I want to add an expired inventory management field to the inventory management system," the server analyzes the request and generates SQL commands to add the necessary database field. It also makes corresponding changes to the back-end logic and user interface. After the modification is complete, the server runs automated tests to verify that the system is working correctly. This series of automated processes is expected to significantly improve the efficiency of logistics facilities.

[0201] Example prompt sentence:

[0202] "Please add an expired inventory management field to your inventory management system."

[0203] This invention enables the rapid and accurate automation of system modification work at logistics facilities, enabling effective management.

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

[0205] Step 1: User enters system modification request

[0206] The user inputs a request such as "I want to add an expired inventory management field to the inventory management system" from the terminal. This input is sent to the server. The input data is in the form of text in natural language and is input via the terminal's input interface.

[0207] Step 2: Analyze system modification requirements

[0208] The server uses a natural language processing engine (NLP engine) to analyze the input system modification request. As a result of the analysis, the modification content is identified. The input data is the user's request text, and the output data is a list or data structure of the analyzed modification content.

[0209] Step 3: Generate a renovation plan

[0210] The server generates a repair plan based on the identified repairs. The repair plan includes database schema changes, back-end logic modifications, and user interface updates. The input data is the analyzed repairs, and the output data is a list of specific repair steps and the commands required to perform them.

[0211] Step 4: Execute SQL commands

[0212] The server executes SQL commands against the database schema based on the modification plan. The input data are the SQL commands included in the modification plan, and the output data are the results of the database structural changes. Specifically, the server sends the SQL commands to the database to add new fields.

[0213] Step 5: Update your backend logic

[0214] The server updates the backend logic based on the modification plan. The input data is the changes to the backend logic included in the modification plan, and the output data is the updated source code or executable program. Specific actions include modifying API endpoints and business logic.

[0215] Step 6: Update the User Interface

[0216] The server updates the user interface based on the modification plan. The input data are the changes to the user interface included in the modification plan, and the output data is the updated user interface. Specific actions include adding new input fields and drop-down menus.

[0217] Step 7: Verify the system after modification

[0218] The server runs unit tests and integration tests to verify that the modified system operates correctly. The input data is the modified system, and the output data is the test results. Specifically, it runs automated test scripts to verify that each function operates as expected.

[0219] Step 8: Generate a system specification

[0220] The server generates a new system specification based on the modifications. The input data is the modification plan and information about the modified system, and the output data is the system specification. Specifically, it automatically generates a document that includes details of the modifications and an explanation of new functions.

[0221] Step 9: Provide system specifications

[0222] The server provides the generated system specifications to the user. The input data is the generated system specifications, and the output data is a display or download link for the user to refer to. Specific operations include displaying the specifications on the terminal or sending them by email.

[0223] Through the above steps, system modification work at logistics facilities can be automated quickly and accurately.

[0224] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0225] To implement the present invention, it is necessary to combine a system called White Boxer with an emotion engine that recognizes the user's emotions. The specific operation of a system including an emotion engine will be described below.

[0226] System program and processing explanation

[0227] User request input and emotion recognition

[0228] The server provides a dedicated interface for users to input requests for system modifications. At this time, the emotion engine analyzes the content and input method of the user's input to recognize emotions. For example, if a user emotionally inputs, "I would like to urgently add a 'Customer Rank' field to the customer management system," the "urgent" part is analyzed and the urgency is recognized.

[0229] Analyzing demands and emotions

[0230] The server analyzes the received system modification request and the recognition results of the emotion engine. The natural language processing engine understands the request content, and the emotion engine identifies the user's emotion (e.g., urgency, importance, dissatisfaction). Specifically, it adds a new field, "Customer Rank," and recognizes its high urgency.

[0231] Identifying the scope of impact and creating a plan

[0232] The server identifies the scope of impact on the entire system based on the modification request and sentiment, and prioritizes modification plans. It identifies changes to the database schema, backend logic, and user interface, and generates modification plans with priority given to those with the highest urgency. For example, it creates a plan to add a new column, customer_rank, to the database and quickly update the corresponding API endpoints and UI components.

[0233] Implementing the renovation plan

[0234] The server automatically executes the system modification based on the generated modification plan.

[0235] Database modification: The server executes an SQL command to add a new column, customer_rank.

[0236] Backend logic modifications: The server updates its API endpoints and business logic to accommodate the new fields.

[0237] User interface modifications: The server adds "customer rank" to UI components, for example adding a new text box or dropdown to an input form.

[0238] System verification after modification

[0239] The server runs automated tests to ensure the modified system works correctly.

[0240] Unit testing: The server runs tests to ensure that each modified part functions correctly.

[0241] Integration testing: The server runs tests to ensure the entire system works together correctly.

[0242] Generate and provide specifications

[0243] The server automatically generates a new system specification based on the modifications and emotion recognition results. This specification includes details of the modifications according to the user's urgency.

[0244] The terminal provides the generated new specification to the user, notifies the user that "The customer rank field has been quickly added. This allows you to quickly manage customer rank information," and displays the details of the specification.

[0245] By incorporating an emotion engine, the system of the present invention can process modification requests while taking into account the user's emotions and urgency, thereby increasing user satisfaction and improving the efficiency and accuracy of system modification work.

[0246] The processing flow will be explained below.

[0247] Step 1:

[0248] Users input requests for system modifications through the White Boxer interface. For example, they might input instructions such as, "I want to add a 'Customer Rank' field to the customer management system. Please hurry." The emotion engine analyzes the input content, input speed, sentence structure, etc., and determines the level of urgency.

[0249] Step 2:

[0250] The server analyzes the received system modification request and the emotion recognition results from the emotion engine. The natural language processing engine understands the request to add the "Customer Rank" field, and the emotion engine identifies the urgency of "please hurry."

[0251] Step 3:

[0252] The server identifies the scope of impact to the entire system based on the modification request and the perceived emotion. It identifies changes to the database schema, back-end logic, and user interface, and prioritizes the modification plan. For example, adding a "Customer Rank" field is urgently needed, so it is given a higher priority than other modification requests.

[0253] Step 4:

[0254] The server generates a specific fix plan based on the previously specified priorities, for example, adding a new column, customer_rank, to the database and quickly updating the corresponding API endpoints and UI components.

[0255] Step 5:

[0256] The server executes the modification of the system based on the generated modification plan.

[0257] Database modification: The server issues an SQL command to add a new column, customer_rank, and applies it to the database.

[0258] Backend logic modifications: The server updates API endpoints and business logic code to accommodate the new fields.

[0259] User Interface Modification: The server adds a new input field "Customer Rank" to the UI, for example by incorporating a new text box or dropdown in the form.

[0260] Step 6:

[0261] The server runs automated tests to ensure the modified system works correctly.

[0262] Unit testing: For each part of the server that has been modified, test scripts are run to verify correct operation.

[0263] Integration testing: The server runs tests to ensure the entire system works together correctly.

[0264] Step 7:

[0265] The server automatically generates new system specifications based on the modifications and emotion recognition results. The generated specifications reflect the details of the modifications requested by the user and the urgency of the modifications based on the analysis results of the emotion engine.

[0266] Step 8:

[0267] The terminal provides the generated new specification to the user. The terminal notifies the user that "The customer rank field has been quickly added. This allows you to quickly manage customer rank information," and displays the detailed specification.

[0268] This system makes it possible to modify systems in conjunction with user emotion analysis using an emotion engine, and by accurately reflecting user intentions and urgency, it is possible to improve the efficiency and accuracy of modification work.

[0269] Example 2

[0270] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0271] Conventional methods for processing system modification requests were unable to consider user emotions or urgency, resulting in inefficient prioritization and execution of modification plans. Furthermore, the increased effort required for system verification and specification creation after modifications could lead to a decline in user satisfaction. Therefore, there was a need for technology that could recognize user emotions and respond quickly and accurately based on that information.

[0272] 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.

[0273] In this invention, the server includes means for accepting a system modification request from a user, means for analyzing the system modification request and the user's emotions to identify the modification content, and means for generating a modification plan based on the modification content and the user's emotions, thereby enabling modification requests to be processed taking into account the user's emotions and urgency.

[0274] A "system modification request" is a request from a user to change, add, or delete something from the system.

[0275] "Emotion" refers to the psychological state and urgency of a user when requesting a system modification.

[0276] A "modification plan" is a document that lists specific changes and procedures that are generated based on a system modification request.

[0277] "Database schema modification" refers to making changes to the database structure, such as adding new columns or modifying existing columns.

[0278] "Back-end logic modifications" refers to changes to the processing logic on the server side of the system, including API modifications and business logic changes.

[0279] "User interface modifications" refers to changes to the screens and controls of a system that users directly interact with. This can include adding new form fields or modifying existing UI components.

[0280] "Natural language processing" is a technology that allows computers to understand and analyze human natural language. It is used to analyze the meaning of text and recognize emotions.

[0281] A "specification" is a document that describes in detail the system design, modifications, and requirements.

[0282] To implement this invention, a server, a terminal, and a generative AI model are required. The server also needs to combine an emotion engine and a natural language processing engine. This system provides a mechanism for users to input system modification requests and respond efficiently by recognizing their emotions.

[0283] The server provides a dedicated interface for users to input system modification requests. This interface can take the form of a web page or a mobile app. The emotion engine analyzes the content and method of user input to recognize the user's emotion (e.g., urgency or importance). For example, if a user inputs "I urgently want to add a 'Customer Rank' field to the customer management system," the emotion engine analyzes the phrase "urgently" to recognize the urgency.

[0284] The server uses a natural language processing engine (e.g., Google Cloud Natural Language) to analyze the request and its sentiment. This engine understands the modification request and identifies the user's urgency. Based on the identified modification content and sentiment, the server identifies the impact on the entire system and identifies changes to the database schema, backend logic, and user interface.

[0285] The server then generates a fix plan based on the urgency of the fix, for example, adding a new column, customer_rank, to the database and quickly updating the corresponding API endpoints and UI components.

[0286] The server then automatically executes the system modifications based on the generated modification plan, executing SQL commands to add new columns to the database, updating back-end logic to add API endpoints and business logic corresponding to the new fields, and updating the user interface to add new fields to input forms.

[0287] After the modifications are made, the server runs automated tests to ensure the system works correctly: unit tests ensure each modified part functions correctly, and integration tests ensure the whole system works together correctly.

[0288] Finally, the server automatically generates new system specifications based on the modifications and emotion recognition results. The specifications include detailed descriptions of modifications based on the user's level of urgency. The terminal then provides the new specifications to the user, notifying them that "The customer rank field has been quickly added. This allows you to quickly manage customer rank information," and displays the details of the specifications.

[0289] Specific examples

[0290] Prompt Sentence Examples

[0291] "I urgently need to add a 'Customer Rank' field to my customer management system. Is this possible?"

[0292] Based on this specific example, the system recognizes the user's request and the urgency of the request, and generates and executes a plan to respond quickly.

[0293] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0294] Step 1:

[0295] A user accesses a dedicated web interface using a browser and inputs a system modification request. The input data is text and meta information in the form of "I would like to urgently add a 'Customer Rank' field to the customer management system." The input data is sent to the server.

[0296] Step 2:

[0297] The server sends the received modification request to the emotion engine, which analyzes the user's emotion (urgency, importance, etc.). The emotion engine analyzes the phrase "urgent" in the text and recognizes that the request is urgent. The analysis result is sent back to the server.

[0298] Step 3:

[0299] The server uses a natural language processing engine to analyze the content of the modification request. The natural language processing engine extracts the specific request, "Add a customer rank field." The results of this analysis are combined with the results of the emotion engine and returned to the server.

[0300] Step 4:

[0301] The server identifies the extent of impact on the entire system based on the content and sentiment of the modification request. Specifically, it analyzes changes to the database schema, back-end logic, and user interface, and lists the affected areas. This list serves as the basis for generating a modification plan.

[0302] Step 5:

[0303] The server generates a fix plan based on the identified impact. For example, a plan is created to add a new column, customer_rank, to the database and change the associated API endpoints and UI components. This plan prioritizes fixes, with the most urgent items being implemented first.

[0304] Step 6:

[0305] The server automatically executes system modifications based on the generated modification plan, which specifically includes the following steps:

[0306] Database modification: Execute the SQL command to add a new column, customer_rank, and update the database.

[0307] Backend logic modifications: Update API endpoints and business logic to accommodate new fields.

[0308] User Interface Improvements: Update UI components and add a new "Customer Rank" field to the input form.

[0309] Step 7:

[0310] The server runs automated tests to verify that the modified system works correctly. Unit tests are run for each modified part, followed by an integration test for the entire system. The results of the tests are recorded in a log file and a pass / fail decision is made.

[0311] Step 8:

[0312] The server automatically generates new system specifications based on the modifications and emotion recognition results. The specifications include detailed descriptions of modifications that reflect the user's urgency. The generated specifications are sent to the device and notified to the user.

[0313] Step 9:

[0314] The terminal provides the user with the new specification received from the server, notifies the user that "The customer rank field has been quickly added. This allows you to quickly manage customer rank information," and displays details of the generated specification.

[0315] (Application example 2)

[0316] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0317] In modern factories, there is a need to provide appropriate support according to the emotions and urgency of workers. However, conventional systems have difficulty taking these emotional aspects into account, which can lead to reduced work efficiency and increased errors. Therefore, a system is needed that can recognize the emotions and urgency of workers in real time and provide appropriate support accordingly.

[0318] 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.

[0319] In this invention, the server includes means for accepting a system modification request from a user, means for analyzing the system modification request to identify the modification content, means for generating a modification plan based on the modification content, means for executing the system modification based on the generated modification plan, means for automatically verifying the system after modification, means for generating a new system specification based on the modification content, means for providing the new system specification to the user, and means for recognizing the user's emotions and urgency and generating a system modification plan and support procedures based thereon. This makes it possible to provide appropriate support in real time that takes into account the emotions and urgency of the worker.

[0320] "User" means a person or agent who uses the system and enters a modification request.

[0321] "System modification request" is input information that includes the content of a user's request for modification or updating of the system.

[0322] "Analysis" is the process of understanding the system modification requirements, identifying their content, and deriving specific modification content.

[0323] "Modifications" refer to changes or updates to the system identified through the analysis.

[0324] A "modification plan" is a specific system modification plan generated based on the identified modification content.

[0325] "Emotion" refers to emotional factors such as the feelings and urgency that users have when inputting a system modification request.

[0326] "Emotion recognition" is the process of identifying emotions and urgency from the user's input and behavior.

[0327] "System modification" refers to the work of making changes or updating the system based on the generated modification plan.

[0328] "Verification" is the process of testing and checking the modified system to ensure that it operates correctly.

[0329] A "system specification" is a document that contains detailed information about the modifications and their results.

[0330] "Support procedures" are specific procedures for providing work support according to the user's emotions and urgency.

[0331] To implement this invention, the following system program and related hardware and software must be used. The server accepts system modification requests from users, analyzes the requests, and identifies the modifications to be made. A natural language processing engine and an emotion recognition engine are used for the analysis, allowing the server to understand the user's emotions and the level of urgency.

[0332] The server then generates a fix plan based on the identified fixes, which may include database schema fixes, backend logic fixes, and user interface fixes. For example, a plan may be created to add a new column to a database and quickly update the corresponding API endpoints and UI components.

[0333] Based on the generated modification plan, the server modifies the system. After the modification, automated tests are run to verify that the system operates correctly. This testing includes unit tests and integration tests. Unit tests verify the functionality of each modified part, while integration tests verify that the entire system works together correctly.

[0334] The server also generates new system specifications based on the modifications and emotion recognition results. These specifications include details of modifications according to the user's level of urgency. The terminal provides the new specifications to the user and notifies them that "The customer rank field has been quickly added. This allows you to quickly manage customer rank information."

[0335] The following hardware and software are used to implement this system:

[0336] Hardware: Smart glasses (e.g., Google Glass®), factory robots (e.g., ABB, KUKA)

[0337] Software: Python, NLTK, TextBlob

[0338] As a concrete example, consider a factory maintenance work support system. This system recognizes the worker's emotions and urgency through smart glasses, and provides optimal procedures and warnings in real time. For example, if a worker says, "We need to replace this part quickly!", the system recognizes the urgency and displays a message on the smart glasses saying, "It looks like the task is difficult. We will display assistance procedures," and provides detailed instructions. This makes it possible to provide assistance that takes into account the worker's emotions and urgency.

[0339] Examples of prompts are:

[0340] "We will develop a system that provides detailed instructions and thoughtful support to help workers calm down when they are in a hurry. If a worker types, 'I need to replace this part quickly!' the application will recognize the urgency and provide appropriate support messages and instructions."

[0341] This makes it possible to apply the emotion engine to build support applications that improve work efficiency and safety in factories.

[0342] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0343] Step 1:

[0344] The server accepts system modification requests from users. The input is the modification request entered by the user via voice or text, and the output is the request information stored by the server. Specifically, the server receives the user's modification request through a dedicated interface and stores it in a database.

[0345] Step 2:

[0346] The server analyzes the received system modification request and identifies the modification content. The input is the request information saved in step 1, and the output is the identified modification content. Specifically, the server analyzes the request information using a natural language processing engine (e.g., NLTK) and extracts the necessary modification content.

[0347] Step 3:

[0348] The server uses an emotion recognition engine to identify the user's emotion and urgency. The input is the request information saved in step 1, and the output is the identified emotion and urgency. Specifically, it uses a library such as TextBlob to analyze the user's input and calculates the emotion score and urgency.

[0349] Step 4:

[0350] The server generates a modification plan based on the identified modification details and emotion recognition results. The input is the modification details extracted in step 2 and the emotion scores identified in step 3, and the output is a modification plan. Specifically, it generates a specific modification plan that combines the necessary database schema changes, backend logic modifications, UI component changes, etc.

[0351] Step 5:

[0352] The server executes the system modifications based on the generated modification plan. The input is the modification plan generated in step 4, and the output is the modified system. Specifically, it executes SQL commands to modify the database structure and update the code of API endpoints and UI components.

[0353] Step 6:

[0354] The server runs automated tests to verify that the modified system operates correctly. The input is the modified system, and the output is the test results. Specifically, it checks the operation of each part and the whole system through unit tests and integration tests, and reports the test results.

[0355] Step 7:

[0356] The server generates a new system specification based on the modifications and emotion recognition results. The inputs are the modifications, emotion scores, and test results, and the output is a new system specification. Specifically, it creates a properly formatted document that describes the modifications, their rationale, and the operating status of the system after the modifications.

[0357] Step 8:

[0358] The terminal provides the generated new system specification to the user. The input is the system specification generated in step 7, and the output is the information provided to the user. Specifically, the contents of the specification are displayed on the user's interface, and a notification such as "The customer rank field has been promptly added" is given.

[0359] In this way, the system can recognize the user's emotions and level of urgency, and implement efficient system modifications based on that.

[0360] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0361] 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 engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0362] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0363] [Second embodiment]

[0364] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0365] 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.

[0366] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0367] 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.

[0368] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0369] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0370] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0371] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0372] 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.

[0373] 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.

[0374] 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.

[0375] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0376] To implement the present invention, a system called White Boxer is first introduced. Next, the operation of this system will be described in detail.

[0377] System program and processing explanation

[0378] User request input

[0379] The server provides a dedicated interface for users to input system modification requests. For example, a user may input a request such as "I want to add a 'customer rank' field to the customer management system."

[0380] Parsing the request

[0381] The server analyzes the received request. This analysis uses a natural language processing engine to understand the content of the input request and identify the necessary modifications. Specifically, the server recognizes the addition of a new field called "Customer Rank."

[0382] Identifying the scope of impact and creating a plan

[0383] The server identifies how adding the new field will impact the entire system, including the need for database schema changes, back-end logic modifications, and user interface updates. The server then generates a specific remediation plan for implementing these changes.

[0384] Implementing the renovation plan

[0385] The server automatically executes system modifications based on the generated modification plan. The modifications are listed below, including specific examples.

[0386] Database schema modification: The server executes the SQL command to add a new field, customer_rank.

[0387] Backend logic modifications: The server updates its API endpoints and business logic to accommodate the new fields.

[0388] User interface modifications: The server adds new input fields to the user interface, for example, a text box or dropdown for entering "Customer Rank" in a customer information input form.

[0389] System verification after modification

[0390] The server runs automated tests to verify that the modified system operates correctly.

[0391] Unit testing: The server runs tests for newly added features and changes.

[0392] Integration testing: The server runs tests to ensure the whole system works together correctly.

[0393] Generate and provide specifications

[0394] The server automatically generates a new system specification based on the modifications. This specification includes details of each modified item. For example, it may include a specific explanation such as, "A new field, 'Customer Rank', has been added to the customer management system."

[0395] The terminal displays the specifications to the user, explaining how to use the newly added field and its impact on the entire system. The terminal notifies the user, "The customer rank field has been added. This allows you to manage customer rank information."

[0396] In this way, the system of the present invention can automate the entire process from requesting system modifications to implementing, verifying, and providing specifications, thereby preventing dependency on individual personnel and significantly improving work efficiency.

[0397] The processing flow will be explained below.

[0398] Step 1:

[0399] The user inputs a system modification request through the White Boxer interface. For example, the user inputs an instruction such as "I want to add a 'Customer Rank' field to the customer management system."

[0400] Step 2:

[0401] The server analyzes the received system modification request. During this analysis, it uses a natural language processing engine to understand the request and identify the necessary modifications. Specifically, it recognizes the addition of a new field, "Customer Rank."

[0402] Step 3:

[0403] Based on the analysis results, the server identifies the extent of the impact on the entire system, and identifies changes to the database schema, back-end logic, and user interface.

[0404] Step 4:

[0405] The server generates a specific repair plan based on the extent of the impact, for example, a plan to add a new column, customer_rank, to the database and update the corresponding API endpoints and UI components.

[0406] Step 5:

[0407] The server executes the modification of the system based on the generated modification plan.

[0408] Database modification: The server executes an SQL command to add a new column, customer_rank.

[0409] Backend logic modifications: The server updates its API endpoints and business logic to accommodate the new fields.

[0410] User interface modifications: The server adds "customer rank" to UI components, for example adding a new text box or dropdown to an input form.

[0411] Step 6:

[0412] The server runs automated tests to ensure the modified system works correctly.

[0413] Unit testing: The server runs tests to ensure that each modified part functions correctly.

[0414] Integration testing: The server runs tests to ensure the entire system works together correctly.

[0415] Step 7:

[0416] The server automatically generates a new system specification based on the modifications, which includes details of each modified item.

[0417] Step 8:

[0418] The terminal provides the generated new specification to the user, notifies the user that "The customer rank field has been added. This allows you to manage customer rank information," and displays the details of the specification.

[0419] Example 1

[0420] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0421] The traditional system modification process requires manual requirement analysis and modification plan creation, which requires a significant amount of time and effort. It is also difficult to identify the scope of impact of the modifications, making it prone to human error. Furthermore, verifying modifications and updating specifications after modifications are also time-consuming tasks, resulting in a decline in overall business efficiency.

[0422] 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.

[0423] In this invention, the server includes means for accepting a system modification request from a user, means for analyzing the system modification request using natural language processing to identify the modification content, means for identifying the scope of impact based on the modification content and generating a modification plan, means for executing the system modification based on the generated modification plan, means for automatically verifying the system after modification (including unit testing and integration testing), means for generating a new system specification based on the modification content, and means for providing the new system specification to the user. This eliminates the need for manual analysis and modification plan generation and automates the entire process, enabling efficient and error-free system modifications.

[0424] "Means for accepting system modification requests from users" refers to the interface through which users input requests for changes or additions to the system and the process by which the server receives them.

[0425] "Means for analyzing system modification requests using natural language processing to identify the modification content" refers to the process of analyzing the request content received from the user using natural language processing technology and automatically extracting the specific modification content.

[0426] "Means for identifying the scope of impact and generating a repair plan" refers to the process of assessing the impact on the entire system based on the identified repair content and formulating a detailed plan for the repair work based on that.

[0427] "Means for executing system modifications" refers to the execution process for modifying the database structure, back-end logic, and user interface according to the generated modification plan.

[0428] "Means for automatically verifying the system" refers to the process of automatically running unit tests and integration tests after a system has been modified to verify that the modified parts and the entire system are operating correctly.

[0429] The "means for generating new system specifications" refers to a process for automatically creating specifications that comprehensively describe the changes and new features of the modified system.

[0430] "Means for providing new system specifications to users" refers to the process of displaying or sharing the generated specifications with users.

[0431] "Database structural modifications" refers to making changes to the system's database schema, including adding new fields and modifying existing fields.

[0432] "Back-end logic modifications" refers to modifying a system's business logic and API endpoints, which updates the way data is processed and the server-side functionality.

[0433] "User interface modifications" refers to changes or improvements to the graphical interface through which a user interacts with a system, including adding new input fields or modifying existing UI components.

[0434] "Unit testing" refers to automated testing that verifies that individual program modules or functions work correctly.

[0435] "Integration testing" refers to automated testing that verifies that different program modules work together correctly as a whole system and behave as expected.

[0436] "Generative artificial intelligence model" refers to a machine learning algorithm used to analyze natural language input from a user and generate appropriate responses or modifications.

[0437] A "prompt sentence" refers to a natural language command or request sentence input to a generative artificial intelligence model.

[0438] This invention relates to a system that automates the system modification process. Specifically, it is a system that automates the process of analyzing modification requests from users, identifying the scope of impact, creating and executing a modification plan, and finally verifying the system and creating and providing specifications.

[0439] System Overview

[0440] First, the system provides a dedicated interface for accepting modification requests from users. This interface is implemented as a web form or a desktop application user interface. Users use this interface to input modification requests.

[0441] For example, consider the case where a user inputs a request such as "I want to add a 'customer rank' field to the customer management system." This request is sent to the server and received by the server.

[0442] Parsing the request

[0443] The server analyzes the received modification requests using a natural language processing engine (e.g., Google's BERT or OpenAI's GPT-3). It receives the request content as text, analyzes it using a generative artificial intelligence model, and identifies the specific modifications required.

[0444] Identifying the impact area and generating a repair plan

[0445] The server then evaluates the impact of the identified modifications on the entire system, which may include changing the database structure, modifying back-end logic, and updating the user interface.The server then generates a specific modification plan based on this information.

[0446] For example, the rework plan for adding a new field, customer_rank, would look like this:

[0447] Modify the database schema: Generate the SQL command to add a new field, customer_rank, to the database (e.g., ALTER TABLE customer ADD COLUMN customer_rank VARCHAR(255);).

[0448] Modifying backend logic: If the system uses Spring Boot, add new API endpoints or modify existing endpoints.

[0449] Update the user interface: If it's a web application using React, add a new input field.

[0450] Implementing the renovation plan

[0451] The server executes the system modification based on the generated modification plan. This process includes the following steps:

[0452] Executes SQL commands to make database schema changes.

[0453] Execute code to modify API endpoints and business logic.

[0454] Executes code to add a new field to the user interface.

[0455] System verification after modification

[0456] After the modifications are applied, the server automatically validates the system. First, it runs unit tests on the newly added features and modified parts. This uses a test framework such as JUnit to check the operation of individual program modules. Next, it runs integration tests to check that the entire system works correctly. This is automated testing at the UI level using a test tool such as Selenium.

[0457] Generate and provide specifications

[0458] Finally, the server automatically generates a new system specification based on the modifications. This specification includes details of each modified item and how to use it. The generated specification is sent to the terminal, which displays it to the user.

[0459] For example, the description for the new field customer_rank might read, "A new field, 'Customer Rank', has been added to the customer management system. This allows you to manage customer rank information."

[0460] Prompt Sentence Examples

[0461] An example of a specific prompt for a generative artificial intelligence model might be, "Generate code to add a 'customer rank' field to a customer management system. This should include database schema changes, backend logic modifications, and user interface updates."

[0462] The system of the present invention automates the system modification process, which was previously performed manually, enabling efficient and error-free modifications, which significantly improves business efficiency and reduces the risks associated with system modifications.

[0463] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0464] Step 1:

[0465] Entering a Request

[0466] User: Enters a modification request using a dedicated interface on the terminal. In this example, the user enters a request such as "I want to add a 'Customer Rank' field to the customer management system."

[0467] Input: Text data of the modification request.

[0468] Output: Sends a modification request to the server.

[0469] Specifically, the user fills out a modification request in a web form and clicks the "Submit" button.

[0470] Step 2:

[0471] Receiving a request

[0472] Server: Receives modification requests sent by users.

[0473] Input: Text data of the modification request from the user.

[0474] Output: The internal data structure of the modification request (e.g., in JSON format).

[0475] As an initial process, an HTTP request is received and the entered modification request is temporarily stored in a database.

[0476] Step 3:

[0477] Parsing the request

[0478] Server: Analyzes the received modification request using a natural language processing engine. Using a generative AI model (e.g., BERT or GPT-3), it identifies the intent of the request and the modification content.

[0479] Input: The internal data structure of the modification request (in JSON format).

[0480] Output: Identification of the modification (e.g. adding a new field customer_rank).

[0481] Specifically, a prompt sentence is input to the generative artificial intelligence model and the analysis results are obtained.

[0482] Step 4:

[0483] Identifying the impact area and generating a repair plan

[0484] Server: Identify the impact on the entire system based on the modifications. Automatically assess whether database schema changes, backend logic modifications, and user interface updates are required.

[0485] Input: Identification results of the repairs.

[0486] Output: A detailed improvement plan (SQL commands, API fixes, UI updates).

[0487] In this step, the database model and source code repository are analyzed to automatically extract areas that require modification.

[0488] Step 5:

[0489] Implementing the renovation plan

[0490] Server: Executes system modifications based on the generated modification plan.

[0491] Modify the database schema: Run an SQL command (e.g., ALTER TABLE customer ADD COLUMN customer_rank VARCHAR(255);) to add a new field.

[0492] Modify backend logic: Update API endpoints and business logic using Spring Boot or similar.

[0493] Update the user interface: Add new input fields to the UI using React or another front-end framework.

[0494] Input: Specific renovation plans.

[0495] Output: Modified system.

[0496] Specific actions include modifying endpoint code in a Spring Boot application or adding new React components.

[0497] Step 6:

[0498] System Verification

[0499] Server: Run automated tests to ensure the modified system works correctly.

[0500] Unit testing: Using JUnit or similar software to check the operation of individual program modules.

[0501] Integration testing: Use Selenium or similar tools to check the operation of the entire system.

[0502] Input: Modified system.

[0503] Output: Test results.

[0504] Specifically, the test script is executed and success / failure data is logged.

[0505] Step 7:

[0506] Generate and provide specifications

[0507] Server: Automatically generate a new system specification based on the modifications. This specification includes details of each modified item and how to use it.

[0508] Input: Modification details and test results.

[0509] Output: An automatically generated specification.

[0510] Terminal: Displays the generated specification to the user.

[0511] As a specific action, the following message is written about the new field customer_rank: "A new field 'Customer Rank' has been added to the customer management system. This allows you to manage customer rank information," and the message is notified to the user.

[0512] (Application example 1)

[0513] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0514] System modification work at logistics facilities is complex and requires manual work by engineers with specialized knowledge, which is time-consuming and costly. There is a demand for automation of this work, but currently there is a lack of technology to process this request quickly and accurately. A system is needed to solve this problem and improve the operational efficiency of logistics facilities.

[0515] 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.

[0516] In this invention, the server includes means for accepting a system modification request from a user, means for analyzing the system modification request to identify the modification content, means for generating a modification plan based on the modification content, means for executing the system modification based on the generated modification plan, means for automatically verifying the system after modification, means for generating a new system specification based on the modification content, means for providing the new system specification to the user, and means that is installed on a robot in the logistics facility and processes modification requests for the logistics system. This makes it possible to quickly and accurately automate system modification work in the logistics facility.

[0517] "User" refers to the person who submits a system modification request, and includes staff and managers at the logistics facility.

[0518] A "system modification request" refers to a request to add new functions or make changes to the system.

[0519] "Analysis" refers to the process of understanding the content of the system modification request and identifying the necessary modifications.

[0520] "Modification contents" refers to the specific items of system changes identified based on the system modification request.

[0521] "Renovation Plan" means a detailed plan for carrying out the identified renovations.

[0522] "Generation" refers to the process of creating and building a renovation plan.

[0523] "Execution" refers to carrying out specific system modification work based on the generated modification plan.

[0524] "Verification" refers to the process of verifying whether a modified system operates correctly.

[0525] "System Specification" means a document that contains detailed specifications for the modified system.

[0526] "Logistics facility" refers to a facility that stores and distributes goods and materials.

[0527] "Robot" refers to a mechanical device that performs automated tasks in logistics facilities.

[0528] "Natural language processing" refers to the technology for analyzing natural language and understanding its meaning.

[0529] "Database schema modification" refers to the process of changing the structure of a database.

[0530] "Back-end logic modification" refers to the process of changing the logic and algorithms that operate behind the system.

[0531] "User interface modification" refers to the work of changing the user interface of a system.

[0532] "AI model" refers to the algorithms and structure of artificial intelligence.

[0533] "Prompt sentence" refers to the input sentence used by an AI model to understand the request and generate an appropriate response.

[0534] To implement this invention, a server must first install the "AutoLogiMod" application on the robots in the logistics facility. This application automatically accepts and analyzes system modification requests, creates and executes modification plans, verifies the modified system, and creates and provides specifications.

[0535] The system's program is configured as follows: The server uses a natural language processing engine (NLP engine) to analyze the system modification request entered by the user and identify its contents. Based on the identified modification contents, the server generates a modification plan. This modification plan includes modifications to the database schema, corrections to back-end logic, and updates to the user interface. The server automatically executes the generated modification plan and modifies the system. The modified system is verified through unit testing and integration testing to confirm correct operation. Finally, the server automatically generates a new system specification based on the modification contents and provides it to the user.

[0536] The hardware used mainly consists of robots (with embedded computers) installed in logistics facilities and associated servers, while the software used includes Python programs, natural language processing engines (libraries like NLTK or SpaCy), SQL parsing engines (sqlparse), and AI-based plan generation modules.

[0537] For example, if a user inputs a system modification request such as "I want to add an expired inventory management field to the inventory management system," the server analyzes the request and generates SQL commands to add the necessary database field. It also makes corresponding changes to the back-end logic and user interface. After the modification is complete, the server runs automated tests to verify that the system is working correctly. This series of automated processes is expected to significantly improve the efficiency of logistics facilities.

[0538] Example prompt sentence:

[0539] "Please add an expired inventory management field to your inventory management system."

[0540] This invention enables the rapid and accurate automation of system modification work at logistics facilities, enabling effective management.

[0541] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0542] Step 1: User enters system modification request

[0543] The user inputs a request such as "I want to add an expired inventory management field to the inventory management system" from the terminal. This input is sent to the server. The input data is in the form of text in natural language and is input via the terminal's input interface.

[0544] Step 2: Analyze system modification requirements

[0545] The server uses a natural language processing engine (NLP engine) to analyze the input system modification request. As a result of the analysis, the modification content is identified. The input data is the user's request text, and the output data is a list or data structure of the analyzed modification content.

[0546] Step 3: Generate a renovation plan

[0547] The server generates a repair plan based on the identified repairs. The repair plan includes database schema changes, back-end logic modifications, and user interface updates. The input data is the analyzed repairs, and the output data is a list of specific repair steps and the commands required to perform them.

[0548] Step 4: Execute SQL commands

[0549] The server executes SQL commands against the database schema based on the modification plan. The input data are the SQL commands included in the modification plan, and the output data are the results of the database structural changes. Specifically, the server sends the SQL commands to the database to add new fields.

[0550] Step 5: Update your backend logic

[0551] The server updates the backend logic based on the modification plan. The input data is the changes to the backend logic included in the modification plan, and the output data is the updated source code or executable program. Specific actions include modifying API endpoints and business logic.

[0552] Step 6: Update the User Interface

[0553] The server updates the user interface based on the modification plan. The input data are the changes to the user interface included in the modification plan, and the output data is the updated user interface. Specific actions include adding new input fields and drop-down menus.

[0554] Step 7: Verify the system after modification

[0555] The server runs unit tests and integration tests to verify that the modified system operates correctly. The input data is the modified system, and the output data is the test results. Specifically, it runs automated test scripts to verify that each function operates as expected.

[0556] Step 8: Generate a system specification

[0557] The server generates a new system specification based on the modifications. The input data is the modification plan and information about the modified system, and the output data is the system specification. Specifically, it automatically generates a document that includes details of the modifications and an explanation of new functions.

[0558] Step 9: Provide system specifications

[0559] The server provides the generated system specifications to the user. The input data is the generated system specifications, and the output data is a display or download link for the user to refer to. Specific operations include displaying the specifications on the terminal or sending them by email.

[0560] Through the above steps, system modification work at logistics facilities can be automated quickly and accurately.

[0561] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0562] To implement the present invention, it is necessary to combine a system called White Boxer with an emotion engine that recognizes the user's emotions. The specific operation of a system including an emotion engine will be described below.

[0563] System program and processing explanation

[0564] User request input and emotion recognition

[0565] The server provides a dedicated interface for users to input requests for system modifications. At this time, the emotion engine analyzes the content and input method of the user's input to recognize emotions. For example, if a user emotionally inputs, "I would like to urgently add a 'Customer Rank' field to the customer management system," the "urgent" part is analyzed and the urgency is recognized.

[0566] Analyzing demands and emotions

[0567] The server analyzes the received system modification request and the recognition results of the emotion engine. The natural language processing engine understands the request content, and the emotion engine identifies the user's emotion (e.g., urgency, importance, dissatisfaction). Specifically, it adds a new field, "Customer Rank," and recognizes its high urgency.

[0568] Identifying the scope of impact and creating a plan

[0569] The server identifies the scope of impact on the entire system based on the modification request and sentiment, and prioritizes modification plans. It identifies changes to the database schema, backend logic, and user interface, and generates modification plans with priority given to those with the highest urgency. For example, it creates a plan to add a new column, customer_rank, to the database and quickly update the corresponding API endpoints and UI components.

[0570] Implementing the renovation plan

[0571] The server automatically executes the system modification based on the generated modification plan.

[0572] Database modification: The server executes an SQL command to add a new column, customer_rank.

[0573] Backend logic modifications: The server updates its API endpoints and business logic to accommodate the new fields.

[0574] User interface modifications: The server adds "customer rank" to UI components, for example adding a new text box or dropdown to an input form.

[0575] System verification after modification

[0576] The server runs automated tests to ensure the modified system works correctly.

[0577] Unit testing: The server runs tests to ensure that each modified part functions correctly.

[0578] Integration testing: The server runs tests to ensure the entire system works together correctly.

[0579] Generate and provide specifications

[0580] The server automatically generates a new system specification based on the modifications and emotion recognition results. This specification includes details of the modifications according to the user's urgency.

[0581] The terminal provides the generated new specification to the user, notifies the user that "The customer rank field has been quickly added. This allows you to quickly manage customer rank information," and displays the details of the specification.

[0582] By incorporating an emotion engine, the system of the present invention can process modification requests while taking into account the user's emotions and urgency, thereby increasing user satisfaction and improving the efficiency and accuracy of system modification work.

[0583] The processing flow will be explained below.

[0584] Step 1:

[0585] Users input requests for system modifications through the White Boxer interface. For example, they might input instructions such as, "I want to add a 'Customer Rank' field to the customer management system. Please hurry." The emotion engine analyzes the input content, input speed, sentence structure, etc., and determines the level of urgency.

[0586] Step 2:

[0587] The server analyzes the received system modification request and the emotion recognition results from the emotion engine. The natural language processing engine understands the request to add the "Customer Rank" field, and the emotion engine identifies the urgency of "please hurry."

[0588] Step 3:

[0589] The server identifies the scope of impact to the entire system based on the modification request and the perceived emotion. It identifies changes to the database schema, back-end logic, and user interface, and prioritizes the modification plan. For example, adding a "Customer Rank" field is urgently needed, so it is given a higher priority than other modification requests.

[0590] Step 4:

[0591] The server generates a specific fix plan based on the previously specified priorities, for example, adding a new column, customer_rank, to the database and quickly updating the corresponding API endpoints and UI components.

[0592] Step 5:

[0593] The server executes the modification of the system based on the generated modification plan.

[0594] Database modification: The server issues an SQL command to add a new column, customer_rank, and applies it to the database.

[0595] Backend logic modifications: The server updates API endpoints and business logic code to accommodate the new fields.

[0596] User Interface Modification: The server adds a new input field "Customer Rank" to the UI, for example by incorporating a new text box or dropdown in the form.

[0597] Step 6:

[0598] The server runs automated tests to ensure the modified system works correctly.

[0599] Unit testing: For each part of the server that has been modified, test scripts are run to verify correct operation.

[0600] Integration testing: The server runs tests to ensure the entire system works together correctly.

[0601] Step 7:

[0602] The server automatically generates new system specifications based on the modifications and emotion recognition results. The generated specifications reflect the details of the modifications requested by the user and the urgency of the modifications based on the analysis results of the emotion engine.

[0603] Step 8:

[0604] The terminal provides the generated new specification to the user. The terminal notifies the user that "The customer rank field has been quickly added. This allows you to quickly manage customer rank information," and displays the detailed specification.

[0605] This system makes it possible to modify systems in conjunction with user emotion analysis using an emotion engine, and by accurately reflecting user intentions and urgency, it is possible to improve the efficiency and accuracy of modification work.

[0606] Example 2

[0607] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0608] Conventional methods for processing system modification requests were unable to consider user emotions or urgency, resulting in inefficient prioritization and execution of modification plans. Furthermore, the increased effort required for system verification and specification creation after modifications could lead to a decline in user satisfaction. Therefore, there was a need for technology that could recognize user emotions and respond quickly and accurately based on that information.

[0609] 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.

[0610] In this invention, the server includes means for accepting a system modification request from a user, means for analyzing the system modification request and the user's emotions to identify the modification content, and means for generating a modification plan based on the modification content and the user's emotions, thereby enabling modification requests to be processed taking into account the user's emotions and urgency.

[0611] A "system modification request" is a request from a user to change, add, or delete something from the system.

[0612] "Emotion" refers to the psychological state and urgency of a user when requesting a system modification.

[0613] A "modification plan" is a document that lists specific changes and procedures that are generated based on a system modification request.

[0614] "Database schema modification" refers to making changes to the database structure, such as adding new columns or modifying existing columns.

[0615] "Back-end logic modifications" refers to changes to the processing logic on the server side of the system, including API modifications and business logic changes.

[0616] "User interface modifications" refers to changes to the screens and controls of a system that users directly interact with. This can include adding new form fields or modifying existing UI components.

[0617] "Natural language processing" is a technology that allows computers to understand and analyze human natural language. It is used to analyze the meaning of text and recognize emotions.

[0618] A "specification" is a document that describes in detail the system design, modifications, and requirements.

[0619] To implement this invention, a server, a terminal, and a generative AI model are required. The server also needs to combine an emotion engine and a natural language processing engine. This system provides a mechanism for users to input system modification requests and respond efficiently by recognizing their emotions.

[0620] The server provides a dedicated interface for users to input system modification requests. This interface can take the form of a web page or a mobile app. The emotion engine analyzes the content and method of user input to recognize the user's emotion (e.g., urgency or importance). For example, if a user inputs "I urgently want to add a 'Customer Rank' field to the customer management system," the emotion engine analyzes the phrase "urgently" to recognize the urgency.

[0621] The server uses a natural language processing engine (e.g., Google Cloud Natural Language) to analyze the request and its sentiment. This engine understands the modification request and identifies the user's urgency. Based on the identified modification content and sentiment, the server identifies the impact on the entire system and identifies changes to the database schema, backend logic, and user interface.

[0622] The server then generates a fix plan based on the urgency of the fix, for example, adding a new column, customer_rank, to the database and quickly updating the corresponding API endpoints and UI components.

[0623] The server then automatically executes the system modifications based on the generated modification plan, executing SQL commands to add new columns to the database, updating back-end logic to add API endpoints and business logic corresponding to the new fields, and updating the user interface to add new fields to input forms.

[0624] After the modifications are made, the server runs automated tests to ensure the system works correctly: unit tests ensure each modified part functions correctly, and integration tests ensure the whole system works together correctly.

[0625] Finally, the server automatically generates new system specifications based on the modifications and emotion recognition results. The specifications include detailed descriptions of modifications based on the user's level of urgency. The terminal then provides the new specifications to the user, notifying them that "The customer rank field has been quickly added. This allows you to quickly manage customer rank information," and displays the details of the specifications.

[0626] Specific examples

[0627] Prompt Sentence Examples

[0628] "I urgently need to add a 'Customer Rank' field to my customer management system. Is this possible?"

[0629] Based on this specific example, the system recognizes the user's request and the urgency of the request, and generates and executes a plan to respond quickly.

[0630] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0631] Step 1:

[0632] A user accesses a dedicated web interface using a browser and inputs a system modification request. The input data is text and meta information in the form of "I would like to urgently add a 'Customer Rank' field to the customer management system." The input data is sent to the server.

[0633] Step 2:

[0634] The server sends the received modification request to the emotion engine, which analyzes the user's emotion (urgency, importance, etc.). The emotion engine analyzes the phrase "urgent" in the text and recognizes that the request is urgent. The analysis result is sent back to the server.

[0635] Step 3:

[0636] The server uses a natural language processing engine to analyze the content of the modification request. The natural language processing engine extracts the specific request, "Add a customer rank field." The results of this analysis are combined with the results of the emotion engine and returned to the server.

[0637] Step 4:

[0638] The server identifies the extent of impact on the entire system based on the content and sentiment of the modification request. Specifically, it analyzes changes to the database schema, back-end logic, and user interface, and lists the affected areas. This list serves as the basis for generating a modification plan.

[0639] Step 5:

[0640] The server generates a fix plan based on the identified impact. For example, a plan is created to add a new column, customer_rank, to the database and change the associated API endpoints and UI components. This plan prioritizes fixes, with the most urgent items being implemented first.

[0641] Step 6:

[0642] The server automatically executes system modifications based on the generated modification plan, which specifically includes the following steps:

[0643] Database modification: Execute the SQL command to add a new column, customer_rank, and update the database.

[0644] Backend logic modifications: Update API endpoints and business logic to accommodate new fields.

[0645] User Interface Improvements: Update UI components and add a new "Customer Rank" field to the input form.

[0646] Step 7:

[0647] The server runs automated tests to verify that the modified system works correctly. Unit tests are run for each modified part, followed by an integration test for the entire system. The results of the tests are recorded in a log file and a pass / fail decision is made.

[0648] Step 8:

[0649] The server automatically generates new system specifications based on the modifications and emotion recognition results. The specifications include detailed descriptions of modifications that reflect the user's urgency. The generated specifications are sent to the device and notified to the user.

[0650] Step 9:

[0651] The terminal provides the user with the new specification received from the server, notifies the user that "The customer rank field has been quickly added. This allows you to quickly manage customer rank information," and displays details of the generated specification.

[0652] (Application example 2)

[0653] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0654] In modern factories, there is a need to provide appropriate support according to the emotions and urgency of workers. However, conventional systems have difficulty taking these emotional aspects into account, which can lead to reduced work efficiency and increased errors. Therefore, a system is needed that can recognize the emotions and urgency of workers in real time and provide appropriate support accordingly.

[0655] 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.

[0656] In this invention, the server includes means for accepting a system modification request from a user, means for analyzing the system modification request to identify the modification content, means for generating a modification plan based on the modification content, means for executing the system modification based on the generated modification plan, means for automatically verifying the system after modification, means for generating a new system specification based on the modification content, means for providing the new system specification to the user, and means for recognizing the user's emotions and urgency and generating a system modification plan and support procedures based thereon. This makes it possible to provide appropriate support in real time that takes into account the emotions and urgency of the worker.

[0657] "User" means a person or agent who uses the system and enters a modification request.

[0658] "System modification request" is input information that includes the content of a user's request for modification or updating of the system.

[0659] "Analysis" is the process of understanding the system modification requirements, identifying their content, and deriving specific modification content.

[0660] "Modifications" refer to changes or updates to the system identified through the analysis.

[0661] A "modification plan" is a specific system modification plan generated based on the identified modification content.

[0662] "Emotion" refers to emotional factors such as the feelings and urgency that users have when inputting a system modification request.

[0663] "Emotion recognition" is the process of identifying emotions and urgency from the user's input and behavior.

[0664] "System modification" refers to the work of making changes or updating the system based on the generated modification plan.

[0665] "Verification" is the process of testing and checking the modified system to ensure that it operates correctly.

[0666] A "system specification" is a document that contains detailed information about the modifications and their results.

[0667] "Support procedures" are specific procedures for providing work support according to the user's emotions and urgency.

[0668] To implement this invention, the following system program and related hardware and software must be used. The server accepts system modification requests from users, analyzes the requests, and identifies the modifications to be made. A natural language processing engine and an emotion recognition engine are used for the analysis, allowing the server to understand the user's emotions and the level of urgency.

[0669] The server then generates a fix plan based on the identified fixes, which may include database schema fixes, backend logic fixes, and user interface fixes. For example, a plan may be created to add a new column to a database and quickly update the corresponding API endpoints and UI components.

[0670] Based on the generated modification plan, the server modifies the system. After the modification, automated tests are run to verify that the system operates correctly. This testing includes unit tests and integration tests. Unit tests verify the functionality of each modified part, while integration tests verify that the entire system works together correctly.

[0671] The server also generates new system specifications based on the modifications and emotion recognition results. These specifications include details of modifications according to the user's level of urgency. The terminal provides the new specifications to the user and notifies them that "The customer rank field has been quickly added. This allows you to quickly manage customer rank information."

[0672] The following hardware and software are used to implement this system:

[0673] Hardware: Smart glasses (e.g., Google Glass), factory robots (e.g., ABB, KUKA)

[0674] Software: Python, NLTK, TextBlob

[0675] As a concrete example, consider a factory maintenance work support system. This system recognizes the worker's emotions and urgency through smart glasses, and provides optimal procedures and warnings in real time. For example, if a worker says, "We need to replace this part quickly!", the system recognizes the urgency and displays a message on the smart glasses saying, "It looks like the task is difficult. We will display assistance procedures," and provides detailed instructions. This makes it possible to provide assistance that takes into account the worker's emotions and urgency.

[0676] Examples of prompts are:

[0677] "We will develop a system that provides detailed instructions and thoughtful support to help workers calm down when they are in a hurry. If a worker types, 'I need to replace this part quickly!' the application will recognize the urgency and provide appropriate support messages and instructions."

[0678] This makes it possible to apply the emotion engine to build support applications that improve work efficiency and safety in factories.

[0679] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0680] Step 1:

[0681] The server accepts system modification requests from users. The input is the modification request entered by the user via voice or text, and the output is the request information stored by the server. Specifically, the server receives the user's modification request through a dedicated interface and stores it in a database.

[0682] Step 2:

[0683] The server analyzes the received system modification request and identifies the modification content. The input is the request information saved in step 1, and the output is the identified modification content. Specifically, the server analyzes the request information using a natural language processing engine (e.g., NLTK) and extracts the necessary modification content.

[0684] Step 3:

[0685] The server uses an emotion recognition engine to identify the user's emotion and urgency. The input is the request information saved in step 1, and the output is the identified emotion and urgency. Specifically, it uses a library such as TextBlob to analyze the user's input and calculates the emotion score and urgency.

[0686] Step 4:

[0687] The server generates a modification plan based on the identified modification details and emotion recognition results. The input is the modification details extracted in step 2 and the emotion scores identified in step 3, and the output is a modification plan. Specifically, it generates a specific modification plan that combines the necessary database schema changes, backend logic modifications, UI component changes, etc.

[0688] Step 5:

[0689] The server executes the system modifications based on the generated modification plan. The input is the modification plan generated in step 4, and the output is the modified system. Specifically, it executes SQL commands to modify the database structure and update the code of API endpoints and UI components.

[0690] Step 6:

[0691] The server runs automated tests to verify that the modified system operates correctly. The input is the modified system, and the output is the test results. Specifically, it checks the operation of each part and the whole system through unit tests and integration tests, and reports the test results.

[0692] Step 7:

[0693] The server generates a new system specification based on the modifications and emotion recognition results. The inputs are the modifications, emotion scores, and test results, and the output is a new system specification. Specifically, it creates a properly formatted document that describes the modifications, their rationale, and the operating status of the system after the modifications.

[0694] Step 8:

[0695] The terminal provides the generated new system specification to the user. The input is the system specification generated in step 7, and the output is the information provided to the user. Specifically, the contents of the specification are displayed on the user's interface, and a notification such as "The customer rank field has been promptly added" is given.

[0696] In this way, the system can recognize the user's emotions and level of urgency, and implement efficient system modifications based on that.

[0697] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0698] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0699] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0700] [Third embodiment]

[0701] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0702] 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.

[0703] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0704] 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.

[0705] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0706] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0707] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0708] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0709] 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.

[0710] 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.

[0711] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0712] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0713] To implement the present invention, a system called White Boxer is first introduced. Next, the operation of this system will be described in detail.

[0714] System program and processing explanation

[0715] User request input

[0716] The server provides a dedicated interface for users to input system modification requests. For example, a user may input a request such as "I want to add a 'customer rank' field to the customer management system."

[0717] Parsing the request

[0718] The server analyzes the received request. This analysis uses a natural language processing engine to understand the content of the input request and identify the necessary modifications. Specifically, the server recognizes the addition of a new field called "Customer Rank."

[0719] Identifying the scope of impact and creating a plan

[0720] The server identifies how adding the new field will impact the entire system, including the need for database schema changes, back-end logic modifications, and user interface updates. The server then generates a specific remediation plan for implementing these changes.

[0721] Implementing the renovation plan

[0722] The server automatically executes system modifications based on the generated modification plan. The modifications are listed below, including specific examples.

[0723] Database schema modification: The server executes the SQL command to add a new field, customer_rank.

[0724] Backend logic modifications: The server updates its API endpoints and business logic to accommodate the new fields.

[0725] User interface modifications: The server adds new input fields to the user interface, for example, a text box or dropdown for entering "Customer Rank" in a customer information input form.

[0726] System verification after modification

[0727] The server runs automated tests to verify that the modified system operates correctly.

[0728] Unit testing: The server runs tests for newly added features and changes.

[0729] Integration testing: The server runs tests to ensure the whole system works together correctly.

[0730] Generate and provide specifications

[0731] The server automatically generates a new system specification based on the modifications. This specification includes details of each modified item. For example, it may include a specific explanation such as, "A new field, 'Customer Rank', has been added to the customer management system."

[0732] The terminal displays the specifications to the user, explaining how to use the newly added field and its impact on the entire system. The terminal notifies the user, "The customer rank field has been added. This allows you to manage customer rank information."

[0733] In this way, the system of the present invention can automate the entire process from requesting system modifications to implementing, verifying, and providing specifications, thereby preventing dependency on individual personnel and significantly improving work efficiency.

[0734] The processing flow will be explained below.

[0735] Step 1:

[0736] The user inputs a system modification request through the White Boxer interface. For example, the user inputs an instruction such as "I want to add a 'Customer Rank' field to the customer management system."

[0737] Step 2:

[0738] The server analyzes the received system modification request. During this analysis, it uses a natural language processing engine to understand the request and identify the necessary modifications. Specifically, it recognizes the addition of a new field, "Customer Rank."

[0739] Step 3:

[0740] Based on the analysis results, the server identifies the extent of the impact on the entire system, and identifies changes to the database schema, back-end logic, and user interface.

[0741] Step 4:

[0742] The server generates a specific repair plan based on the extent of the impact, for example, a plan to add a new column, customer_rank, to the database and update the corresponding API endpoints and UI components.

[0743] Step 5:

[0744] The server executes the modification of the system based on the generated modification plan.

[0745] Database modification: The server executes an SQL command to add a new column, customer_rank.

[0746] Backend logic modifications: The server updates its API endpoints and business logic to accommodate the new fields.

[0747] User interface modifications: The server adds "customer rank" to UI components, for example adding a new text box or dropdown to an input form.

[0748] Step 6:

[0749] The server runs automated tests to ensure the modified system works correctly.

[0750] Unit testing: The server runs tests to ensure that each modified part functions correctly.

[0751] Integration testing: The server runs tests to ensure the entire system works together correctly.

[0752] Step 7:

[0753] The server automatically generates a new system specification based on the modifications, which includes details of each modified item.

[0754] Step 8:

[0755] The terminal provides the generated new specification to the user, notifies the user that "The customer rank field has been added. This allows you to manage customer rank information," and displays the details of the specification.

[0756] Example 1

[0757] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0758] The traditional system modification process requires manual requirement analysis and modification plan creation, which requires a significant amount of time and effort. It is also difficult to identify the scope of impact of the modifications, making it prone to human error. Furthermore, verifying modifications and updating specifications after modifications are also time-consuming tasks, resulting in a decline in overall business efficiency.

[0759] 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.

[0760] In this invention, the server includes means for accepting a system modification request from a user, means for analyzing the system modification request using natural language processing to identify the modification content, means for identifying the scope of impact based on the modification content and generating a modification plan, means for executing the system modification based on the generated modification plan, means for automatically verifying the system after modification (including unit testing and integration testing), means for generating a new system specification based on the modification content, and means for providing the new system specification to the user. This eliminates the need for manual analysis and modification plan generation and automates the entire process, enabling efficient and error-free system modifications.

[0761] "Means for accepting system modification requests from users" refers to the interface through which users input requests for changes or additions to the system and the process by which the server receives them.

[0762] "Means for analyzing system modification requests using natural language processing to identify the modification content" refers to the process of analyzing the request content received from the user using natural language processing technology and automatically extracting the specific modification content.

[0763] "Means for identifying the scope of impact and generating a repair plan" refers to the process of assessing the impact on the entire system based on the identified repair content and formulating a detailed plan for the repair work based on that.

[0764] "Means for executing system modifications" refers to the execution process for modifying the database structure, back-end logic, and user interface according to the generated modification plan.

[0765] "Means for automatically verifying the system" refers to the process of automatically running unit tests and integration tests after a system has been modified to verify that the modified parts and the entire system are operating correctly.

[0766] The "means for generating new system specifications" refers to a process for automatically creating specifications that comprehensively describe the changes and new features of the modified system.

[0767] "Means for providing new system specifications to users" refers to the process of displaying or sharing the generated specifications with users.

[0768] "Database structural modifications" refers to making changes to the system's database schema, including adding new fields and modifying existing fields.

[0769] "Back-end logic modifications" refers to modifying a system's business logic and API endpoints, which updates the way data is processed and the server-side functionality.

[0770] "User interface modifications" refers to changes or improvements to the graphical interface through which a user interacts with a system, including adding new input fields or modifying existing UI components.

[0771] "Unit testing" refers to automated testing that verifies that individual program modules or functions work correctly.

[0772] "Integration testing" refers to automated testing that verifies that different program modules work together correctly as a whole system and behave as expected.

[0773] "Generative artificial intelligence model" refers to a machine learning algorithm used to analyze natural language input from a user and generate appropriate responses or modifications.

[0774] A "prompt sentence" refers to a natural language command or request sentence input to a generative artificial intelligence model.

[0775] This invention relates to a system that automates the system modification process. Specifically, it is a system that automates the process of analyzing modification requests from users, identifying the scope of impact, creating and executing a modification plan, and finally verifying the system and creating and providing specifications.

[0776] System Overview

[0777] First, the system provides a dedicated interface for accepting modification requests from users. This interface is implemented as a web form or a desktop application user interface. Users use this interface to input modification requests.

[0778] For example, consider the case where a user inputs a request such as "I want to add a 'customer rank' field to the customer management system." This request is sent to the server and received by the server.

[0779] Parsing the request

[0780] The server analyzes the received modification requests using a natural language processing engine (e.g., Google's BERT or OpenAI's GPT-3). It receives the request content as text, analyzes it using a generative artificial intelligence model, and identifies the specific modifications required.

[0781] Identifying the impact area and generating a repair plan

[0782] The server then evaluates the impact of the identified modifications on the entire system, which may include changing the database structure, modifying back-end logic, and updating the user interface.The server then generates a specific modification plan based on this information.

[0783] For example, the rework plan for adding a new field, customer_rank, would look like this:

[0784] Modify the database schema: Generate the SQL command to add a new field, customer_rank, to the database (e.g., ALTER TABLE customer ADD COLUMN customer_rank VARCHAR(255);).

[0785] Modifying backend logic: If the system uses Spring Boot, add new API endpoints or modify existing endpoints.

[0786] Update the user interface: If it's a web application using React, add a new input field.

[0787] Implementing the renovation plan

[0788] The server executes the system modification based on the generated modification plan. This process includes the following steps:

[0789] Executes SQL commands to make database schema changes.

[0790] Execute code to modify API endpoints and business logic.

[0791] Executes code to add a new field to the user interface.

[0792] System verification after modification

[0793] After the modifications are applied, the server automatically validates the system. First, it runs unit tests on the newly added features and modified parts. This uses a test framework such as JUnit to check the operation of individual program modules. Next, it runs integration tests to check that the entire system works correctly. This is automated testing at the UI level using a test tool such as Selenium.

[0794] Generate and provide specifications

[0795] Finally, the server automatically generates a new system specification based on the modifications. This specification includes details of each modified item and how to use it. The generated specification is sent to the terminal, which displays it to the user.

[0796] For example, the description for the new field customer_rank might read, "A new field, 'Customer Rank', has been added to the customer management system. This allows you to manage customer rank information."

[0797] Prompt Sentence Examples

[0798] An example of a specific prompt for a generative artificial intelligence model might be, "Generate code to add a 'customer rank' field to a customer management system. This should include database schema changes, backend logic modifications, and user interface updates."

[0799] The system of the present invention automates the system modification process, which was previously performed manually, enabling efficient and error-free modifications, which significantly improves business efficiency and reduces the risks associated with system modifications.

[0800] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0801] Step 1:

[0802] Entering a Request

[0803] User: Enters a modification request using a dedicated interface on the terminal. In this example, the user enters a request such as "I want to add a 'Customer Rank' field to the customer management system."

[0804] Input: Text data of the modification request.

[0805] Output: Sends a modification request to the server.

[0806] Specifically, the user fills out a modification request in a web form and clicks the "Submit" button.

[0807] Step 2:

[0808] Receiving a request

[0809] Server: Receives modification requests sent by users.

[0810] Input: Text data of the modification request from the user.

[0811] Output: The internal data structure of the modification request (e.g., in JSON format).

[0812] As an initial process, an HTTP request is received and the entered modification request is temporarily stored in a database.

[0813] Step 3:

[0814] Parsing the request

[0815] Server: Analyzes the received modification request using a natural language processing engine. Using a generative AI model (e.g., BERT or GPT-3), it identifies the intent of the request and the modification content.

[0816] Input: The internal data structure of the modification request (in JSON format).

[0817] Output: Identification of the modification (e.g. adding a new field customer_rank).

[0818] Specifically, a prompt sentence is input to the generative artificial intelligence model and the analysis results are obtained.

[0819] Step 4:

[0820] Identifying the impact area and generating a repair plan

[0821] Server: Identify the impact on the entire system based on the modifications. Automatically assess whether database schema changes, backend logic modifications, and user interface updates are required.

[0822] Input: Identification results of the repairs.

[0823] Output: A detailed improvement plan (SQL commands, API fixes, UI updates).

[0824] In this step, the database model and source code repository are analyzed to automatically extract areas that require modification.

[0825] Step 5:

[0826] Implementing the renovation plan

[0827] Server: Executes system modifications based on the generated modification plan.

[0828] Modify the database schema: Run an SQL command (e.g., ALTER TABLE customer ADD COLUMN customer_rank VARCHAR(255);) to add a new field.

[0829] Modify backend logic: Update API endpoints and business logic using Spring Boot or similar.

[0830] Update the user interface: Add new input fields to the UI using React or another front-end framework.

[0831] Input: Specific renovation plans.

[0832] Output: Modified system.

[0833] Specific actions include modifying endpoint code in a Spring Boot application or adding new React components.

[0834] Step 6:

[0835] System Verification

[0836] Server: Run automated tests to ensure the modified system works correctly.

[0837] Unit testing: Using JUnit or similar software to check the operation of individual program modules.

[0838] Integration testing: Use Selenium or similar tools to check the operation of the entire system.

[0839] Input: Modified system.

[0840] Output: Test results.

[0841] Specifically, the test script is executed and success / failure data is logged.

[0842] Step 7:

[0843] Generate and provide specifications

[0844] Server: Automatically generate a new system specification based on the modifications. This specification includes details of each modified item and how to use it.

[0845] Input: Modification details and test results.

[0846] Output: An automatically generated specification.

[0847] Terminal: Displays the generated specification to the user.

[0848] As a specific action, the following message is written about the new field customer_rank: "A new field 'Customer Rank' has been added to the customer management system. This allows you to manage customer rank information," and the message is notified to the user.

[0849] (Application example 1)

[0850] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0851] System modification work at logistics facilities is complex and requires manual work by engineers with specialized knowledge, which is time-consuming and costly. There is a demand for automation of this work, but currently there is a lack of technology to process this request quickly and accurately. A system is needed to solve this problem and improve the operational efficiency of logistics facilities.

[0852] 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.

[0853] In this invention, the server includes means for accepting a system modification request from a user, means for analyzing the system modification request to identify the modification content, means for generating a modification plan based on the modification content, means for executing the system modification based on the generated modification plan, means for automatically verifying the system after modification, means for generating a new system specification based on the modification content, means for providing the new system specification to the user, and means that is installed on a robot in the logistics facility and processes modification requests for the logistics system. This makes it possible to quickly and accurately automate system modification work in the logistics facility.

[0854] "User" refers to the person who submits a system modification request, and includes staff and managers at the logistics facility.

[0855] A "system modification request" refers to a request to add new functions or make changes to the system.

[0856] "Analysis" refers to the process of understanding the content of the system modification request and identifying the necessary modifications.

[0857] "Modification contents" refers to the specific items of system changes identified based on the system modification request.

[0858] "Renovation Plan" means a detailed plan for carrying out the identified renovations.

[0859] "Generation" refers to the process of creating and building a renovation plan.

[0860] "Execution" refers to carrying out specific system modification work based on the generated modification plan.

[0861] "Verification" refers to the process of verifying whether a modified system operates correctly.

[0862] "System Specification" means a document that contains detailed specifications for the modified system.

[0863] "Logistics facility" refers to a facility that stores and distributes goods and materials.

[0864] "Robot" refers to a mechanical device that performs automated tasks in logistics facilities.

[0865] "Natural language processing" refers to the technology for analyzing natural language and understanding its meaning.

[0866] "Database schema modification" refers to the process of changing the structure of a database.

[0867] "Back-end logic modification" refers to the process of changing the logic and algorithms that operate behind the system.

[0868] "User interface modification" refers to the work of changing the user interface of a system.

[0869] "AI model" refers to the algorithms and structure of artificial intelligence.

[0870] "Prompt sentence" refers to the input sentence used by an AI model to understand the request and generate an appropriate response.

[0871] To implement this invention, a server must first install the "AutoLogiMod" application on the robots in the logistics facility. This application automatically accepts and analyzes system modification requests, creates and executes modification plans, verifies the modified system, and creates and provides specifications.

[0872] The system's program is configured as follows: The server uses a natural language processing engine (NLP engine) to analyze the system modification request entered by the user and identify its contents. Based on the identified modification contents, the server generates a modification plan. This modification plan includes modifications to the database schema, corrections to back-end logic, and updates to the user interface. The server automatically executes the generated modification plan and modifies the system. The modified system is verified through unit testing and integration testing to confirm correct operation. Finally, the server automatically generates a new system specification based on the modification contents and provides it to the user.

[0873] The hardware used mainly consists of robots (with embedded computers) installed in logistics facilities and associated servers, while the software used includes Python programs, natural language processing engines (libraries like NLTK or SpaCy), SQL parsing engines (sqlparse), and AI-based plan generation modules.

[0874] For example, if a user inputs a system modification request such as "I want to add an expired inventory management field to the inventory management system," the server analyzes the request and generates SQL commands to add the necessary database field. It also makes corresponding changes to the back-end logic and user interface. After the modification is complete, the server runs automated tests to verify that the system is working correctly. This series of automated processes is expected to significantly improve the efficiency of logistics facilities.

[0875] Example prompt sentence:

[0876] "Please add an expired inventory management field to your inventory management system."

[0877] This invention enables the rapid and accurate automation of system modification work at logistics facilities, enabling effective management.

[0878] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0879] Step 1: User enters system modification request

[0880] The user inputs a request such as "I want to add an expired inventory management field to the inventory management system" from the terminal. This input is sent to the server. The input data is in the form of text in natural language and is input via the terminal's input interface.

[0881] Step 2: Analyze system modification requirements

[0882] The server uses a natural language processing engine (NLP engine) to analyze the input system modification request. As a result of the analysis, the modification content is identified. The input data is the user's request text, and the output data is a list or data structure of the analyzed modification content.

[0883] Step 3: Generate a renovation plan

[0884] The server generates a repair plan based on the identified repairs. The repair plan includes database schema changes, back-end logic modifications, and user interface updates. The input data is the analyzed repairs, and the output data is a list of specific repair steps and the commands required to perform them.

[0885] Step 4: Execute SQL commands

[0886] The server executes SQL commands against the database schema based on the modification plan. The input data are the SQL commands included in the modification plan, and the output data are the results of the database structural changes. Specifically, the server sends the SQL commands to the database to add new fields.

[0887] Step 5: Update your backend logic

[0888] The server updates the backend logic based on the modification plan. The input data is the changes to the backend logic included in the modification plan, and the output data is the updated source code or executable program. Specific actions include modifying API endpoints and business logic.

[0889] Step 6: Update the User Interface

[0890] The server updates the user interface based on the modification plan. The input data are the changes to the user interface included in the modification plan, and the output data is the updated user interface. Specific actions include adding new input fields and drop-down menus.

[0891] Step 7: Verify the system after modification

[0892] The server runs unit tests and integration tests to verify that the modified system operates correctly. The input data is the modified system, and the output data is the test results. Specifically, it runs automated test scripts to verify that each function operates as expected.

[0893] Step 8: Generate a system specification

[0894] The server generates a new system specification based on the modifications. The input data is the modification plan and information about the modified system, and the output data is the system specification. Specifically, it automatically generates a document that includes details of the modifications and an explanation of new functions.

[0895] Step 9: Provide system specifications

[0896] The server provides the generated system specifications to the user. The input data is the generated system specifications, and the output data is a display or download link for the user to refer to. Specific operations include displaying the specifications on the terminal or sending them by email.

[0897] Through the above steps, system modification work at logistics facilities can be automated quickly and accurately.

[0898] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0899] To implement the present invention, it is necessary to combine a system called White Boxer with an emotion engine that recognizes the user's emotions. The specific operation of a system including an emotion engine will be described below.

[0900] System program and processing explanation

[0901] User request input and emotion recognition

[0902] The server provides a dedicated interface for users to input requests for system modifications. At this time, the emotion engine analyzes the content and input method of the user's input to recognize emotions. For example, if a user emotionally inputs, "I would like to urgently add a 'Customer Rank' field to the customer management system," the "urgent" part is analyzed and the urgency is recognized.

[0903] Analyzing demands and emotions

[0904] The server analyzes the received system modification request and the recognition results of the emotion engine. The natural language processing engine understands the request content, and the emotion engine identifies the user's emotion (e.g., urgency, importance, dissatisfaction). Specifically, it adds a new field, "Customer Rank," and recognizes its high urgency.

[0905] Identifying the scope of impact and creating a plan

[0906] The server identifies the scope of impact on the entire system based on the modification request and sentiment, and prioritizes modification plans. It identifies changes to the database schema, backend logic, and user interface, and generates modification plans with priority given to those with the highest urgency. For example, it creates a plan to add a new column, customer_rank, to the database and quickly update the corresponding API endpoints and UI components.

[0907] Implementing the renovation plan

[0908] The server automatically executes the system modification based on the generated modification plan.

[0909] Database modification: The server executes an SQL command to add a new column, customer_rank.

[0910] Backend logic modifications: The server updates its API endpoints and business logic to accommodate the new fields.

[0911] User interface modifications: The server adds "customer rank" to UI components, for example adding a new text box or dropdown to an input form.

[0912] System verification after modification

[0913] The server runs automated tests to ensure the modified system works correctly.

[0914] Unit testing: The server runs tests to ensure that each modified part functions correctly.

[0915] Integration testing: The server runs tests to ensure the entire system works together correctly.

[0916] Generate and provide specifications

[0917] The server automatically generates a new system specification based on the modifications and emotion recognition results. This specification includes details of the modifications according to the user's urgency.

[0918] The terminal provides the generated new specification to the user, notifies the user that "The customer rank field has been quickly added. This allows you to quickly manage customer rank information," and displays the details of the specification.

[0919] By incorporating an emotion engine, the system of the present invention can process modification requests while taking into account the user's emotions and urgency, thereby increasing user satisfaction and improving the efficiency and accuracy of system modification work.

[0920] The processing flow will be explained below.

[0921] Step 1:

[0922] Users input requests for system modifications through the White Boxer interface. For example, they might input instructions such as, "I want to add a 'Customer Rank' field to the customer management system. Please hurry." The emotion engine analyzes the input content, input speed, sentence structure, etc., and determines the level of urgency.

[0923] Step 2:

[0924] The server analyzes the received system modification request and the emotion recognition results from the emotion engine. The natural language processing engine understands the request to add the "Customer Rank" field, and the emotion engine identifies the urgency of "please hurry."

[0925] Step 3:

[0926] The server identifies the scope of impact to the entire system based on the modification request and the perceived emotion. It identifies changes to the database schema, back-end logic, and user interface, and prioritizes the modification plan. For example, adding a "Customer Rank" field is urgently needed, so it is given a higher priority than other modification requests.

[0927] Step 4:

[0928] The server generates a specific fix plan based on the previously specified priorities, for example, adding a new column, customer_rank, to the database and quickly updating the corresponding API endpoints and UI components.

[0929] Step 5:

[0930] The server executes the modification of the system based on the generated modification plan.

[0931] Database modification: The server issues an SQL command to add a new column, customer_rank, and applies it to the database.

[0932] Backend logic modifications: The server updates API endpoints and business logic code to accommodate the new fields.

[0933] User Interface Modification: The server adds a new input field "Customer Rank" to the UI, for example by incorporating a new text box or dropdown in the form.

[0934] Step 6:

[0935] The server runs automated tests to ensure the modified system works correctly.

[0936] Unit testing: For each part of the server that has been modified, test scripts are run to verify correct operation.

[0937] Integration testing: The server runs tests to ensure the entire system works together correctly.

[0938] Step 7:

[0939] The server automatically generates new system specifications based on the modifications and emotion recognition results. The generated specifications reflect the details of the modifications requested by the user and the urgency of the modifications based on the analysis results of the emotion engine.

[0940] Step 8:

[0941] The terminal provides the generated new specification to the user. The terminal notifies the user that "The customer rank field has been quickly added. This allows you to quickly manage customer rank information," and displays the detailed specification.

[0942] This system makes it possible to modify systems in conjunction with user emotion analysis using an emotion engine, and by accurately reflecting user intentions and urgency, it is possible to improve the efficiency and accuracy of modification work.

[0943] Example 2

[0944] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0945] Conventional methods for processing system modification requests were unable to consider user emotions or urgency, resulting in inefficient prioritization and execution of modification plans. Furthermore, the increased effort required for system verification and specification creation after modifications could lead to a decline in user satisfaction. Therefore, there was a need for technology that could recognize user emotions and respond quickly and accurately based on that information.

[0946] 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.

[0947] In this invention, the server includes means for accepting a system modification request from a user, means for analyzing the system modification request and the user's emotions to identify the modification content, and means for generating a modification plan based on the modification content and the user's emotions, thereby enabling modification requests to be processed taking into account the user's emotions and urgency.

[0948] A "system modification request" is a request from a user to change, add, or delete something from the system.

[0949] "Emotion" refers to the psychological state and urgency of a user when requesting a system modification.

[0950] A "modification plan" is a document that lists specific changes and procedures that are generated based on a system modification request.

[0951] "Database schema modification" refers to making changes to the database structure, such as adding new columns or modifying existing columns.

[0952] "Back-end logic modifications" refers to changes to the processing logic on the server side of the system, including API modifications and business logic changes.

[0953] "User interface modifications" refers to changes to the screens and controls of a system that users directly interact with. This can include adding new form fields or modifying existing UI components.

[0954] "Natural language processing" is a technology that allows computers to understand and analyze human natural language. It is used to analyze the meaning of text and recognize emotions.

[0955] A "specification" is a document that describes in detail the system design, modifications, and requirements.

[0956] To implement this invention, a server, a terminal, and a generative AI model are required. The server also needs to combine an emotion engine and a natural language processing engine. This system provides a mechanism for users to input system modification requests and respond efficiently by recognizing their emotions.

[0957] The server provides a dedicated interface for users to input system modification requests. This interface can take the form of a web page or a mobile app. The emotion engine analyzes the content and method of user input to recognize the user's emotion (e.g., urgency or importance). For example, if a user inputs "I urgently want to add a 'Customer Rank' field to the customer management system," the emotion engine analyzes the phrase "urgently" to recognize the urgency.

[0958] The server uses a natural language processing engine (e.g., Google Cloud Natural Language) to analyze the request and its sentiment. This engine understands the modification request and identifies the user's urgency. Based on the identified modification content and sentiment, the server identifies the impact on the entire system and identifies changes to the database schema, backend logic, and user interface.

[0959] The server then generates a fix plan based on the urgency of the fix, for example, adding a new column, customer_rank, to the database and quickly updating the corresponding API endpoints and UI components.

[0960] The server then automatically executes the system modifications based on the generated modification plan, executing SQL commands to add new columns to the database, updating back-end logic to add API endpoints and business logic corresponding to the new fields, and updating the user interface to add new fields to input forms.

[0961] After the modifications are made, the server runs automated tests to ensure the system works correctly: unit tests ensure each modified part functions correctly, and integration tests ensure the whole system works together correctly.

[0962] Finally, the server automatically generates new system specifications based on the modifications and emotion recognition results. The specifications include detailed descriptions of modifications based on the user's level of urgency. The terminal then provides the new specifications to the user, notifying them that "The customer rank field has been quickly added. This allows you to quickly manage customer rank information," and displays the details of the specifications.

[0963] Specific examples

[0964] Prompt Sentence Examples

[0965] "I urgently need to add a 'Customer Rank' field to my customer management system. Is this possible?"

[0966] Based on this specific example, the system recognizes the user's request and the urgency of the request, and generates and executes a plan to respond quickly.

[0967] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0968] Step 1:

[0969] A user accesses a dedicated web interface using a browser and inputs a system modification request. The input data is text and meta information in the form of "I would like to urgently add a 'Customer Rank' field to the customer management system." The input data is sent to the server.

[0970] Step 2:

[0971] The server sends the received modification request to the emotion engine, which analyzes the user's emotion (urgency, importance, etc.). The emotion engine analyzes the phrase "urgent" in the text and recognizes that the request is urgent. The analysis result is sent back to the server.

[0972] Step 3:

[0973] The server uses a natural language processing engine to analyze the content of the modification request. The natural language processing engine extracts the specific request, "Add a customer rank field." The results of this analysis are combined with the results of the emotion engine and returned to the server.

[0974] Step 4:

[0975] The server identifies the extent of impact on the entire system based on the content and sentiment of the modification request. Specifically, it analyzes changes to the database schema, back-end logic, and user interface, and lists the affected areas. This list serves as the basis for generating a modification plan.

[0976] Step 5:

[0977] The server generates a fix plan based on the identified impact. For example, a plan is created to add a new column, customer_rank, to the database and change the associated API endpoints and UI components. This plan prioritizes fixes, with the most urgent items being implemented first.

[0978] Step 6:

[0979] The server automatically executes system modifications based on the generated modification plan, which specifically includes the following steps:

[0980] Database modification: Execute the SQL command to add a new column, customer_rank, and update the database.

[0981] Backend logic modifications: Update API endpoints and business logic to accommodate new fields.

[0982] User Interface Improvements: Update UI components and add a new "Customer Rank" field to the input form.

[0983] Step 7:

[0984] The server runs automated tests to verify that the modified system works correctly. Unit tests are run for each modified part, followed by an integration test for the entire system. The results of the tests are recorded in a log file and a pass / fail decision is made.

[0985] Step 8:

[0986] The server automatically generates new system specifications based on the modifications and emotion recognition results. The specifications include detailed descriptions of modifications that reflect the user's urgency. The generated specifications are sent to the device and notified to the user.

[0987] Step 9:

[0988] The terminal provides the user with the new specification received from the server, notifies the user that "The customer rank field has been quickly added. This allows you to quickly manage customer rank information," and displays details of the generated specification.

[0989] (Application example 2)

[0990] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0991] In modern factories, there is a need to provide appropriate support according to the emotions and urgency of workers. However, conventional systems have difficulty taking these emotional aspects into account, which can lead to reduced work efficiency and increased errors. Therefore, a system is needed that can recognize the emotions and urgency of workers in real time and provide appropriate support accordingly.

[0992] 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.

[0993] In this invention, the server includes means for accepting a system modification request from a user, means for analyzing the system modification request to identify the modification content, means for generating a modification plan based on the modification content, means for executing the system modification based on the generated modification plan, means for automatically verifying the system after modification, means for generating a new system specification based on the modification content, means for providing the new system specification to the user, and means for recognizing the user's emotions and urgency and generating a system modification plan and support procedures based thereon. This makes it possible to provide appropriate support in real time that takes into account the emotions and urgency of the worker.

[0994] "User" means a person or agent who uses the system and enters a modification request.

[0995] "System modification request" is input information that includes the content of a user's request for modification or updating of the system.

[0996] "Analysis" is the process of understanding the system modification requirements, identifying their content, and deriving specific modification content.

[0997] "Modifications" refer to changes or updates to the system identified through the analysis.

[0998] A "modification plan" is a specific system modification plan generated based on the identified modification content.

[0999] "Emotion" refers to emotional factors such as the feelings and urgency that users have when inputting a system modification request.

[1000] "Emotion recognition" is the process of identifying emotions and urgency from the user's input and behavior.

[1001] "System modification" refers to the work of making changes or updating the system based on the generated modification plan.

[1002] "Verification" is the process of testing and checking the modified system to ensure that it operates correctly.

[1003] A "system specification" is a document that contains detailed information about the modifications and their results.

[1004] "Support procedures" are specific procedures for providing work support according to the user's emotions and urgency.

[1005] To implement this invention, the following system program and related hardware and software must be used. The server accepts system modification requests from users, analyzes the requests, and identifies the modifications to be made. A natural language processing engine and an emotion recognition engine are used for the analysis, allowing the server to understand the user's emotions and the level of urgency.

[1006] The server then generates a fix plan based on the identified fixes, which may include database schema fixes, backend logic fixes, and user interface fixes. For example, a plan may be created to add a new column to a database and quickly update the corresponding API endpoints and UI components.

[1007] Based on the generated modification plan, the server modifies the system. After the modification, automated tests are run to verify that the system operates correctly. This testing includes unit tests and integration tests. Unit tests verify the functionality of each modified part, while integration tests verify that the entire system works together correctly.

[1008] The server also generates new system specifications based on the modifications and emotion recognition results. These specifications include details of modifications according to the user's level of urgency. The terminal provides the new specifications to the user and notifies them that "The customer rank field has been quickly added. This allows you to quickly manage customer rank information."

[1009] The following hardware and software are used to implement this system:

[1010] Hardware: Smart glasses (e.g., Google Glass), factory robots (e.g., ABB, KUKA)

[1011] Software: Python, NLTK, TextBlob

[1012] As a concrete example, consider a factory maintenance work support system. This system recognizes the worker's emotions and urgency through smart glasses, and provides optimal procedures and warnings in real time. For example, if a worker says, "We need to replace this part quickly!", the system recognizes the urgency and displays a message on the smart glasses saying, "It looks like the task is difficult. We will display assistance procedures," and provides detailed instructions. This makes it possible to provide assistance that takes into account the worker's emotions and urgency.

[1013] Examples of prompts are:

[1014] "We will develop a system that provides detailed instructions and thoughtful support to help workers calm down when they are in a hurry. If a worker types, 'I need to replace this part quickly!' the application will recognize the urgency and provide appropriate support messages and instructions."

[1015] This makes it possible to apply the emotion engine to build support applications that improve work efficiency and safety in factories.

[1016] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1017] Step 1:

[1018] The server accepts system modification requests from users. The input is the modification request entered by the user via voice or text, and the output is the request information stored by the server. Specifically, the server receives the user's modification request through a dedicated interface and stores it in a database.

[1019] Step 2:

[1020] The server analyzes the received system modification request and identifies the modification content. The input is the request information saved in step 1, and the output is the identified modification content. Specifically, the server analyzes the request information using a natural language processing engine (e.g., NLTK) and extracts the necessary modification content.

[1021] Step 3:

[1022] The server uses an emotion recognition engine to identify the user's emotion and urgency. The input is the request information saved in step 1, and the output is the identified emotion and urgency. Specifically, it uses a library such as TextBlob to analyze the user's input and calculates the emotion score and urgency.

[1023] Step 4:

[1024] The server generates a modification plan based on the identified modification details and emotion recognition results. The input is the modification details extracted in step 2 and the emotion scores identified in step 3, and the output is a modification plan. Specifically, it generates a specific modification plan that combines the necessary database schema changes, backend logic modifications, UI component changes, etc.

[1025] Step 5:

[1026] The server executes the system modifications based on the generated modification plan. The input is the modification plan generated in step 4, and the output is the modified system. Specifically, it executes SQL commands to modify the database structure and update the code of API endpoints and UI components.

[1027] Step 6:

[1028] The server runs automated tests to verify that the modified system operates correctly. The input is the modified system, and the output is the test results. Specifically, it checks the operation of each part and the whole system through unit tests and integration tests, and reports the test results.

[1029] Step 7:

[1030] The server generates a new system specification based on the modifications and emotion recognition results. The inputs are the modifications, emotion scores, and test results, and the output is a new system specification. Specifically, it creates a properly formatted document that describes the modifications, their rationale, and the operating status of the system after the modifications.

[1031] Step 8:

[1032] The terminal provides the generated new system specification to the user. The input is the system specification generated in step 7, and the output is the information provided to the user. Specifically, the contents of the specification are displayed on the user's interface, and a notification such as "The customer rank field has been promptly added" is given.

[1033] In this way, the system can recognize the user's emotions and level of urgency, and implement efficient system modifications based on that.

[1034] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1035] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1036] 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.

[1037] [Fourth embodiment]

[1038] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1039] 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.

[1040] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1041] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1042] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1043] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1044] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1045] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1046] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1047] 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.

[1048] 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.

[1049] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1050] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1051] To implement the present invention, a system called White Boxer is first introduced. Next, the operation of this system will be described in detail.

[1052] System program and processing explanation

[1053] User request input

[1054] The server provides a dedicated interface for users to input system modification requests. For example, a user may input a request such as "I want to add a 'customer rank' field to the customer management system."

[1055] Parsing the request

[1056] The server analyzes the received request. This analysis uses a natural language processing engine to understand the content of the input request and identify the necessary modifications. Specifically, the server recognizes the addition of a new field called "Customer Rank."

[1057] Identifying the scope of impact and creating a plan

[1058] The server identifies how adding the new field will impact the entire system, including the need for database schema changes, back-end logic modifications, and user interface updates. The server then generates a specific remediation plan for implementing these changes.

[1059] Implementing the renovation plan

[1060] The server automatically executes system modifications based on the generated modification plan. The modifications are listed below, including specific examples.

[1061] Database schema modification: The server executes the SQL command to add a new field, customer_rank.

[1062] Backend logic modifications: The server updates its API endpoints and business logic to accommodate the new fields.

[1063] User interface modifications: The server adds new input fields to the user interface, for example, a text box or dropdown for entering "Customer Rank" in a customer information input form.

[1064] System verification after modification

[1065] The server runs automated tests to verify that the modified system operates correctly.

[1066] Unit testing: The server runs tests for newly added features and changes.

[1067] Integration testing: The server runs tests to ensure the whole system works together correctly.

[1068] Generate and provide specifications

[1069] The server automatically generates a new system specification based on the modifications. This specification includes details of each modified item. For example, it may include a specific explanation such as, "A new field, 'Customer Rank', has been added to the customer management system."

[1070] The terminal displays the specifications to the user, explaining how to use the newly added field and its impact on the entire system. The terminal notifies the user, "The customer rank field has been added. This allows you to manage customer rank information."

[1071] In this way, the system of the present invention can automate the entire process from requesting system modifications to implementing, verifying, and providing specifications, thereby preventing dependency on individual personnel and significantly improving work efficiency.

[1072] The processing flow will be explained below.

[1073] Step 1:

[1074] The user inputs a system modification request through the White Boxer interface. For example, the user inputs an instruction such as "I want to add a 'Customer Rank' field to the customer management system."

[1075] Step 2:

[1076] The server analyzes the received system modification request. During this analysis, it uses a natural language processing engine to understand the request and identify the necessary modifications. Specifically, it recognizes the addition of a new field, "Customer Rank."

[1077] Step 3:

[1078] Based on the analysis results, the server identifies the extent of the impact on the entire system, and identifies changes to the database schema, back-end logic, and user interface.

[1079] Step 4:

[1080] The server generates a specific repair plan based on the extent of the impact, for example, a plan to add a new column, customer_rank, to the database and update the corresponding API endpoints and UI components.

[1081] Step 5:

[1082] The server executes the modification of the system based on the generated modification plan.

[1083] Database modification: The server executes an SQL command to add a new column, customer_rank.

[1084] Backend logic modifications: The server updates its API endpoints and business logic to accommodate the new fields.

[1085] User interface modifications: The server adds "customer rank" to UI components, for example adding a new text box or dropdown to an input form.

[1086] Step 6:

[1087] The server runs automated tests to ensure the modified system works correctly.

[1088] Unit testing: The server runs tests to ensure that each modified part functions correctly.

[1089] Integration testing: The server runs tests to ensure the entire system works together correctly.

[1090] Step 7:

[1091] The server automatically generates a new system specification based on the modifications, which includes details of each modified item.

[1092] Step 8:

[1093] The terminal provides the generated new specification to the user, notifies the user that "The customer rank field has been added. This allows you to manage customer rank information," and displays the details of the specification.

[1094] Example 1

[1095] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1096] The traditional system modification process requires manual requirement analysis and modification plan creation, which requires a significant amount of time and effort. It is also difficult to identify the scope of impact of the modifications, making it prone to human error. Furthermore, verifying modifications and updating specifications after modifications are also time-consuming tasks, resulting in a decline in overall business efficiency.

[1097] 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.

[1098] In this invention, the server includes means for accepting a system modification request from a user, means for analyzing the system modification request using natural language processing to identify the modification content, means for identifying the scope of impact based on the modification content and generating a modification plan, means for executing the system modification based on the generated modification plan, means for automatically verifying the system after modification (including unit testing and integration testing), means for generating a new system specification based on the modification content, and means for providing the new system specification to the user. This eliminates the need for manual analysis and modification plan generation and automates the entire process, enabling efficient and error-free system modifications.

[1099] "Means for accepting system modification requests from users" refers to the interface through which users input requests for changes or additions to the system and the process by which the server receives them.

[1100] "Means for analyzing system modification requests using natural language processing to identify the modification content" refers to the process of analyzing the request content received from the user using natural language processing technology and automatically extracting the specific modification content.

[1101] "Means for identifying the scope of impact and generating a repair plan" refers to the process of assessing the impact on the entire system based on the identified repair content and formulating a detailed plan for the repair work based on that.

[1102] "Means for executing system modifications" refers to the execution process for modifying the database structure, back-end logic, and user interface according to the generated modification plan.

[1103] "Means for automatically verifying the system" refers to the process of automatically running unit tests and integration tests after a system has been modified to verify that the modified parts and the entire system are operating correctly.

[1104] The "means for generating new system specifications" refers to a process for automatically creating specifications that comprehensively describe the changes and new features of the modified system.

[1105] "Means for providing new system specifications to users" refers to the process of displaying or sharing the generated specifications with users.

[1106] "Database structural modifications" refers to making changes to the system's database schema, including adding new fields and modifying existing fields.

[1107] "Back-end logic modifications" refers to modifying a system's business logic and API endpoints, which updates the way data is processed and the server-side functionality.

[1108] "User interface modifications" refers to changes or improvements to the graphical interface through which a user interacts with a system, including adding new input fields or modifying existing UI components.

[1109] "Unit testing" refers to automated testing that verifies that individual program modules or functions work correctly.

[1110] "Integration testing" refers to automated testing that verifies that different program modules work together correctly as a whole system and behave as expected.

[1111] "Generative artificial intelligence model" refers to a machine learning algorithm used to analyze natural language input from a user and generate appropriate responses or modifications.

[1112] A "prompt sentence" refers to a natural language command or request sentence input to a generative artificial intelligence model.

[1113] This invention relates to a system that automates the system modification process. Specifically, it is a system that automates the process of analyzing modification requests from users, identifying the scope of impact, creating and executing a modification plan, and finally verifying the system and creating and providing specifications.

[1114] System Overview

[1115] First, the system provides a dedicated interface for accepting modification requests from users. This interface is implemented as a web form or a desktop application user interface. Users use this interface to input modification requests.

[1116] For example, consider the case where a user inputs a request such as "I want to add a 'customer rank' field to the customer management system." This request is sent to the server and received by the server.

[1117] Parsing the request

[1118] The server analyzes the received modification requests using a natural language processing engine (e.g., Google's BERT or OpenAI's GPT-3). It receives the request content as text, analyzes it using a generative artificial intelligence model, and identifies the specific modifications required.

[1119] Identifying the impact area and generating a repair plan

[1120] The server then evaluates the impact of the identified modifications on the entire system, which may include changing the database structure, modifying back-end logic, and updating the user interface.The server then generates a specific modification plan based on this information.

[1121] For example, the rework plan for adding a new field, customer_rank, would look like this:

[1122] Modify the database schema: Generate the SQL command to add a new field, customer_rank, to the database (e.g., ALTER TABLE customer ADD COLUMN customer_rank VARCHAR(255);).

[1123] Modifying backend logic: If the system uses Spring Boot, add new API endpoints or modify existing endpoints.

[1124] Update the user interface: If it's a web application using React, add a new input field.

[1125] Implementing the renovation plan

[1126] The server executes the system modification based on the generated modification plan. This process includes the following steps:

[1127] Executes SQL commands to make database schema changes.

[1128] Execute code to modify API endpoints and business logic.

[1129] Executes code to add a new field to the user interface.

[1130] System verification after modification

[1131] After the modifications are applied, the server automatically validates the system. First, it runs unit tests on the newly added features and modified parts. This uses a test framework such as JUnit to check the operation of individual program modules. Next, it runs integration tests to check that the entire system works correctly. This is automated testing at the UI level using a test tool such as Selenium.

[1132] Generate and provide specifications

[1133] Finally, the server automatically generates a new system specification based on the modifications. This specification includes details of each modified item and how to use it. The generated specification is sent to the terminal, which displays it to the user.

[1134] For example, the description for the new field customer_rank might read, "A new field, 'Customer Rank', has been added to the customer management system. This allows you to manage customer rank information."

[1135] Prompt Sentence Examples

[1136] An example of a specific prompt for a generative artificial intelligence model might be, "Generate code to add a 'customer rank' field to a customer management system. This should include database schema changes, backend logic modifications, and user interface updates."

[1137] The system of the present invention automates the system modification process, which was previously performed manually, enabling efficient and error-free modifications, which significantly improves business efficiency and reduces the risks associated with system modifications.

[1138] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1139] Step 1:

[1140] Entering a Request

[1141] User: Enters a modification request using a dedicated interface on the terminal. In this example, the user enters a request such as "I want to add a 'Customer Rank' field to the customer management system."

[1142] Input: Text data of the modification request.

[1143] Output: Sends a modification request to the server.

[1144] Specifically, the user fills out a modification request in a web form and clicks the "Submit" button.

[1145] Step 2:

[1146] Receiving a request

[1147] Server: Receives modification requests sent by users.

[1148] Input: Text data of the modification request from the user.

[1149] Output: The internal data structure of the modification request (e.g., in JSON format).

[1150] As an initial process, an HTTP request is received and the entered modification request is temporarily stored in a database.

[1151] Step 3:

[1152] Parsing the request

[1153] Server: Analyzes the received modification request using a natural language processing engine. Using a generative AI model (e.g., BERT or GPT-3), it identifies the intent of the request and the modification content.

[1154] Input: The internal data structure of the modification request (in JSON format).

[1155] Output: Identification of the modification (e.g. adding a new field customer_rank).

[1156] Specifically, a prompt sentence is input to the generative artificial intelligence model and the analysis results are obtained.

[1157] Step 4:

[1158] Identifying the impact area and generating a repair plan

[1159] Server: Identify the impact on the entire system based on the modifications. Automatically assess whether database schema changes, backend logic modifications, and user interface updates are required.

[1160] Input: Identification results of the repairs.

[1161] Output: A detailed improvement plan (SQL commands, API fixes, UI updates).

[1162] In this step, the database model and source code repository are analyzed to automatically extract areas that require modification.

[1163] Step 5:

[1164] Implementing the renovation plan

[1165] Server: Executes system modifications based on the generated modification plan.

[1166] Modify the database schema: Run an SQL command (e.g., ALTER TABLE customer ADD COLUMN customer_rank VARCHAR(255);) to add a new field.

[1167] Modify backend logic: Update API endpoints and business logic using Spring Boot or similar.

[1168] Update the user interface: Add new input fields to the UI using React or another front-end framework.

[1169] Input: Specific renovation plans.

[1170] Output: Modified system.

[1171] Specific actions include modifying endpoint code in a Spring Boot application or adding new React components.

[1172] Step 6:

[1173] System Verification

[1174] Server: Run automated tests to ensure the modified system works correctly.

[1175] Unit testing: Using JUnit or similar software to check the operation of individual program modules.

[1176] Integration testing: Use Selenium or similar tools to check the operation of the entire system.

[1177] Input: Modified system.

[1178] Output: Test results.

[1179] Specifically, the test script is executed and success / failure data is logged.

[1180] Step 7:

[1181] Generate and provide specifications

[1182] Server: Automatically generate a new system specification based on the modifications. This specification includes details of each modified item and how to use it.

[1183] Input: Modification details and test results.

[1184] Output: An automatically generated specification.

[1185] Terminal: Displays the generated specification to the user.

[1186] As a specific action, the following message is written about the new field customer_rank: "A new field 'Customer Rank' has been added to the customer management system. This allows you to manage customer rank information," and the message is notified to the user.

[1187] (Application example 1)

[1188] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1189] System modification work at logistics facilities is complex and requires manual work by engineers with specialized knowledge, which is time-consuming and costly. There is a demand for automation of this work, but currently there is a lack of technology to process this request quickly and accurately. A system is needed to solve this problem and improve the operational efficiency of logistics facilities.

[1190] 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.

[1191] In this invention, the server includes means for accepting a system modification request from a user, means for analyzing the system modification request to identify the modification content, means for generating a modification plan based on the modification content, means for executing the system modification based on the generated modification plan, means for automatically verifying the system after modification, means for generating a new system specification based on the modification content, means for providing the new system specification to the user, and means that is installed on a robot in the logistics facility and processes modification requests for the logistics system. This makes it possible to quickly and accurately automate system modification work in the logistics facility.

[1192] "User" refers to the person who submits a system modification request, and includes staff and managers at the logistics facility.

[1193] A "system modification request" refers to a request to add new functions or make changes to the system.

[1194] "Analysis" refers to the process of understanding the content of the system modification request and identifying the necessary modifications.

[1195] "Modification contents" refers to the specific items of system changes identified based on the system modification request.

[1196] "Renovation Plan" means a detailed plan for carrying out the identified renovations.

[1197] "Generation" refers to the process of creating and building a renovation plan.

[1198] "Execution" refers to carrying out specific system modification work based on the generated modification plan.

[1199] "Verification" refers to the process of verifying whether a modified system operates correctly.

[1200] "System Specification" means a document that contains detailed specifications for the modified system.

[1201] "Logistics facility" refers to a facility that stores and distributes goods and materials.

[1202] "Robot" refers to a mechanical device that performs automated tasks in logistics facilities.

[1203] "Natural language processing" refers to the technology for analyzing natural language and understanding its meaning.

[1204] "Database schema modification" refers to the process of changing the structure of a database.

[1205] "Back-end logic modification" refers to the process of changing the logic and algorithms that operate behind the system.

[1206] "User interface modification" refers to the work of changing the user interface of a system.

[1207] "AI model" refers to the algorithms and structure of artificial intelligence.

[1208] "Prompt sentence" refers to the input sentence used by an AI model to understand the request and generate an appropriate response.

[1209] To implement this invention, a server must first install the "AutoLogiMod" application on the robots in the logistics facility. This application automatically accepts and analyzes system modification requests, creates and executes modification plans, verifies the modified system, and creates and provides specifications.

[1210] The system's program is configured as follows: The server uses a natural language processing engine (NLP engine) to analyze the system modification request entered by the user and identify its contents. Based on the identified modification contents, the server generates a modification plan. This modification plan includes modifications to the database schema, corrections to back-end logic, and updates to the user interface. The server automatically executes the generated modification plan and modifies the system. The modified system is verified through unit testing and integration testing to confirm correct operation. Finally, the server automatically generates a new system specification based on the modification contents and provides it to the user.

[1211] The hardware used mainly consists of robots (with embedded computers) installed in logistics facilities and associated servers, while the software used includes Python programs, natural language processing engines (libraries like NLTK or SpaCy), SQL parsing engines (sqlparse), and AI-based plan generation modules.

[1212] For example, if a user inputs a system modification request such as "I want to add an expired inventory management field to the inventory management system," the server analyzes the request and generates SQL commands to add the necessary database field. It also makes corresponding changes to the back-end logic and user interface. After the modification is complete, the server runs automated tests to verify that the system is working correctly. This series of automated processes is expected to significantly improve the efficiency of logistics facilities.

[1213] Example prompt sentence:

[1214] "Please add an expired inventory management field to your inventory management system."

[1215] This invention enables the rapid and accurate automation of system modification work at logistics facilities, enabling effective management.

[1216] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1217] Step 1: User enters system modification request

[1218] The user inputs a request such as "I want to add an expired inventory management field to the inventory management system" from the terminal. This input is sent to the server. The input data is in the form of text in natural language and is input via the terminal's input interface.

[1219] Step 2: Analyze system modification requirements

[1220] The server uses a natural language processing engine (NLP engine) to analyze the input system modification request. As a result of the analysis, the modification content is identified. The input data is the user's request text, and the output data is a list or data structure of the analyzed modification content.

[1221] Step 3: Generate a renovation plan

[1222] The server generates a repair plan based on the identified repairs. The repair plan includes database schema changes, back-end logic modifications, and user interface updates. The input data is the analyzed repairs, and the output data is a list of specific repair steps and the commands required to perform them.

[1223] Step 4: Execute SQL commands

[1224] The server executes SQL commands against the database schema based on the modification plan. The input data are the SQL commands included in the modification plan, and the output data are the results of the database structural changes. Specifically, the server sends the SQL commands to the database to add new fields.

[1225] Step 5: Update your backend logic

[1226] The server updates the backend logic based on the modification plan. The input data is the changes to the backend logic included in the modification plan, and the output data is the updated source code or executable program. Specific actions include modifying API endpoints and business logic.

[1227] Step 6: Update the User Interface

[1228] The server updates the user interface based on the modification plan. The input data are the changes to the user interface included in the modification plan, and the output data is the updated user interface. Specific actions include adding new input fields and drop-down menus.

[1229] Step 7: Verify the system after modification

[1230] The server runs unit tests and integration tests to verify that the modified system operates correctly. The input data is the modified system, and the output data is the test results. Specifically, it runs automated test scripts to verify that each function operates as expected.

[1231] Step 8: Generate a system specification

[1232] The server generates a new system specification based on the modifications. The input data is the modification plan and information about the modified system, and the output data is the system specification. Specifically, it automatically generates a document that includes details of the modifications and an explanation of new functions.

[1233] Step 9: Provide system specifications

[1234] The server provides the generated system specifications to the user. The input data is the generated system specifications, and the output data is a display or download link for the user to refer to. Specific operations include displaying the specifications on the terminal or sending them by email.

[1235] Through the above steps, system modification work at logistics facilities can be automated quickly and accurately.

[1236] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1237] To implement the present invention, it is necessary to combine a system called White Boxer with an emotion engine that recognizes the user's emotions. The specific operation of a system including an emotion engine will be described below.

[1238] System program and processing explanation

[1239] User request input and emotion recognition

[1240] The server provides a dedicated interface for users to input requests for system modifications. At this time, the emotion engine analyzes the content and input method of the user's input to recognize emotions. For example, if a user emotionally inputs, "I would like to urgently add a 'Customer Rank' field to the customer management system," the "urgent" part is analyzed and the urgency is recognized.

[1241] Analyzing demands and emotions

[1242] The server analyzes the received system modification request and the recognition results of the emotion engine. The natural language processing engine understands the request content, and the emotion engine identifies the user's emotion (e.g., urgency, importance, dissatisfaction). Specifically, it adds a new field, "Customer Rank," and recognizes its high urgency.

[1243] Identifying the scope of impact and creating a plan

[1244] The server identifies the scope of impact on the entire system based on the modification request and sentiment, and prioritizes modification plans. It identifies changes to the database schema, backend logic, and user interface, and generates modification plans with priority given to those with the highest urgency. For example, it creates a plan to add a new column, customer_rank, to the database and quickly update the corresponding API endpoints and UI components.

[1245] Implementing the renovation plan

[1246] The server automatically executes the system modification based on the generated modification plan.

[1247] Database modification: The server executes an SQL command to add a new column, customer_rank.

[1248] Backend logic modifications: The server updates its API endpoints and business logic to accommodate the new fields.

[1249] User interface modifications: The server adds "customer rank" to UI components, for example adding a new text box or dropdown to an input form.

[1250] System verification after modification

[1251] The server runs automated tests to ensure the modified system works correctly.

[1252] Unit testing: The server runs tests to ensure that each modified part functions correctly.

[1253] Integration testing: The server runs tests to ensure the entire system works together correctly.

[1254] Generate and provide specifications

[1255] The server automatically generates a new system specification based on the modifications and emotion recognition results. This specification includes details of the modifications according to the user's urgency.

[1256] The terminal provides the generated new specification to the user, notifies the user that "The customer rank field has been quickly added. This allows you to quickly manage customer rank information," and displays the details of the specification.

[1257] By incorporating an emotion engine, the system of the present invention can process modification requests while taking into account the user's emotions and urgency, thereby increasing user satisfaction and improving the efficiency and accuracy of system modification work.

[1258] The processing flow will be explained below.

[1259] Step 1:

[1260] Users input requests for system modifications through the White Boxer interface. For example, they might input instructions such as, "I want to add a 'Customer Rank' field to the customer management system. Please hurry." The emotion engine analyzes the input content, input speed, sentence structure, etc., and determines the level of urgency.

[1261] Step 2:

[1262] The server analyzes the received system modification request and the emotion recognition results from the emotion engine. The natural language processing engine understands the request to add the "Customer Rank" field, and the emotion engine identifies the urgency of "please hurry."

[1263] Step 3:

[1264] The server identifies the scope of impact to the entire system based on the modification request and the perceived emotion. It identifies changes to the database schema, back-end logic, and user interface, and prioritizes the modification plan. For example, adding a "Customer Rank" field is urgently needed, so it is given a higher priority than other modification requests.

[1265] Step 4:

[1266] The server generates a specific fix plan based on the previously specified priorities, for example, adding a new column, customer_rank, to the database and quickly updating the corresponding API endpoints and UI components.

[1267] Step 5:

[1268] The server executes the modification of the system based on the generated modification plan.

[1269] Database modification: The server issues an SQL command to add a new column, customer_rank, and applies it to the database.

[1270] Backend logic modifications: The server updates API endpoints and business logic code to accommodate the new fields.

[1271] User Interface Modification: The server adds a new input field "Customer Rank" to the UI, for example by incorporating a new text box or dropdown in the form.

[1272] Step 6:

[1273] The server runs automated tests to ensure the modified system works correctly.

[1274] Unit testing: For each part of the server that has been modified, test scripts are run to verify correct operation.

[1275] Integration testing: The server runs tests to ensure the entire system works together correctly.

[1276] Step 7:

[1277] The server automatically generates new system specifications based on the modifications and emotion recognition results. The generated specifications reflect the details of the modifications requested by the user and the urgency of the modifications based on the analysis results of the emotion engine.

[1278] Step 8:

[1279] The terminal provides the generated new specification to the user. The terminal notifies the user that "The customer rank field has been quickly added. This allows you to quickly manage customer rank information," and displays the detailed specification.

[1280] This system makes it possible to modify systems in conjunction with user emotion analysis using an emotion engine, and by accurately reflecting user intentions and urgency, it is possible to improve the efficiency and accuracy of modification work.

[1281] Example 2

[1282] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1283] Conventional methods for processing system modification requests were unable to consider user emotions or urgency, resulting in inefficient prioritization and execution of modification plans. Furthermore, the increased effort required for system verification and specification creation after modifications could lead to a decline in user satisfaction. Therefore, there was a need for technology that could recognize user emotions and respond quickly and accurately based on that information.

[1284] 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.

[1285] In this invention, the server includes means for accepting a system modification request from a user, means for analyzing the system modification request and the user's emotions to identify the modification content, and means for generating a modification plan based on the modification content and the user's emotions, thereby enabling modification requests to be processed taking into account the user's emotions and urgency.

[1286] A "system modification request" is a request from a user to change, add, or delete something from the system.

[1287] "Emotion" refers to the psychological state and urgency of a user when requesting a system modification.

[1288] A "modification plan" is a document that lists specific changes and procedures that are generated based on a system modification request.

[1289] "Database schema modification" refers to making changes to the database structure, such as adding new columns or modifying existing columns.

[1290] "Back-end logic modifications" refers to changes to the processing logic on the server side of the system, including API modifications and business logic changes.

[1291] "User interface modifications" refers to changes to the screens and controls of a system that users directly interact with. This can include adding new form fields or modifying existing UI components.

[1292] "Natural language processing" is a technology that allows computers to understand and analyze human natural language. It is used to analyze the meaning of text and recognize emotions.

[1293] A "specification" is a document that describes in detail the system design, modifications, and requirements.

[1294] To implement this invention, a server, a terminal, and a generative AI model are required. The server also needs to combine an emotion engine and a natural language processing engine. This system provides a mechanism for users to input system modification requests and respond efficiently by recognizing their emotions.

[1295] The server provides a dedicated interface for users to input system modification requests. This interface can take the form of a web page or a mobile app. The emotion engine analyzes the content and method of user input to recognize the user's emotion (e.g., urgency or importance). For example, if a user inputs "I urgently want to add a 'Customer Rank' field to the customer management system," the emotion engine analyzes the phrase "urgently" to recognize the urgency.

[1296] The server uses a natural language processing engine (e.g., Google Cloud Natural Language) to analyze the request and its sentiment. This engine understands the modification request and identifies the user's urgency. Based on the identified modification content and sentiment, the server identifies the impact on the entire system and identifies changes to the database schema, backend logic, and user interface.

[1297] The server then generates a fix plan based on the urgency of the fix, for example, adding a new column, customer_rank, to the database and quickly updating the corresponding API endpoints and UI components.

[1298] The server then automatically executes the system modifications based on the generated modification plan, executing SQL commands to add new columns to the database, updating back-end logic to add API endpoints and business logic corresponding to the new fields, and updating the user interface to add new fields to input forms.

[1299] After the modifications are made, the server runs automated tests to ensure the system works correctly: unit tests ensure each modified part functions correctly, and integration tests ensure the whole system works together correctly.

[1300] Finally, the server automatically generates new system specifications based on the modifications and emotion recognition results. The specifications include detailed descriptions of modifications based on the user's level of urgency. The terminal then provides the new specifications to the user, notifying them that "The customer rank field has been quickly added. This allows you to quickly manage customer rank information," and displays the details of the specifications.

[1301] Specific examples

[1302] Prompt Sentence Examples

[1303] "I urgently need to add a 'Customer Rank' field to my customer management system. Is this possible?"

[1304] Based on this specific example, the system recognizes the user's request and the urgency of the request, and generates and executes a plan to respond quickly.

[1305] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1306] Step 1:

[1307] A user accesses a dedicated web interface using a browser and inputs a system modification request. The input data is text and meta information in the form of "I would like to urgently add a 'Customer Rank' field to the customer management system." The input data is sent to the server.

[1308] Step 2:

[1309] The server sends the received modification request to the emotion engine, which analyzes the user's emotion (urgency, importance, etc.). The emotion engine analyzes the phrase "urgent" in the text and recognizes that the request is urgent. The analysis result is sent back to the server.

[1310] Step 3:

[1311] The server uses a natural language processing engine to analyze the content of the modification request. The natural language processing engine extracts the specific request, "Add a customer rank field." The results of this analysis are combined with the results of the emotion engine and returned to the server.

[1312] Step 4:

[1313] The server identifies the extent of impact on the entire system based on the content and sentiment of the modification request. Specifically, it analyzes changes to the database schema, back-end logic, and user interface, and lists the affected areas. This list serves as the basis for generating a modification plan.

[1314] Step 5:

[1315] The server generates a fix plan based on the identified impact. For example, a plan is created to add a new column, customer_rank, to the database and change the associated API endpoints and UI components. This plan prioritizes fixes, with the most urgent items being implemented first.

[1316] Step 6:

[1317] The server automatically executes system modifications based on the generated modification plan, which specifically includes the following steps:

[1318] Database modification: Execute the SQL command to add a new column, customer_rank, and update the database.

[1319] Backend logic modifications: Update API endpoints and business logic to accommodate new fields.

[1320] User Interface Improvements: Update UI components and add a new "Customer Rank" field to the input form.

[1321] Step 7:

[1322] The server runs automated tests to verify that the modified system works correctly. Unit tests are run for each modified part, followed by an integration test for the entire system. The results of the tests are recorded in a log file and a pass / fail decision is made.

[1323] Step 8:

[1324] The server automatically generates new system specifications based on the modifications and emotion recognition results. The specifications include detailed descriptions of modifications that reflect the user's urgency. The generated specifications are sent to the device and notified to the user.

[1325] Step 9:

[1326] The terminal provides the user with the new specification received from the server, notifies the user that "The customer rank field has been quickly added. This allows you to quickly manage customer rank information," and displays details of the generated specification.

[1327] (Application example 2)

[1328] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1329] In modern factories, there is a need to provide appropriate support according to the emotions and urgency of workers. However, conventional systems have difficulty taking these emotional aspects into account, which can lead to reduced work efficiency and increased errors. Therefore, a system is needed that can recognize the emotions and urgency of workers in real time and provide appropriate support accordingly.

[1330] 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.

[1331] In this invention, the server includes means for accepting a system modification request from a user, means for analyzing the system modification request to identify the modification content, means for generating a modification plan based on the modification content, means for executing the system modification based on the generated modification plan, means for automatically verifying the system after modification, means for generating a new system specification based on the modification content, means for providing the new system specification to the user, and means for recognizing the user's emotions and urgency and generating a system modification plan and support procedures based thereon. This makes it possible to provide appropriate support in real time that takes into account the emotions and urgency of the worker.

[1332] "User" means a person or agent who uses the system and enters a modification request.

[1333] "System modification request" is input information that includes the content of a user's request for modification or updating of the system.

[1334] "Analysis" is the process of understanding the system modification requirements, identifying their content, and deriving specific modification content.

[1335] "Modifications" refer to changes or updates to the system identified through the analysis.

[1336] A "modification plan" is a specific system modification plan generated based on the identified modification content.

[1337] "Emotion" refers to emotional factors such as the feelings and urgency that users have when inputting a system modification request.

[1338] "Emotion recognition" is the process of identifying emotions and urgency from the user's input and behavior.

[1339] "System modification" refers to the work of making changes or updating the system based on the generated modification plan.

[1340] "Verification" is the process of testing and checking the modified system to ensure that it operates correctly.

[1341] A "system specification" is a document that contains detailed information about the modifications and their results.

[1342] "Support procedures" are specific procedures for providing work support according to the user's emotions and urgency.

[1343] To implement this invention, the following system program and related hardware and software must be used. The server accepts system modification requests from users, analyzes the requests, and identifies the modifications to be made. A natural language processing engine and an emotion recognition engine are used for the analysis, allowing the server to understand the user's emotions and the level of urgency.

[1344] The server then generates a fix plan based on the identified fixes, which may include database schema fixes, backend logic fixes, and user interface fixes. For example, a plan may be created to add a new column to a database and quickly update the corresponding API endpoints and UI components.

[1345] Based on the generated modification plan, the server modifies the system. After the modification, automated tests are run to verify that the system operates correctly. This testing includes unit tests and integration tests. Unit tests verify the functionality of each modified part, while integration tests verify that the entire system works together correctly.

[1346] The server also generates new system specifications based on the modifications and emotion recognition results. These specifications include details of modifications according to the user's level of urgency. The terminal provides the new specifications to the user and notifies them that "The customer rank field has been quickly added. This allows you to quickly manage customer rank information."

[1347] The following hardware and software are used to implement this system:

[1348] Hardware: Smart glasses (e.g., Google Glass), factory robots (e.g., ABB, KUKA)

[1349] Software: Python, NLTK, TextBlob

[1350] As a concrete example, consider a factory maintenance work support system. This system recognizes the worker's emotions and urgency through smart glasses, and provides optimal procedures and warnings in real time. For example, if a worker says, "We need to replace this part quickly!", the system recognizes the urgency and displays a message on the smart glasses saying, "It looks like the task is difficult. We will display assistance procedures," and provides detailed instructions. This makes it possible to provide assistance that takes into account the worker's emotions and urgency.

[1351] Examples of prompts are:

[1352] "We will develop a system that provides detailed instructions and thoughtful support to help workers calm down when they are in a hurry. If a worker types, 'I need to replace this part quickly!' the application will recognize the urgency and provide appropriate support messages and instructions."

[1353] This makes it possible to apply the emotion engine to build support applications that improve work efficiency and safety in factories.

[1354] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1355] Step 1:

[1356] The server accepts system modification requests from users. The input is the modification request entered by the user via voice or text, and the output is the request information stored by the server. Specifically, the server receives the user's modification request through a dedicated interface and stores it in a database.

[1357] Step 2:

[1358] The server analyzes the received system modification request and identifies the modification content. The input is the request information saved in step 1, and the output is the identified modification content. Specifically, the server analyzes the request information using a natural language processing engine (e.g., NLTK) and extracts the necessary modification content.

[1359] Step 3:

[1360] The server uses an emotion recognition engine to identify the user's emotion and urgency. The input is the request information saved in step 1, and the output is the identified emotion and urgency. Specifically, it uses a library such as TextBlob to analyze the user's input and calculates the emotion score and urgency.

[1361] Step 4:

[1362] The server generates a modification plan based on the identified modification details and emotion recognition results. The input is the modification details extracted in step 2 and the emotion scores identified in step 3, and the output is a modification plan. Specifically, it generates a specific modification plan that combines the necessary database schema changes, backend logic modifications, UI component changes, etc.

[1363] Step 5:

[1364] The server executes the system modifications based on the generated modification plan. The input is the modification plan generated in step 4, and the output is the modified system. Specifically, it executes SQL commands to modify the database structure and update the code of API endpoints and UI components.

[1365] Step 6:

[1366] The server runs automated tests to verify that the modified system operates correctly. The input is the modified system, and the output is the test results. Specifically, it checks the operation of each part and the whole system through unit tests and integration tests, and reports the test results.

[1367] Step 7:

[1368] The server generates a new system specification based on the modifications and emotion recognition results. The inputs are the modifications, emotion scores, and test results, and the output is a new system specification. Specifically, it creates a properly formatted document that describes the modifications, their rationale, and the operating status of the system after the modifications.

[1369] Step 8:

[1370] The terminal provides the generated new system specification to the user. The input is the system specification generated in step 7, and the output is the information provided to the user. Specifically, the contents of the specification are displayed on the user's interface, and a notification such as "The customer rank field has been promptly added" is given.

[1371] In this way, the system can recognize the user's emotions and level of urgency, and implement efficient system modifications based on that.

[1372] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1373] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1374] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1375] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1376] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1377] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1378] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1379] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1380] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1381] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1382] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1383] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1384] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1385] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1386] 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.

[1387] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1388] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1389] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1390] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1391] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1392] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1393] The following is further disclosed regarding the above embodiment.

[1394] (Claim 1)

[1395] A means for receiving system modification requests from users;

[1396] means for analyzing the system modification request and identifying the modification content;

[1397] means for generating a modification plan based on the modification content;

[1398] means for executing system modifications based on the generated modification plan;

[1399] A means of automatically verifying the modified system;

[1400] means for generating a new system specification based on the modification content;

[1401] means for providing said new system specification to a user;

[1402] A system including:

[1403] (Claim 2)

[1404] 2. The system according to claim 1, further comprising means for analyzing the system modification request by natural language processing.

[1405] (Claim 3)

[1406] 2. The system of claim 1, wherein executing the modification plan includes database schema modification, back-end logic modification, and user interface modification.

[1407] "Example 1"

[1408] (Claim 1)

[1409] A means for receiving system modification requests from users;

[1410] means for analyzing the system modification request by natural language processing to identify the modification content;

[1411] a means for identifying an impact range based on the modification content and generating a modification plan;

[1412] means for executing system modifications based on the generated modification plan;

[1413] Automated means of validating the modified system (including unit and integration tests);

[1414] means for generating a new system specification based on the modification content;

[1415] means for providing said new system specification to a user;

[1416] A system including:

[1417] (Claim 2)

[1418] 2. The system of claim 1, wherein the execution of the modification plan includes database structure modification, back-end logic modification, and user interface modification.

[1419] (Claim 3)

[1420] 10. The system of claim 1, further comprising: analyzing the prompt sentence using a generative artificial intelligence model to identify the modification content.

[1421] "Application Example 1"

[1422] (Claim 1)

[1423] A means for receiving system modification requests from users;

[1424] means for analyzing the system modification request and identifying the modification content;

[1425] means for generating a modification plan based on the modification content;

[1426] means for executing system modifications based on the generated modification plan;

[1427] A means of automatically verifying the modified system;

[1428] means for generating a new system specification based on the modification content;

[1429] means for providing said new system specification to a user;

[1430] a means for processing a request for modification of the logistics system, the means being installed on a robot in the logistics facility;

[1431] A system including:

[1432] (Claim 2)

[1433] 2. The system according to claim 1, further comprising means for analyzing the system modification request by natural language processing.

[1434] (Claim 3)

[1435] 2. The system of claim 1, wherein executing the modification plan includes database schema modification, back-end logic modification, and user interface modification.

[1436] "Example 2: Combining Emotion Engines"

[1437] (Claim 1)

[1438] A means for receiving system modification requests from users;

[1439] A means for analyzing the system modification request and the user's emotions to identify the modification content;

[1440] a means for generating a modification plan based on the modification content and the user's feelings;

[1441] means for executing system modifications based on the generated modification plan;

[1442] A means of automatically verifying the modified system;

[1443] A means for generating a new system specification based on the modification content and user sentiment;

[1444] means for providing said new system specification to a user;

[1445] A system including:

[1446] (Claim 2)

[1447] 2. The system according to claim 1, further comprising means for analyzing the system modification request by natural language processing.

[1448] (Claim 3)

[1449] 2. The system of claim 1, wherein executing the modification plan includes database schema modification, back-end logic modification, and user interface modification.

[1450] "Application example 2 when combining emotion engines"

[1451] (Claim 1)

[1452] A means for receiving system modification requests from users;

[1453] means for analyzing the system modification request and identifying the modification content;

[1454] means for generating a modification plan based on the modification content;

[1455] means for executing system modifications based on the generated modification plan;

[1456] A means of automatically verifying the modified system;

[1457] means for generating a new system specification based on the modification content;

[1458] means for providing said new system specification to a user;

[1459] A system that includes a means for recognizing users' emotions and urgency and generating system repair plans and support procedures based on those emotions and urgency.

[1460] (Claim 2)

[1461] 2. The system according to claim 1, further comprising means for analyzing the system modification request by natural language processing.

[1462] (Claim 3)

[1463] 2. The system of claim 1, wherein executing the modification plan includes database schema modification, back-end logic modification, and user interface modification. [Explanation of symbols]

[1464] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for receiving system modification requests from users; means for analyzing the system modification request and identifying the modification content; means for generating a modification plan based on the modification content; means for executing system modifications based on the generated modification plan; A means of automatically verifying the modified system; means for generating a new system specification based on the modification content; means for providing said new system specification to a user; A system including:

2. 2. The system according to claim 1, further comprising means for analyzing the system modification request by natural language processing.

3. 2. The system of claim 1, wherein executing the modification plan includes database schema modifications, back-end logic modifications, and user interface modifications.

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