system

Abstract

We provide the system. [Solution] A means for analyzing business requirements received from users using natural language processing and automatically generating the necessary application programming interfaces based on the analysis results, A means for selecting and configuring an appropriate authentication method for an automatically generated application programming interface, A means for automatically generating technical documentation regarding the use of the generated application programming interface, A means for automatically evaluating the security risks of the generated application programming interface and the entire system, A means of automatically generating code snippets for recommended code to users, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Recently, in the context where the preparation of a digital environment for cooperation with an AI agent is emphasized, the conventional method has problems that complicated system design and large-scale development are required, and the hurdle for introduction is high. Due to this problem, many companies are not fully enjoying the automation and efficiency improvement of business processes using AI technology. Furthermore, there is also a problem that it is difficult to quickly perform highly secure authentication settings and security evaluations.

Means for Solving the Problems

[0005] This invention provides a system that analyzes user business requirements using natural language processing and automatically generates the necessary application programming interfaces. This simplifies the complex system design process. Furthermore, it selects and configures appropriate authentication protocols for these automatically generated application programming interfaces. It also automatically generates technical documentation detailing how to use the generated APIs and provides a means for evaluating the overall system security risks, thereby ensuring safety and efficiency when introducing AI technology. It also includes a means for automatically generating and providing recommended code snippets to facilitate user development. These features enable the creation of an efficient and secure digital environment.

[0006] A "user" is defined as an entity that uses this system to input business requirements and utilizes the system's automated functions.

[0007] "Business requirements" are the specifications of the functions and processes that users desire from the system, and they form the foundational information on which the system operates.

[0008] "Natural language processing" is a technology that enables computers to understand, analyze, and extract information from human natural language, and then perform appropriate processing based on that information.

[0009] An "Application Programming Interface (API)" is a set of rules and procedures that define how different software programs can interact with each other, enabling the sending and receiving of data and the use of functions.

[0010] "Authentication method" refers to the means a system uses to verify that access comes from an appropriate user or client and to eliminate unauthorized access.

[0011] "Technical documentation" refers to documents that describe in detail how to use system-related functions, specifications, and configuration procedures, serving as a guide for users and developers to correctly utilize the system.

[0012] "Security risk" refers to threats and vulnerabilities that can occur in information systems, such as unauthorized access, data leakage, and information tampering, and is an indicator that quantifies the impact they have.

[0013] A "code snippet" is a small piece of program code that accomplishes a specific function and serves as a reference for developers when creating programs quickly. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12]It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the language used in the following description will be explained.

[0017] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention relates to a system that automatically performs API design, authentication configuration, documentation generation, security assessment, and code snippet generation based on business requirements provided by the user.

[0036] The user uses a terminal to input the business requirements they wish to implement. This input is in natural language, describing specific needs such as, "We would like to integrate AI into our inventory management system." The server receives this input information and analyzes the requirements using natural language processing technology.

[0037] Based on the analysis results, the server automatically generates API endpoints that meet the requirements. The generated APIs provide specific functions, such as checking and updating inventory, to satisfy the user's business needs. In doing so, the server automatically defines the API parameters and data format.

[0038] Furthermore, the server must select and implement an appropriate authentication method as a security measure for accessing the generated API. This is essential to guarantee secure access for users and clients.

[0039] This system also automatically generates technical documentation outlining the usage procedures and specifications of the generated APIs. Users can then appropriately utilize the APIs based on the provided technical documentation.

[0040] From a security standpoint, the server automatically assesses the overall system security risks. It can report any vulnerabilities discovered to the user and suggest corrective measures.

[0041] Finally, the server generates and provides recommended code snippets to help users easily assemble programs. This allows users to quickly develop features.

[0042] Thus, the invented system enables easy integration with AI technology, significantly streamlining the development of digital environments. Users can quickly implement necessary functions without undertaking large-scale system development. Server-based automation allows for the deployment of systems in an accurate and secure environment.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user uses a terminal to input specific business requirements in natural language. For example, they might make a request such as, "I want to set up an API to automatically update product inventory."

[0046] Step 2:

[0047] The server receives business requirements sent by the user and analyzes the request using a natural language processing engine. This analysis extracts the necessary functions and API components.

[0048] Step 3:

[0049] Based on the extracted requirements, the server translates each function into a specific API endpoint and automatically designs the endpoints. This process also configures the necessary input parameters and the format of the returned data for each endpoint.

[0050] Step 4:

[0051] The server applies an appropriate authentication protocol to the generated API endpoint. This authentication protocol, such as OAuth 2.0, is selected based on the security requirements specified by the user.

[0052] Step 5:

[0053] The server automatically generates detailed technical documentation for the designed API, clearly outlining how to use it and any important considerations. This generated documentation serves as a guideline for users to correctly utilize the API.

[0054] Step 6:

[0055] The server automatically assesses the security risks of the API and the entire system. Based on the assessment results, it identifies potential vulnerabilities and security areas that need improvement, and provides them to the user in a report.

[0056] Step 7:

[0057] The server automatically generates code snippets that help users integrate the API into their own systems and applications. This allows users to implement the necessary functionality more quickly.

[0058] (Example 1)

[0059] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0060] In modern information system development, it is necessary to respond quickly and efficiently to user business requirements. However, tasks such as designing program interfaces, configuring security, creating technical documentation, and preparing code are extremely time-consuming and labor-intensive when performed manually, and are also prone to errors. This invention aims to solve these problems and provide technology that enables the rapid and accurate implementation of functions required by users.

[0061] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0062] In this invention, the server includes means for analyzing business requests received from users using natural language processing technology and automatically generating the necessary program interfaces based on the analysis results; means for selecting and setting an appropriate identification method for the automatically generated program interfaces; and means for automatically generating technical documentation regarding the use of the generated program interfaces. This makes it possible to quickly and accurately provide functions that meet the user's business requests and to improve the efficiency of information system construction.

[0063] "Business requirements" are a natural language expression of the specific functions and performance that users require from an information system in order to accomplish a particular task.

[0064] "Natural language processing technology" is a technology that analyzes text written in human language and converts its meaning into a form that a computer can understand.

[0065] A "programming interface" is a set of rules and procedures that define how different software systems communicate with each other, and includes access points for providing functionality.

[0066] "Identification method" refers to the means or procedures used to verify the authentication and authorization of users and clients accessing the system.

[0067] "Technical documentation" refers to documents that describe the usage and specifications of a system and its components, providing information to enable users to properly utilize the system.

[0068] A "communication point" is a part of a program interface and refers to an access point for receiving requests from external sources.

[0069] "Connection privileges" refer to the right of users and clients who have undergone appropriate authentication to access a system or program interface.

[0070] This invention is a system that allows users to input specific business requests in natural language using a terminal. For example, a user inputs a request such as "I would like to integrate AI into the inventory management system" into the terminal's interface. The terminal then sends this information to the server.

[0071] The server utilizes generative AI models to analyze received input using natural language processing techniques. Through this analysis, the server identifies specific functions and objectives from user requests and automatically generates appropriate program interfaces. In doing so, the server uses a software development platform to define the communication points and data formats of the program interfaces. For example, it might set up API endpoints for managing inventory data and integrating with AI.

[0072] Next, the server automatically selects and configures an appropriate identification method for the generated program interface. Specifically, it applies authentication protocols such as OAuth or API keys to ensure that users can securely access the interface.

[0073] Furthermore, the server automatically generates technical documentation describing how to use the program interface. This documentation includes interface specifications, API usage procedures, and sample requests, helping users easily utilize the system. This enables users to quickly and appropriately build the system by referring to the provided documentation.

[0074] An example of a prompt might be, "I need an API that allows me to check the inventory status of online bookstores in real time." By entering such a prompt, the server automatically prepares a program interface that matches the user's request and provides a comprehensive solution including relevant technical information and authentication settings. This system enables users to build business systems quickly and accurately, and improve the efficiency of information processing.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The user uses a terminal to input business requests in natural language. A concrete example is writing "I would like to integrate AI into the inventory management system" in the input form. This request is sent to the server as a prompt message.

[0078] Step 2:

[0079] The server receives the prompt message and performs natural language processing using a generative AI model. The server analyzes the prompt message to identify business requests and specific functions and objectives. At this stage, text analysis is performed to extract keywords such as "inventory management" and "AI integration." The output after analysis provides data that identifies the user's objectives and functions.

[0080] Step 3:

[0081] The server automatically designs the program interface based on the analysis results. Specifically, it defines API endpoints based on the extracted functions and sets the necessary parameters and data formats. In this process, it uses an information system construction platform to generate an endpoint such as "GET / api / inventory". The output is a specification definition.

[0082] Step 4:

[0083] The server configures an identification method for the generated program interface to ensure secure user access. It automatically sets up authentication protocols by applying OAuth or API keys. At this stage, authentication keys are generated and applied. The applied authentication configuration information is obtained as output.

[0084] Step 5:

[0085] To ensure users can properly utilize the interface, the server automatically generates technical documentation detailing how to use the program interface. This documentation includes endpoint descriptions and sample code. The output is a technical document for the user.

[0086] Step 6:

[0087] Finally, the server automatically assesses the security of the entire system and the security risks associated with the generated interfaces. Based on this assessment, it reports any vulnerabilities found and suggested mitigation measures to the user. A security report is then generated as output.

[0088] (Application Example 1)

[0089] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0090] Traditional inventory management and tracking systems at logistics centers have faced challenges in efficient operation due to difficulties in integrating with a wide variety of software platforms. Furthermore, the complexity of designing application interfaces and the cumbersome setup of authentication and security measures have been significant issues. In such environments, there is a need for overall system automation and improved security.

[0091] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0092] In this invention, the server includes means for analyzing information processing requirements received from a user using natural language processing and automatically generating the necessary application interface based on the analysis results; means for generating application interfaces for machinery and equipment deployed at logistics processing centers to cooperate with inventory management and tracking systems; and means for selecting and setting an appropriate authentication method for the automatically generated application interface. This enables efficient automation of complex logistics management operations while allowing smooth cooperation between different systems without compromising security.

[0093] "Information processing requirements" refer to the specific functions and system operation requirements that users need to perform their tasks.

[0094] "Natural language processing" is a technology that enables computers to understand and process natural human language, and is a means of extracting useful information from text data.

[0095] An "application interface" is a means of communication used to exchange functions and information between different software programs.

[0096] A "logistics processing center" is a facility that handles a series of logistics-related operations, such as receiving, storing, sorting, and shipping goods.

[0097] "Machinery and equipment" refers to a series of technological devices used to automate tasks and perform operations efficiently.

[0098] An "inventory management and tracking system" is a system that monitors the quantity and location of goods in storage and optimizes the logistics process.

[0099] "Authentication methods" refer to a set of processes and techniques used to legitimize access to a system.

[0100] An "application interface communication connection point" refers to a point of connection where different systems or programs exchange data with each other.

[0101] "Authentication rules" are procedures and rules used to verify access rights and identify users in digital systems.

[0102] To implement this invention, an information processing system including a server is required. The server receives information processing requirements provided by the user through a terminal and performs natural language processing. For this processing, spaCy, a highly accurate NLP library, is used as the natural language processing technology. Based on the results of the processing, the necessary application interfaces are designed and automatically generated. In this process, Python programs and the Flask framework are utilized to establish flexible API communication connection points.

[0103] The server then generates APIs that enable machinery and equipment deployed at logistics processing centers to interact with inventory management and tracking systems. In this process, authentication protocols to ensure security are automated using the OAuth library.

[0104] To maintain the security of the entire system, the server incorporates a process that performs security risk assessments on the generated application interfaces and automatically generates technical documentation. This allows users to smoothly utilize the API while referring to the provided technical documentation. In addition, short programs are automatically generated to enable users to proceed with development quickly.

[0105] As a concrete example, let's assume a small robot at a logistics center performs the task of "checking and updating the inventory status of product code 123." In this case, by entering the prompt message, "Please generate an API to link with the inventory management system and check the inventory of product code 123 in real time," the system will generate and operate the appropriate API. This will streamline the inventory check work within the logistics center.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The user enters business requirements into the terminal in natural language. The terminal sends the entered information to the server. The input for this process is a prompt from the user, such as "Please generate an API that will connect with the inventory management system and allow real-time inventory checks for product code 123," and the output is the requirement information received by the server. This requirement is sent to the server as the data that forms the basis for API generation.

[0109] Step 2:

[0110] The server inputs the received requirements information into a natural language processing engine (e.g., spaCy) and analyzes the information. In this process, the server breaks down the input natural language and extracts the functional requirements as structured data. The output consists of the specific functions and parameters included in the requirements. Specifically, the process involves breaking down the text into morphemes, analyzing the meaning of each word, and mapping them to functions.

[0111] Step 3:

[0112] The server automatically generates the necessary application interfaces based on the analysis results. The input is functional requirement data, and the output is the automatically generated API endpoints and specifications. The server uses the Flask framework to program and configure the API endpoints in Python. In this processing step, the APIs for the functions requested from the terminal are generated and immediately available.

[0113] Step 4:

[0114] For the generated application interface, the server automatically configures appropriate authentication using an authentication protocol (e.g., OAuth). The input is the generated API specification, and the output is a secure API with built-in authentication. The server then performs specific operations to add code that manages access permissions to ensure secure user access.

[0115] Step 5:

[0116] The server automatically generates technical documentation for using the generated API. The input is the endpoint and its specifications, and the output is documentation outlining the steps a user takes to use the API. This includes available endpoints, parameters, authentication information, etc. This step provides documented information to improve user convenience.

[0117] Step 6:

[0118] The server automatically generates and provides code snippets for use at logistics centers. Input is technical documentation and API endpoint information, while output is code snippets for specific implementations. Using a generation AI model, the server suggests efficient program code to help expedite work.

[0119] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0120] This invention relates to a system that enables the efficient development of digital environments using AI technology, and in particular aims to improve the user experience by incorporating an emotion engine.

[0121] Users input business requirements into the system using a terminal in natural language. At the same time, the system captures the user's emotional state from their speech and writing. For example, suppose a user inputs the requirement, "I want to resolve this inventory management delay." The server uses an emotion engine to recognize the emotion of "anxiety" from this input.

[0122] The server analyzes the received business requirements and simultaneously combines them with emotional data extracted via the emotion engine, reflecting the results in the analysis. By flexibly adjusting the analysis results according to the emotional data, more personalized solutions are provided.

[0123] Next, an API endpoint is automatically generated based on the analysis results. The authentication protocol settings used here are also adjusted as appropriate according to the user's emotional state. For example, if the user is in an unstable emotional state, a more robust authentication procedure may be adopted.

[0124] Furthermore, the server generates technical documentation on the use of the generated APIs. The tone and content of the technical documentation are optimized according to the emotions identified by the emotion engine and are provided in a user-friendly format.

[0125] In addition, recommended code snippets are automatically generated to help users develop more efficiently. These snippets are also structured based on user sentiment data and designed to reduce user stress.

[0126] Thus, by incorporating an emotion engine, the present invention provides a superior user experience and efficiency compared to conventional digital environment development systems. In particular, by incorporating emotional data into the analysis process, a more user-friendly system that meets user needs can be constructed.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] The user enters their business requirements into the terminal. Here, requests in natural language, such as "I want to integrate the inventory management system with AI immediately," are entered.

[0130] Step 2:

[0131] The device sends user input to a text analysis module, and simultaneously analyzes the user's emotions with an emotion recognition module. In this case, the expression "right now" is used to identify the emotions of "urgency" and "anxiety."

[0132] Step 3:

[0133] The server receives business requirements and sentiment information sent from the terminal and analyzes them using a natural language processing engine. This analysis extracts specific API functions related to "inventory management" and "AI integration."

[0134] Step 4:

[0135] The server automatically designs the necessary API endpoints based on the extracted functions. The design process proceeds with priority given to "urgency."

[0136] Step 5:

[0137] The server configures the authentication protocol for the generated API. Because the user is feeling "anxious," a simplified authentication procedure is selected to enable quick access.

[0138] Step 6:

[0139] The server automatically generates technical documentation to support the use of the generated API. This documentation is designed to be emotionally resonant, clear, and concise, ensuring that users can quickly understand it.

[0140] Step 7:

[0141] The server automatically generates recommended code snippets to help users utilize the system. Considering the user's "urgency," immediately usable sample code is provided.

[0142] Step 8:

[0143] The device presents the generated APIs, technical documentation, and code snippets to the user, establishing an immediately usable interaction.

[0144] This processing flow allows users to set up a system environment that enables them to efficiently solve problems.

[0145] (Example 2)

[0146] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0147] Modern information processing systems suffer from a decline in user experience quality because automatically generated information processing interfaces, based on user input, are provided without considering the user's emotional state. Furthermore, the generated interfaces and technical documentation are not optimized to meet user needs and emotions, resulting in a lack of convenience and efficiency. In addition, while rapid response is required regarding security, traditional methods have limitations.

[0148] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0149] In this invention, the server includes means for recognizing the user's emotional state and integrating emotional data into the analysis results to personalize the solution; means for selecting and setting an appropriate authentication method for the automatically generated information processing interface; and means for automatically generating technical documentation regarding the use of the generated information processing interface and optimizing it to a tone that corresponds to the user's emotions. This makes it possible to provide an interface that takes the user's emotions into consideration, improving the user experience and achieving highly secure information processing.

[0150] A "user" refers to a person who inputs business requirements into a system and receives information processing services.

[0151] "Natural language processing technology" refers to the technology used by computers to understand, analyze, and generate natural human language.

[0152] "Analysis results" refers to the output information after analyzing the received business requirements using natural language processing.

[0153] An "information processing interface" refers to a software structure that functions as a medium for different software components to exchange information.

[0154] "Emotional state" refers to the psychological and emotional characteristics recognized from the user's input.

[0155] "Emotional data" refers to information about emotions extracted from user utterances and input.

[0156] "Personalization" refers to adjusting responses and services to suit the specific needs and circumstances of a particular user.

[0157] "Authentication method" refers to the process or technology used to verify a user's identity and grant them permissions.

[0158] "Technical documentation" refers to a document that describes in detail the usage and characteristics of the generated information processing interface.

[0159] "Security risk" refers to the possibility that the security of a system may be threatened by unauthorized access, leakage, or tampering of information.

[0160] A "recommended program snippet" refers to a short code example that demonstrates the recommended way of writing code to efficiently perform a particular task.

[0161] This invention is based on an information processing system, analyzes the user's business requirements using natural language processing technology, and improves the user experience by recognizing the user's emotional state using an emotion engine. The following are specific embodiments for carrying out the present invention.

[0162] The server is equipped with high-performance data processing hardware and software that implements natural language processing algorithms. This server receives business requirements from users and analyzes the input text data. This analysis is intended to identify the problems the user is facing. In particular, it configures the necessary connection points and authentication methods based on the analysis results to enable the automatic generation of information processing interfaces.

[0163] In addition, the server uses an emotion engine to extract emotional data from user input. This data is integrated with the analysis results and used to provide personalized solutions tailored to each individual user. The user's emotional state is also taken into consideration when creating technical documentation that shows how to use the generated interface.

[0164] For example, if a user enters "I want to resolve this inventory management delay," the server analyzes the business requirements, and at the same time, its emotion engine detects the emotion of "urgency." Based on this information, a rapid response with a sense of urgency is suggested, and the authentication protocol used may also be strengthened.

[0165] Furthermore, users can receive automatically generated recommended program snippets, which are presented in a format tailored to their current emotional state. This allows users to work more efficiently.

[0166] An example of a prompt might be the text, "Please provide a template that utilizes the latest machine learning models." In response, the server provides the relevant information processing interface and, if necessary, provides appropriate technical documentation and program snippets. This system allows users to receive support that takes their emotional state into consideration and to perform their tasks with high efficiency.

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] Users input business requirements into this system using a terminal in natural language. This input is sent to the server. The server receives this input and analyzes the business requirements as text data using natural language processing technology. The output of this analysis is information about the specific problems and actions that the user seeks to solve.

[0170] Step 2:

[0171] The server applies an emotion engine to further process the analyzed business requirements. It extracts emotional states from the user's input text and integrates this emotion data with the analysis results of the business requirements. Specifically, it analyzes keywords and phrases in the text and recognizes emotions such as "anxiety" and "interest." The output of this process is a more detailed and personalized business requirement that includes emotion data.

[0172] Step 3:

[0173] The server automatically generates an appropriate information processing interface based on business requirements that integrate emotional data. This process determines which APIs are needed and which authentication protocols to use. Specifically, it implements API endpoints tailored to the business requirements and authentication settings to manage access permissions. The output is the automatically generated information processing interface.

[0174] Step 4:

[0175] The server automatically generates technical documentation regarding the use of the generated information processing interface. This includes a description of the interface's functions and usage procedures. Furthermore, the tone and content of the documentation are optimized, taking into account the user's emotional state. The output is technical documentation provided in a user-friendly format.

[0176] Step 5:

[0177] The server generates recommended program snippets to assist in the use of the information processing interface. These program snippets are optimized to efficiently meet business requirements and are constructed based on user sentiment data. At this stage, a completed program snippet is output as a response to user input, and the user can use it for further development.

[0178] (Application Example 2)

[0179] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0180] In today's digital environment, there is a need for systems that can respond quickly and accurately to users' business requirements. However, existing systems do not adequately provide personalized responses that take into account the user's emotional state. As a result, the user experience is degraded, and business efficiency is hindered. It is necessary to improve this and create a more user-friendly and less stressful interaction.

[0181] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0182] In this invention, the server includes means for analyzing business requirements received from the user using natural language processing and automatically generating the necessary application programming interface based on the analysis results; means for acquiring emotional data in real time from the customer's facial expressions and speech and generating a personalized response to the user's business requirements based on that data; and means for automatically evaluating the security risks of the generated application programming interface and the entire system. This enables flexible responses that take into account the user's emotional state, resulting in a better user experience and improved operational efficiency.

[0183] "Business requirements" are specific requirements related to the objectives that the user wants to achieve or the problems they want to solve.

[0184] "Natural language processing" is a technology that enables computers to understand and analyze the language that humans use in everyday life.

[0185] An "application programming interface" is a set of definitions and procedures that enable different software components to communicate with one another.

[0186] "Emotional data" refers to information that indicates an individual's psychological state, analyzed from nonverbal cues such as facial expressions and voice.

[0187] A "personalized response" is a reply or action that is customized according to the preferences and circumstances of a particular individual.

[0188] "Security risk" refers to potential threats and vulnerabilities related to information systems and data.

[0189] A "recommended code snippet" is a sample code snippet provided to help users solve a specific technical problem.

[0190] An "authentication protocol" is a set of procedures and rules that ensure only authorized users can access a system or data.

[0191] To implement this invention, users must access the system using a dedicated terminal and input their business requirements in natural language format. The server analyzes this input using a natural language processing engine to understand the business requirements. For analysis, machine learning libraries such as TENSORFLOW® and PyTorch are used to enable advanced natural language processing. In addition, Google® Cloud Vision API and Azure® Text Analytics are used to obtain user sentiment data from the input text and utterances. This identifies the user's current emotional state.

[0192] Next, the server automatically generates appropriate application programming interfaces (APIs) based on the analysis results and sentiment data. This process defines API endpoints specific to business requirements and documents their usage in technical documentation. The tone and content of this technical documentation are also customized according to the user's sentiment data. This feature allows users to utilize the system more intuitively.

[0193] Furthermore, the server generates recommended code snippets for the user, helping them quickly resolve specific technical challenges they face. These code snippets are also designed to take the user's current emotional state into consideration and reduce stress. For example, if a user is anxious about complex system configurations, the server will generate simple, step-by-step code snippets.

[0194] For example, if a customer has questions about a service at a physical store, such as "I don't know how to use this product," a store employee wearing smart glasses can instantly understand their feelings and provide a customized response to reassure them. Such a system enables seamless interaction between humans and technology in physical stores, and is expected to realize a high level of user experience.

[0195] An example of a prompt message would be: "During interactions with customers, recognize their emotions (e.g., anxiety, joy) in real time and display direct customer service advice on the employee's smart glasses."

[0196] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0197] Step 1:

[0198] The user inputs business requirements in natural language using a terminal. This input is obtained as text data in natural language format. This data is then passed by the server to the next process, natural language analysis. The user's emotional state, corresponding to their speech and input, is also recorded simultaneously.

[0199] Step 2:

[0200] The server parses the natural language text data received from the user using a natural language processing engine (e.g., TensorFlow or PyTorch). This process extracts specific intentions and requirements of the business requirements. The output is the parsed business requirements and their corresponding data structures. Furthermore, a sentiment engine is used to extract user sentiment data from the input data.

[0201] Step 3:

[0202] The server automatically generates application programming interface (API) endpoints using a generative AI model based on the analyzed business requirements and sentiment data. At this stage, the necessary API structure is identified based on the extracted functions. As output, specific API endpoint definitions are generated, and related technical documentation is created.

[0203] Step 4:

[0204] The server generates recommended code snippets, taking into account the user's emotional state. Using emotional data and analyzed business requirements as input, the output includes code snippets designed to alleviate user emotions and improve work efficiency.

[0205] Step 5:

[0206] Users receive APIs and code snippets generated through their devices, allowing them to intuitively and effectively interact with the system. Based on sentiment data collected throughout the system, the user experience is delivered in a personalized manner, enabling responses that are better suited to the user's needs.

[0207] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0208] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0209] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0210] [Second Embodiment]

[0211] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0212] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0213] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0214] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0215] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0216] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0217] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0218] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0219] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0220] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0221] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0222] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0223] This invention relates to a system that automatically performs API design, authentication configuration, documentation generation, security assessment, and code snippet generation based on business requirements provided by the user.

[0224] The user uses a terminal to input the business requirements they wish to implement. This input is in natural language, describing specific needs such as, "We would like to integrate AI into our inventory management system." The server receives this input information and analyzes the requirements using natural language processing technology.

[0225] Based on the analysis results, the server automatically generates API endpoints that meet the requirements. The generated APIs provide specific functions, such as checking and updating inventory, to satisfy the user's business needs. In doing so, the server automatically defines the API parameters and data format.

[0226] Furthermore, the server must select and implement an appropriate authentication method as a security measure for accessing the generated API. This is essential to guarantee secure access for users and clients.

[0227] This system also automatically generates technical documentation outlining the usage procedures and specifications of the generated APIs. Users can then appropriately utilize the APIs based on the provided technical documentation.

[0228] From a security standpoint, the server automatically assesses the overall system security risks. It can report any vulnerabilities discovered to the user and suggest corrective measures.

[0229] Finally, the server generates and provides recommended code snippets to help users easily assemble programs. This allows users to quickly develop features.

[0230] Thus, the invented system enables easy integration with AI technology, significantly streamlining the development of digital environments. Users can quickly implement necessary functions without undertaking large-scale system development. Server-based automation allows for the deployment of systems in an accurate and secure environment.

[0231] The following describes the processing flow.

[0232] Step 1:

[0233] The user uses a terminal to input specific business requirements in natural language. For example, they might make a request such as, "I want to set up an API to automatically update product inventory."

[0234] Step 2:

[0235] The server receives business requirements sent by the user and analyzes the request using a natural language processing engine. This analysis extracts the necessary functions and API components.

[0236] Step 3:

[0237] Based on the extracted requirements, the server translates each function into a specific API endpoint and automatically designs the endpoints. This process also configures the necessary input parameters and the format of the returned data for each endpoint.

[0238] Step 4:

[0239] The server applies an appropriate authentication protocol to the generated API endpoint. This authentication protocol, such as OAuth 2.0, is selected based on the security requirements specified by the user.

[0240] Step 5:

[0241] The server automatically generates detailed technical documentation for the designed API, clearly outlining how to use it and any important considerations. This generated documentation serves as a guideline for users to correctly utilize the API.

[0242] Step 6:

[0243] The server automatically assesses the security risks of the API and the entire system. Based on the assessment results, it identifies potential vulnerabilities and security areas that need improvement, and provides them to the user in a report.

[0244] Step 7:

[0245] The server automatically generates code snippets that help users integrate the API into their own systems and applications. This allows users to implement the necessary functionality more quickly.

[0246] (Example 1)

[0247] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0248] In modern information system development, it is necessary to respond quickly and efficiently to user business requirements. However, tasks such as designing program interfaces, configuring security, creating technical documentation, and preparing code are extremely time-consuming and labor-intensive when performed manually, and are also prone to errors. This invention aims to solve these problems and provide technology that enables the rapid and accurate implementation of functions required by users.

[0249] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0250] In this invention, the server includes means for analyzing business requests received from users using natural language processing technology and automatically generating the necessary program interfaces based on the analysis results; means for selecting and setting an appropriate identification method for the automatically generated program interfaces; and means for automatically generating technical documentation regarding the use of the generated program interfaces. This makes it possible to quickly and accurately provide functions that meet the user's business requests and to improve the efficiency of information system construction.

[0251] "Business requirements" are a natural language expression of the specific functions and performance that users require from an information system in order to accomplish a particular task.

[0252] "Natural language processing technology" is a technology that analyzes text written in human language and converts its meaning into a form that a computer can understand.

[0253] A "programming interface" is a set of rules and procedures that define how different software systems communicate with each other, and includes access points for providing functionality.

[0254] "Identification method" refers to the means or procedures used to verify the authentication and authorization of users and clients accessing the system.

[0255] "Technical documentation" refers to documents that describe the usage and specifications of a system and its components, providing information to enable users to properly utilize the system.

[0256] A "communication point" is a part of a program interface and refers to an access point for receiving requests from external sources.

[0257] "Connection privileges" refer to the right of users and clients who have undergone appropriate authentication to access a system or program interface.

[0258] This invention is a system that allows users to input specific business requests in natural language using a terminal. For example, a user inputs a request such as "I would like to integrate AI into the inventory management system" into the terminal's interface. The terminal then sends this information to the server.

[0259] The server utilizes generative AI models to analyze received input using natural language processing techniques. Through this analysis, the server identifies specific functions and objectives from user requests and automatically generates appropriate program interfaces. In doing so, the server uses a software development platform to define the communication points and data formats of the program interfaces. For example, it might set up API endpoints for managing inventory data and integrating with AI.

[0260] Next, the server automatically selects and configures an appropriate identification method for the generated program interface. Specifically, it applies authentication protocols such as OAuth or API keys to ensure that users can securely access the interface.

[0261] Furthermore, the server automatically generates technical documentation describing how to use the program interface. This documentation includes interface specifications, API usage procedures, and sample requests, helping users easily utilize the system. This enables users to quickly and appropriately build the system by referring to the provided documentation.

[0262] An example of a prompt might be, "I need an API that allows me to check the inventory status of online bookstores in real time." By entering such a prompt, the server automatically prepares a program interface that matches the user's request and provides a comprehensive solution including relevant technical information and authentication settings. This system enables users to build business systems quickly and accurately, and improve the efficiency of information processing.

[0263] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0264] Step 1:

[0265] The user uses a terminal to input business requests in natural language. A concrete example is writing "I would like to integrate AI into the inventory management system" in the input form. This request is sent to the server as a prompt message.

[0266] Step 2:

[0267] The server receives the prompt message and performs natural language processing using a generative AI model. The server analyzes the prompt message to identify business requests and specific functions and objectives. At this stage, text analysis is performed to extract keywords such as "inventory management" and "AI integration." The output after analysis provides data that identifies the user's objectives and functions.

[0268] Step 3:

[0269] The server automatically designs the program interface based on the analysis results. Specifically, it defines API endpoints based on the extracted functions and sets the necessary parameters and data formats. In this process, it uses an information system construction platform to generate an endpoint such as "GET / api / inventory". The output is a specification definition.

[0270] Step 4:

[0271] The server configures an identification method for the generated program interface to ensure secure user access. It automatically sets up authentication protocols by applying OAuth or API keys. At this stage, authentication keys are generated and applied. The applied authentication configuration information is obtained as output.

[0272] Step 5:

[0273] To ensure users can properly utilize the interface, the server automatically generates technical documentation detailing how to use the program interface. This documentation includes endpoint descriptions and sample code. The output is a technical document for the user.

[0274] Step 6:

[0275] Finally, the server automatically assesses the security of the entire system and the security risks associated with the generated interfaces. Based on this assessment, it reports any vulnerabilities found and suggested mitigation measures to the user. A security report is then generated as output.

[0276] (Application Example 1)

[0277] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0278] Traditional inventory management and tracking systems at logistics centers have faced challenges in efficient operation due to difficulties in integrating with a wide variety of software platforms. Furthermore, the complexity of designing application interfaces and the cumbersome setup of authentication and security measures have been significant issues. In such environments, there is a need for overall system automation and improved security.

[0279] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0280] In this invention, the server includes means for analyzing information processing requirements received from a user using natural language processing and automatically generating the necessary application interface based on the analysis results; means for generating application interfaces for machinery and equipment deployed at logistics processing centers to cooperate with inventory management and tracking systems; and means for selecting and setting an appropriate authentication method for the automatically generated application interface. This enables efficient automation of complex logistics management operations while allowing smooth cooperation between different systems without compromising security.

[0281] "Information processing requirements" refer to the specific functions and system operation requirements that users need to perform their tasks.

[0282] "Natural language analysis" refers to the technology by which a computer understands and processes human natural language, and is a means for extracting useful information from text data.

[0283] "Application interface" refers to the communication means for exchanging functions and information between different software.

[0284] "Logistics processing base" refers to a facility that conducts a series of logistics-related operations such as receiving, storing, sorting, and shipping goods.

[0285] "Machinery and equipment" refers to a series of technical devices used to automate operations and conduct business efficiently.

[0286] "Inventory management and tracking system" refers to a system that monitors the quantity and location of goods in storage and optimizes the logistics process.

[0287] "Authentication method" refers to a series of processes and technologies used to justify access to a system.

[0288] "Communication connection point of application interface" refers to the connection point where different systems or programs exchange data with each other.

[0289] "Authentication convention" refers to the procedures and rules for verifying access rights and identifying users in a digital system.

[0290] To implement this invention, an information processing system including a server is required. The server receives the information processing requirements provided by the user through the terminal and conducts natural language analysis. For this analysis, spaCy, a high-precision NLP library as a natural language processing technology, is used. Based on the analyzed results, the design and automatic generation of the required application interface are conducted. In this process, a flexible API communication connection point is set by utilizing the Python program and Flask framework.

[0291] The server then generates APIs that enable machinery and equipment deployed at logistics processing centers to interact with inventory management and tracking systems. In this process, authentication protocols to ensure security are automated using the OAuth library.

[0292] To maintain the security of the entire system, the server incorporates a process that performs security risk assessments on the generated application interfaces and automatically generates technical documentation. This allows users to smoothly utilize the API while referring to the provided technical documentation. In addition, short programs are automatically generated to enable users to proceed with development quickly.

[0293] As a concrete example, let's assume a small robot at a logistics center performs the task of "checking and updating the inventory status of product code 123." In this case, by entering the prompt message, "Please generate an API to link with the inventory management system and check the inventory of product code 123 in real time," the system will generate and operate the appropriate API. This will streamline the inventory check work within the logistics center.

[0294] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0295] Step 1:

[0296] The user enters business requirements into the terminal in natural language. The terminal sends the entered information to the server. The input for this process is a prompt from the user, such as "Please generate an API that will connect with the inventory management system and allow real-time inventory checks for product code 123," and the output is the requirement information received by the server. This requirement is sent to the server as the data that forms the basis for API generation.

[0297] Step 2:

[0298] The server inputs the received requirements information into a natural language processing engine (e.g., spaCy) and analyzes the information. In this process, the server breaks down the input natural language and extracts the functional requirements as structured data. The output consists of the specific functions and parameters included in the requirements. Specifically, the process involves breaking down the text into morphemes, analyzing the meaning of each word, and mapping them to functions.

[0299] Step 3:

[0300] The server automatically generates the necessary application interfaces based on the analysis results. The input is functional requirement data, and the output is the automatically generated API endpoints and specifications. The server uses the Flask framework to program and configure the API endpoints in Python. In this processing step, the APIs for the functions requested from the terminal are generated and immediately available.

[0301] Step 4:

[0302] For the generated application interface, the server automatically configures appropriate authentication using an authentication protocol (e.g., OAuth). The input is the generated API specification, and the output is a secure API with built-in authentication. The server then performs specific operations to add code that manages access permissions to ensure secure user access.

[0303] Step 5:

[0304] The server automatically generates technical documentation for using the generated API. The input is the endpoint and its specifications, and the output is documentation outlining the steps a user takes to use the API. This includes available endpoints, parameters, authentication information, etc. This step provides documented information to improve user convenience.

[0305] Step 6:

[0306] The server automatically generates code snippets for use at logistics bases and provides them to users. The input is technical documents and API endpoint information, and the output is code snippets for specific implementations. The server uses a generative AI model to propose efficient program code and assist in speeding up the work.

[0307] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion identification model 59 and perform specific processing using the user's emotions.

[0308] The present invention relates to a system that enables efficient maintenance of a digital environment using AI technology, and particularly aims to improve the user experience by incorporating an emotion engine.

[0309] The user uses a terminal to input business requirements to this system in natural language. At this time, the emotional state is also captured simultaneously from the user's speech and text. For example, suppose the user inputs a requirement such as "I want to solve the delay in this inventory management." The server uses the emotion engine to recognize the emotion of "frustration" from this.

[0310] The server analyzes the received business requirements and combines the emotion data extracted via the emotion engine, reflecting it in the analysis results. By flexibly adjusting the analysis results according to the emotion data, a more personalized solution is provided.

[0311] Subsequently, API endpoints are automatically generated based on the analysis results. The settings of the authentication protocol used here are also appropriately adjusted according to the user's emotional state. For example, when the user is in an unstable emotional state, a more robust authentication procedure can also be adopted.

[0312] Furthermore, the server creates a technical document regarding the use of the generated API. The tone and content of the technical document are optimized according to the emotions identified by the emotion engine and provided in a form that is easy for the user to understand.

[0313] In addition, recommended code snippets are automatically generated to help users develop more efficiently. These snippets are also structured based on user sentiment data and designed to reduce user stress.

[0314] Thus, by incorporating an emotion engine, the present invention provides a superior user experience and efficiency compared to conventional digital environment development systems. In particular, by incorporating emotional data into the analysis process, a more user-friendly system that meets user needs can be constructed.

[0315] The following describes the processing flow.

[0316] Step 1:

[0317] The user enters their business requirements into the terminal. Here, requests in natural language, such as "I want to integrate the inventory management system with AI immediately," are entered.

[0318] Step 2:

[0319] The device sends user input to a text analysis module, and simultaneously analyzes the user's emotions with an emotion recognition module. In this case, the expression "right now" is used to identify the emotions of "urgency" and "anxiety."

[0320] Step 3:

[0321] The server receives business requirements and sentiment information sent from the terminal and analyzes them using a natural language processing engine. This analysis extracts specific API functions related to "inventory management" and "AI integration."

[0322] Step 4:

[0323] The server automatically designs the necessary API endpoints based on the extracted functions. The design process proceeds with priority given to "urgency."

[0324] Step 5:

[0325] The server configures the authentication protocol for the generated API. Because the user is feeling "anxious," a simplified authentication procedure is selected to enable quick access.

[0326] Step 6:

[0327] The server automatically generates technical documentation to support the use of the generated API. This documentation is designed to be emotionally resonant, clear, and concise, ensuring that users can quickly understand it.

[0328] Step 7:

[0329] The server automatically generates recommended code snippets to help users utilize the system. Considering the user's "urgency," immediately usable sample code is provided.

[0330] Step 8:

[0331] The device presents the generated APIs, technical documentation, and code snippets to the user, establishing an immediately usable interaction.

[0332] This processing flow allows users to set up a system environment that enables them to efficiently solve problems.

[0333] (Example 2)

[0334] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0335] Modern information processing systems suffer from a decline in user experience quality because automatically generated information processing interfaces, based on user input, are provided without considering the user's emotional state. Furthermore, the generated interfaces and technical documentation are not optimized to meet user needs and emotions, resulting in a lack of convenience and efficiency. In addition, while rapid response is required regarding security, traditional methods have limitations.

[0336] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0337] In this invention, the server includes means for recognizing the user's emotional state and integrating emotional data into the analysis results to personalize the solution; means for selecting and setting an appropriate authentication method for the automatically generated information processing interface; and means for automatically generating technical documentation regarding the use of the generated information processing interface and optimizing it to a tone that corresponds to the user's emotions. This makes it possible to provide an interface that takes the user's emotions into consideration, improving the user experience and achieving highly secure information processing.

[0338] A "user" refers to a person who inputs business requirements into a system and receives information processing services.

[0339] "Natural language processing technology" refers to the technology used by computers to understand, analyze, and generate natural human language.

[0340] "Analysis results" refers to the output information after analyzing the received business requirements using natural language processing.

[0341] An "information processing interface" refers to a software structure that functions as a medium for different software components to exchange information.

[0342] "Emotional state" refers to the psychological and emotional characteristics recognized from the user's input.

[0343] "Emotional data" refers to information about emotions extracted from user utterances and input.

[0344] "Personalization" refers to adjusting responses and services to suit the specific needs and circumstances of a particular user.

[0345] "Authentication method" refers to the process or technology used to verify a user's identity and grant them permissions.

[0346] "Technical documentation" refers to a document that describes in detail the usage and characteristics of the generated information processing interface.

[0347] "Security risk" refers to the possibility that the security of a system may be threatened by unauthorized access, leakage, or tampering of information.

[0348] A "recommended program snippet" refers to a short code example that demonstrates the recommended way of writing code to efficiently perform a particular task.

[0349] This invention is based on an information processing system, analyzes the user's business requirements using natural language processing technology, and improves the user experience by recognizing the user's emotional state using an emotion engine. The following are specific embodiments for carrying out the present invention.

[0350] The server is equipped with high-performance data processing hardware and software that implements natural language processing algorithms. This server receives business requirements from users and analyzes the input text data. This analysis is intended to identify the problems the user is facing. In particular, it configures the necessary connection points and authentication methods based on the analysis results to enable the automatic generation of information processing interfaces.

[0351] In addition, the server uses an emotion engine to extract emotional data from user input. This data is integrated with the analysis results and used to provide personalized solutions tailored to each individual user. The user's emotional state is also taken into consideration when creating technical documentation that shows how to use the generated interface.

[0352] For example, if a user enters "I want to resolve this inventory management delay," the server analyzes the business requirements, and at the same time, its emotion engine detects the emotion of "urgency." Based on this information, a rapid response with a sense of urgency is suggested, and the authentication protocol used may also be strengthened.

[0353] Furthermore, users can receive automatically generated recommended program snippets, which are presented in a format tailored to their current emotional state. This allows users to work more efficiently.

[0354] An example of a prompt might be the text, "Please provide a template that utilizes the latest machine learning models." In response, the server provides the relevant information processing interface and, if necessary, provides appropriate technical documentation and program snippets. This system allows users to receive support that takes their emotional state into consideration and to perform their tasks with high efficiency.

[0355] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0356] Step 1:

[0357] Users input business requirements into this system using a terminal in natural language. This input is sent to the server. The server receives this input and analyzes the business requirements as text data using natural language processing technology. The output of this analysis is information about the specific problems and actions that the user seeks to solve.

[0358] Step 2:

[0359] The server applies an emotion engine to further process the analyzed business requirements. It extracts emotional states from the user's input text and integrates this emotion data with the analysis results of the business requirements. Specifically, it analyzes keywords and phrases in the text and recognizes emotions such as "anxiety" and "interest." The output of this process is a more detailed and personalized business requirement that includes emotion data.

[0360] Step 3:

[0361] The server automatically generates an appropriate information processing interface based on business requirements that integrate emotional data. This process determines which APIs are needed and which authentication protocols to use. Specifically, it implements API endpoints tailored to the business requirements and authentication settings to manage access permissions. The output is the automatically generated information processing interface.

[0362] Step 4:

[0363] The server automatically generates technical documentation regarding the use of the generated information processing interface. This includes a description of the interface's functions and usage procedures. Furthermore, the tone and content of the documentation are optimized, taking into account the user's emotional state. The output is technical documentation provided in a user-friendly format.

[0364] Step 5:

[0365] The server generates recommended program snippets to assist in the use of the information processing interface. These program snippets are optimized to efficiently meet business requirements and are constructed based on user sentiment data. At this stage, a completed program snippet is output as a response to user input, and the user can use it for further development.

[0366] (Application Example 2)

[0367] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0368] In today's digital environment, there is a need for systems that can respond quickly and accurately to users' business requirements. However, existing systems do not adequately provide personalized responses that take into account the user's emotional state. As a result, the user experience is degraded, and business efficiency is hindered. It is necessary to improve this and create a more user-friendly and less stressful interaction.

[0369] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0370] In this invention, the server includes means for analyzing business requirements received from the user using natural language processing and automatically generating the necessary application programming interface based on the analysis results; means for acquiring emotional data in real time from the customer's facial expressions and speech and generating a personalized response to the user's business requirements based on that data; and means for automatically evaluating the security risks of the generated application programming interface and the entire system. This enables flexible responses that take into account the user's emotional state, resulting in a better user experience and improved operational efficiency.

[0371] "Business requirements" are specific requirements related to the objectives that the user wants to achieve or the problems they want to solve.

[0372] "Natural language processing" is a technology that enables computers to understand and analyze the language that humans use in everyday life.

[0373] An "application programming interface" is a set of definitions and procedures that enable different software components to communicate with one another.

[0374] "Emotional data" refers to information that indicates an individual's psychological state, analyzed from nonverbal cues such as facial expressions and voice.

[0375] A "personalized response" is a reply or action that is customized according to the preferences and circumstances of a particular individual.

[0376] "Security risk" refers to potential threats and vulnerabilities related to information systems and data.

[0377] A "recommended code snippet" is a sample code snippet provided to help users solve a specific technical problem.

[0378] An "authentication protocol" is a set of procedures and rules that ensure only authorized users can access a system or data.

[0379] To implement this invention, users must access the system using a dedicated terminal and input their business requirements in natural language format. The server analyzes this input using a natural language processing engine to understand the business requirements. For analysis, advanced natural language processing is enabled, and machine learning libraries such as TensorFlow and PyTorch are used. In addition, Google Cloud Vision API and Azure Text Analytics are used to obtain user sentiment data from the input text and utterances. This identifies the user's current emotional state.

[0380] Next, the server automatically generates appropriate application programming interfaces (APIs) based on the analysis results and sentiment data. This process defines API endpoints specific to business requirements and documents their usage in technical documentation. The tone and content of this technical documentation are also customized according to the user's sentiment data. This feature allows users to utilize the system more intuitively.

[0381] Furthermore, the server generates recommended code snippets for the user, helping them quickly resolve specific technical challenges they face. These code snippets are also designed to take the user's current emotional state into consideration and reduce stress. For example, if a user is anxious about complex system configurations, the server will generate simple, step-by-step code snippets.

[0382] For example, if a customer has questions about a service at a physical store, such as "I don't know how to use this product," a store employee wearing smart glasses can instantly understand their feelings and provide a customized response to reassure them. Such a system enables seamless interaction between humans and technology in physical stores, and is expected to realize a high level of user experience.

[0383] An example of a prompt message would be: "During interactions with customers, recognize their emotions (e.g., anxiety, joy) in real time and display direct customer service advice on the employee's smart glasses."

[0384] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0385] Step 1:

[0386] The user inputs business requirements in natural language using a terminal. This input is obtained as text data in natural language format. This data is then passed by the server to the next process, natural language analysis. The user's emotional state, corresponding to their speech and input, is also recorded simultaneously.

[0387] Step 2:

[0388] The server parses the natural language text data received from the user using a natural language processing engine (e.g., TensorFlow or PyTorch). This process extracts specific intentions and requirements of the business requirements. The output is the parsed business requirements and their corresponding data structures. Furthermore, a sentiment engine is used to extract user sentiment data from the input data.

[0389] Step 3:

[0390] The server automatically generates application programming interface (API) endpoints using a generative AI model based on the analyzed business requirements and sentiment data. At this stage, the necessary API structure is identified based on the extracted functions. As output, specific API endpoint definitions are generated, and related technical documentation is created.

[0391] Step 4:

[0392] The server generates recommended code snippets, taking into account the user's emotional state. Using emotional data and analyzed business requirements as input, the output includes code snippets designed to alleviate user emotions and improve work efficiency.

[0393] Step 5:

[0394] Users receive APIs and code snippets generated through their devices, allowing them to intuitively and effectively interact with the system. Based on sentiment data collected throughout the system, the user experience is delivered in a personalized manner, enabling responses that are better suited to the user's needs.

[0395] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0396] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0397] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0398] [Third Embodiment]

[0399] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0400] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0401] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0402] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0403] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0404] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0405] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0406] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0407] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0408] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0409] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0410] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0411] This invention relates to a system that automatically performs API design, authentication configuration, documentation generation, security assessment, and code snippet generation based on business requirements provided by the user.

[0412] The user uses a terminal to input the business requirements they wish to implement. This input is in natural language, describing specific needs such as, "We would like to integrate AI into our inventory management system." The server receives this input information and analyzes the requirements using natural language processing technology.

[0413] Based on the analysis results, the server automatically generates API endpoints that meet the requirements. The generated APIs provide specific functions, such as checking and updating inventory, to satisfy the user's business needs. In doing so, the server automatically defines the API parameters and data format.

[0414] Furthermore, the server must select and implement an appropriate authentication method as a security measure for accessing the generated API. This is essential to guarantee secure access for users and clients.

[0415] This system also automatically generates technical documentation outlining the usage procedures and specifications of the generated APIs. Users can then appropriately utilize the APIs based on the provided technical documentation.

[0416] From a security standpoint, the server automatically assesses the overall system security risks. It can report any vulnerabilities discovered to the user and suggest corrective measures.

[0417] Finally, the server generates and provides recommended code snippets to help users easily assemble programs. This allows users to quickly develop features.

[0418] Thus, the invented system enables easy integration with AI technology, significantly streamlining the development of digital environments. Users can quickly implement necessary functions without undertaking large-scale system development. Server-based automation allows for the deployment of systems in an accurate and secure environment.

[0419] The following describes the processing flow.

[0420] Step 1:

[0421] The user uses a terminal to input specific business requirements in natural language. For example, they might make a request such as, "I want to set up an API to automatically update product inventory."

[0422] Step 2:

[0423] The server receives business requirements sent by the user and analyzes the request using a natural language processing engine. This analysis extracts the necessary functions and API components.

[0424] Step 3:

[0425] Based on the extracted requirements, the server translates each function into a specific API endpoint and automatically designs the endpoints. This process also configures the necessary input parameters and the format of the returned data for each endpoint.

[0426] Step 4:

[0427] The server applies an appropriate authentication protocol to the generated API endpoint. This authentication protocol, such as OAuth 2.0, is selected based on the security requirements specified by the user.

[0428] Step 5:

[0429] The server automatically generates detailed technical documentation for the designed API, clearly outlining how to use it and any important considerations. This generated documentation serves as a guideline for users to correctly utilize the API.

[0430] Step 6:

[0431] The server automatically assesses the security risks of the API and the entire system. Based on the assessment results, it identifies potential vulnerabilities and security areas that need improvement, and provides them to the user in a report.

[0432] Step 7:

[0433] The server automatically generates code snippets that help users integrate the API into their own systems and applications. This allows users to implement the necessary functionality more quickly.

[0434] (Example 1)

[0435] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0436] In modern information system development, it is necessary to respond quickly and efficiently to user business requirements. However, tasks such as designing program interfaces, configuring security, creating technical documentation, and preparing code are extremely time-consuming and labor-intensive when performed manually, and are also prone to errors. This invention aims to solve these problems and provide technology that enables the rapid and accurate implementation of functions required by users.

[0437] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0438] In this invention, the server includes means for analyzing business requests received from users using natural language processing technology and automatically generating the necessary program interfaces based on the analysis results; means for selecting and setting an appropriate identification method for the automatically generated program interfaces; and means for automatically generating technical documentation regarding the use of the generated program interfaces. This makes it possible to quickly and accurately provide functions that meet the user's business requests and to improve the efficiency of information system construction.

[0439] "Business requirements" are a natural language expression of the specific functions and performance that users require from an information system in order to accomplish a particular task.

[0440] "Natural language processing technology" is a technology that analyzes text written in human language and converts its meaning into a form that a computer can understand.

[0441] A "programming interface" is a set of rules and procedures that define how different software systems communicate with each other, and includes access points for providing functionality.

[0442] "Identification method" refers to the means or procedures used to verify the authentication and authorization of users and clients accessing the system.

[0443] "Technical documentation" refers to documents that describe the usage and specifications of a system and its components, providing information to enable users to properly utilize the system.

[0444] A "communication point" is a part of a program interface and refers to an access point for receiving requests from external sources.

[0445] "Connection privileges" refer to the right of users and clients who have undergone appropriate authentication to access a system or program interface.

[0446] This invention is a system that allows users to input specific business requests in natural language using a terminal. For example, a user inputs a request such as "I would like to integrate AI into the inventory management system" into the terminal's interface. The terminal then sends this information to the server.

[0447] The server utilizes generative AI models to analyze received input using natural language processing techniques. Through this analysis, the server identifies specific functions and objectives from user requests and automatically generates appropriate program interfaces. In doing so, the server uses a software development platform to define the communication points and data formats of the program interfaces. For example, it might set up API endpoints for managing inventory data and integrating with AI.

[0448] Next, the server automatically selects and configures an appropriate identification method for the generated program interface. Specifically, it applies authentication protocols such as OAuth or API keys to ensure that users can securely access the interface.

[0449] Furthermore, the server automatically generates technical documentation describing how to use the program interface. This documentation includes interface specifications, API usage procedures, and sample requests, helping users easily utilize the system. This enables users to quickly and appropriately build the system by referring to the provided documentation.

[0450] An example of a prompt might be, "I need an API that allows me to check the inventory status of online bookstores in real time." By entering such a prompt, the server automatically prepares a program interface that matches the user's request and provides a comprehensive solution including relevant technical information and authentication settings. This system enables users to build business systems quickly and accurately, and improve the efficiency of information processing.

[0451] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0452] Step 1:

[0453] The user uses a terminal to input business requests in natural language. A concrete example is writing "I would like to integrate AI into the inventory management system" in the input form. This request is sent to the server as a prompt message.

[0454] Step 2:

[0455] The server receives the prompt message and performs natural language processing using a generative AI model. The server analyzes the prompt message to identify business requests and specific functions and objectives. At this stage, text analysis is performed to extract keywords such as "inventory management" and "AI integration." The output after analysis provides data that identifies the user's objectives and functions.

[0456] Step 3:

[0457] The server automatically designs the program interface based on the analysis results. Specifically, it defines API endpoints based on the extracted functions and sets the necessary parameters and data formats. In this process, it uses an information system construction platform to generate an endpoint such as "GET / api / inventory". The output is a specification definition.

[0458] Step 4:

[0459] The server configures an identification method for the generated program interface to ensure secure user access. It automatically sets up authentication protocols by applying OAuth or API keys. At this stage, authentication keys are generated and applied. The applied authentication configuration information is obtained as output.

[0460] Step 5:

[0461] To ensure users can properly utilize the interface, the server automatically generates technical documentation detailing how to use the program interface. This documentation includes endpoint descriptions and sample code. The output is a technical document for the user.

[0462] Step 6:

[0463] Finally, the server automatically assesses the security of the entire system and the security risks associated with the generated interfaces. Based on this assessment, it reports any vulnerabilities found and suggested mitigation measures to the user. A security report is then generated as output.

[0464] (Application Example 1)

[0465] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0466] Traditional inventory management and tracking systems at logistics centers have faced challenges in efficient operation due to difficulties in integrating with a wide variety of software platforms. Furthermore, the complexity of designing application interfaces and the cumbersome setup of authentication and security measures have been significant issues. In such environments, there is a need for overall system automation and improved security.

[0467] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0468] In this invention, the server includes means for analyzing information processing requirements received from a user using natural language processing and automatically generating the necessary application interface based on the analysis results; means for generating application interfaces for machinery and equipment deployed at logistics processing centers to cooperate with inventory management and tracking systems; and means for selecting and setting an appropriate authentication method for the automatically generated application interface. This enables efficient automation of complex logistics management operations while allowing smooth cooperation between different systems without compromising security.

[0469] "Information processing requirements" refer to the specific functions and system operation requirements that users need to perform their tasks.

[0470] "Natural language processing" is a technology that enables computers to understand and process natural human language, and is a means of extracting useful information from text data.

[0471] An "application interface" is a means of communication used to exchange functions and information between different software programs.

[0472] A "logistics processing center" is a facility that handles a series of logistics-related operations, such as receiving, storing, sorting, and shipping goods.

[0473] "Machinery and equipment" refers to a series of technological devices used to automate tasks and perform operations efficiently.

[0474] An "inventory management and tracking system" is a system that monitors the quantity and location of goods in storage and optimizes the logistics process.

[0475] "Authentication methods" refer to a set of processes and techniques used to legitimize access to a system.

[0476] An "application interface communication connection point" refers to a point of connection where different systems or programs exchange data with each other.

[0477] "Authentication rules" are procedures and rules used to verify access rights and identify users in digital systems.

[0478] To implement this invention, an information processing system including a server is required. The server receives information processing requirements provided by the user through a terminal and performs natural language processing. For this processing, spaCy, a highly accurate NLP library, is used as the natural language processing technology. Based on the results of the processing, the necessary application interfaces are designed and automatically generated. In this process, Python programs and the Flask framework are utilized to establish flexible API communication connection points.

[0479] The server then generates APIs that enable machinery and equipment deployed at logistics processing centers to interact with inventory management and tracking systems. In this process, authentication protocols to ensure security are automated using the OAuth library.

[0480] To maintain the security of the entire system, the server incorporates a process that performs security risk assessments on the generated application interfaces and automatically generates technical documentation. This allows users to smoothly utilize the API while referring to the provided technical documentation. In addition, short programs are automatically generated to enable users to proceed with development quickly.

[0481] As a concrete example, let's assume a small robot at a logistics center performs the task of "checking and updating the inventory status of product code 123." In this case, by entering the prompt message, "Please generate an API to link with the inventory management system and check the inventory of product code 123 in real time," the system will generate and operate the appropriate API. This will streamline the inventory check work within the logistics center.

[0482] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0483] Step 1:

[0484] The user enters business requirements into the terminal in natural language. The terminal sends the entered information to the server. The input for this process is a prompt from the user, such as "Please generate an API that will connect with the inventory management system and allow real-time inventory checks for product code 123," and the output is the requirement information received by the server. This requirement is sent to the server as the data that forms the basis for API generation.

[0485] Step 2:

[0486] The server inputs the received requirements information into a natural language processing engine (e.g., spaCy) and analyzes the information. In this process, the server breaks down the input natural language and extracts the functional requirements as structured data. The output consists of the specific functions and parameters included in the requirements. Specifically, the process involves breaking down the text into morphemes, analyzing the meaning of each word, and mapping them to functions.

[0487] Step 3:

[0488] The server automatically generates the necessary application interfaces based on the analysis results. The input is functional requirement data, and the output is the automatically generated API endpoints and specifications. The server uses the Flask framework to program and configure the API endpoints in Python. In this processing step, the APIs for the functions requested from the terminal are generated and immediately available.

[0489] Step 4:

[0490] For the generated application interface, the server automatically configures appropriate authentication using an authentication protocol (e.g., OAuth). The input is the generated API specification, and the output is a secure API with built-in authentication. The server then performs specific operations to add code that manages access permissions to ensure secure user access.

[0491] Step 5:

[0492] The server automatically generates technical documentation for using the generated API. The input is the endpoint and its specifications, and the output is documentation outlining the steps a user takes to use the API. This includes available endpoints, parameters, authentication information, etc. This step provides documented information to improve user convenience.

[0493] Step 6:

[0494] The server automatically generates and provides code snippets for use at logistics centers. Input is technical documentation and API endpoint information, while output is code snippets for specific implementations. Using a generation AI model, the server suggests efficient program code to help expedite work.

[0495] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0496] This invention relates to a system that enables the efficient development of digital environments using AI technology, and in particular aims to improve the user experience by incorporating an emotion engine.

[0497] Users input business requirements into the system using a terminal in natural language. At the same time, the system captures the user's emotional state from their speech and writing. For example, suppose a user inputs the requirement, "I want to resolve this inventory management delay." The server uses an emotion engine to recognize the emotion of "anxiety" from this input.

[0498] The server analyzes the received business requirements and simultaneously combines them with emotional data extracted via the emotion engine, reflecting the results in the analysis. By flexibly adjusting the analysis results according to the emotional data, more personalized solutions are provided.

[0499] Next, an API endpoint is automatically generated based on the analysis results. The authentication protocol settings used here are also adjusted as appropriate according to the user's emotional state. For example, if the user is in an unstable emotional state, a more robust authentication procedure may be adopted.

[0500] Furthermore, the server generates technical documentation on the use of the generated APIs. The tone and content of the technical documentation are optimized according to the emotions identified by the emotion engine and are provided in a user-friendly format.

[0501] In addition, recommended code snippets are automatically generated to help users develop more efficiently. These snippets are also structured based on user sentiment data and designed to reduce user stress.

[0502] Thus, by incorporating an emotion engine, the present invention provides a superior user experience and efficiency compared to conventional digital environment development systems. In particular, by incorporating emotional data into the analysis process, a more user-friendly system that meets user needs can be constructed.

[0503] The following describes the processing flow.

[0504] Step 1:

[0505] The user enters their business requirements into the terminal. Here, requests in natural language, such as "I want to integrate the inventory management system with AI immediately," are entered.

[0506] Step 2:

[0507] The device sends user input to a text analysis module, and simultaneously analyzes the user's emotions with an emotion recognition module. In this case, the expression "right now" is used to identify the emotions of "urgency" and "anxiety."

[0508] Step 3:

[0509] The server receives business requirements and sentiment information sent from the terminal and analyzes them using a natural language processing engine. This analysis extracts specific API functions related to "inventory management" and "AI integration."

[0510] Step 4:

[0511] The server automatically designs the necessary API endpoints based on the extracted functions. The design process proceeds with priority given to "urgency."

[0512] Step 5:

[0513] The server configures the authentication protocol for the generated API. Because the user is feeling "anxious," a simplified authentication procedure is selected to enable quick access.

[0514] Step 6:

[0515] The server automatically generates technical documentation to support the use of the generated API. This documentation is designed to be emotionally resonant, clear, and concise, ensuring that users can quickly understand it.

[0516] Step 7:

[0517] The server automatically generates recommended code snippets to help users utilize the system. Considering the user's "urgency," immediately usable sample code is provided.

[0518] Step 8:

[0519] The device presents the generated APIs, technical documentation, and code snippets to the user, establishing an immediately usable interaction.

[0520] This processing flow allows users to set up a system environment that enables them to efficiently solve problems.

[0521] (Example 2)

[0522] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0523] Modern information processing systems suffer from a decline in user experience quality because automatically generated information processing interfaces, based on user input, are provided without considering the user's emotional state. Furthermore, the generated interfaces and technical documentation are not optimized to meet user needs and emotions, resulting in a lack of convenience and efficiency. In addition, while rapid response is required regarding security, traditional methods have limitations.

[0524] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0525] In this invention, the server includes means for recognizing the user's emotional state and integrating emotional data into the analysis results to personalize the solution; means for selecting and setting an appropriate authentication method for the automatically generated information processing interface; and means for automatically generating technical documentation regarding the use of the generated information processing interface and optimizing it to a tone that corresponds to the user's emotions. This makes it possible to provide an interface that takes the user's emotions into consideration, improving the user experience and achieving highly secure information processing.

[0526] A "user" refers to a person who inputs business requirements into a system and receives information processing services.

[0527] "Natural language processing technology" refers to the technology used by computers to understand, analyze, and generate natural human language.

[0528] "Analysis results" refers to the output information after analyzing the received business requirements using natural language processing.

[0529] An "information processing interface" refers to a software structure that functions as a medium for different software components to exchange information.

[0530] "Emotional state" refers to the psychological and emotional characteristics recognized from the user's input.

[0531] "Emotional data" refers to information about emotions extracted from user utterances and input.

[0532] "Personalization" refers to adjusting responses and services to suit the specific needs and circumstances of a particular user.

[0533] "Authentication method" refers to the process or technology used to verify a user's identity and grant them permissions.

[0534] "Technical documentation" refers to a document that describes in detail the usage and characteristics of the generated information processing interface.

[0535] "Security risk" refers to the possibility that the security of a system may be threatened by unauthorized access, leakage, or tampering of information.

[0536] A "recommended program snippet" refers to a short code example that demonstrates the recommended way of writing code to efficiently perform a particular task.

[0537] This invention is based on an information processing system, analyzes the user's business requirements using natural language processing technology, and improves the user experience by recognizing the user's emotional state using an emotion engine. The following are specific embodiments for carrying out the present invention.

[0538] The server is equipped with high-performance data processing hardware and software that implements natural language processing algorithms. This server receives business requirements from users and analyzes the input text data. This analysis is intended to identify the problems the user is facing. In particular, it configures the necessary connection points and authentication methods based on the analysis results to enable the automatic generation of information processing interfaces.

[0539] In addition, the server uses an emotion engine to extract emotional data from user input. This data is integrated with the analysis results and used to provide personalized solutions tailored to each individual user. The user's emotional state is also taken into consideration when creating technical documentation that shows how to use the generated interface.

[0540] For example, if a user enters "I want to resolve this inventory management delay," the server analyzes the business requirements, and at the same time, its emotion engine detects the emotion of "urgency." Based on this information, a rapid response with a sense of urgency is suggested, and the authentication protocol used may also be strengthened.

[0541] Furthermore, users can receive automatically generated recommended program snippets, which are presented in a format tailored to their current emotional state. This allows users to work more efficiently.

[0542] An example of a prompt might be the text, "Please provide a template that utilizes the latest machine learning models." In response, the server provides the relevant information processing interface and, if necessary, provides appropriate technical documentation and program snippets. This system allows users to receive support that takes their emotional state into consideration and to perform their tasks with high efficiency.

[0543] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0544] Step 1:

[0545] Users input business requirements into this system using a terminal in natural language. This input is sent to the server. The server receives this input and analyzes the business requirements as text data using natural language processing technology. The output of this analysis is information about the specific problems and actions that the user seeks to solve.

[0546] Step 2:

[0547] The server applies an emotion engine to further process the analyzed business requirements. It extracts emotional states from the user's input text and integrates this emotion data with the analysis results of the business requirements. Specifically, it analyzes keywords and phrases in the text and recognizes emotions such as "anxiety" and "interest." The output of this process is a more detailed and personalized business requirement that includes emotion data.

[0548] Step 3:

[0549] The server automatically generates an appropriate information processing interface based on business requirements that integrate emotional data. This process determines which APIs are needed and which authentication protocols to use. Specifically, it implements API endpoints tailored to the business requirements and authentication settings to manage access permissions. The output is the automatically generated information processing interface.

[0550] Step 4:

[0551] The server automatically generates technical documentation regarding the use of the generated information processing interface. This includes a description of the interface's functions and usage procedures. Furthermore, the tone and content of the documentation are optimized, taking into account the user's emotional state. The output is technical documentation provided in a user-friendly format.

[0552] Step 5:

[0553] The server generates recommended program snippets to assist in the use of the information processing interface. These program snippets are optimized to efficiently meet business requirements and are constructed based on user sentiment data. At this stage, a completed program snippet is output as a response to user input, and the user can use it for further development.

[0554] (Application Example 2)

[0555] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0556] In today's digital environment, there is a need for systems that can respond quickly and accurately to users' business requirements. However, existing systems do not adequately provide personalized responses that take into account the user's emotional state. As a result, the user experience is degraded, and business efficiency is hindered. It is necessary to improve this and create a more user-friendly and less stressful interaction.

[0557] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0558] In this invention, the server includes means for analyzing business requirements received from the user using natural language processing and automatically generating the necessary application programming interface based on the analysis results; means for acquiring emotional data in real time from the customer's facial expressions and speech and generating a personalized response to the user's business requirements based on that data; and means for automatically evaluating the security risks of the generated application programming interface and the entire system. This enables flexible responses that take into account the user's emotional state, resulting in a better user experience and improved operational efficiency.

[0559] "Business requirements" are specific requirements related to the objectives that the user wants to achieve or the problems they want to solve.

[0560] "Natural language processing" is a technology that enables computers to understand and analyze the language that humans use in everyday life.

[0561] An "application programming interface" is a set of definitions and procedures that enable different software components to communicate with one another.

[0562] "Emotional data" refers to information that indicates an individual's psychological state, analyzed from nonverbal cues such as facial expressions and voice.

[0563] A "personalized response" is a reply or action that is customized according to the preferences and circumstances of a particular individual.

[0564] "Security risk" refers to potential threats and vulnerabilities related to information systems and data.

[0565] A "recommended code snippet" is a sample code snippet provided to help users solve a specific technical problem.

[0566] An "authentication protocol" is a set of procedures and rules that ensure only authorized users can access a system or data.

[0567] To implement this invention, users must access the system using a dedicated terminal and input their business requirements in natural language format. The server analyzes this input using a natural language processing engine to understand the business requirements. For analysis, advanced natural language processing is enabled, and machine learning libraries such as TensorFlow and PyTorch are used. In addition, Google Cloud Vision API and Azure Text Analytics are used to obtain user sentiment data from the input text and utterances. This identifies the user's current emotional state.

[0568] Next, the server automatically generates appropriate application programming interfaces (APIs) based on the analysis results and sentiment data. This process defines API endpoints specific to business requirements and documents their usage in technical documentation. The tone and content of this technical documentation are also customized according to the user's sentiment data. This feature allows users to utilize the system more intuitively.

[0569] Furthermore, the server generates recommended code snippets for the user, helping them quickly resolve specific technical challenges they face. These code snippets are also designed to take the user's current emotional state into consideration and reduce stress. For example, if a user is anxious about complex system configurations, the server will generate simple, step-by-step code snippets.

[0570] For example, if a customer has questions about a service at a physical store, such as "I don't know how to use this product," a store employee wearing smart glasses can instantly understand their feelings and provide a customized response to reassure them. Such a system enables seamless interaction between humans and technology in physical stores, and is expected to realize a high level of user experience.

[0571] An example of a prompt message would be: "During interactions with customers, recognize their emotions (e.g., anxiety, joy) in real time and display direct customer service advice on the employee's smart glasses."

[0572] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0573] Step 1:

[0574] The user inputs business requirements in natural language using a terminal. This input is obtained as text data in natural language format. This data is then passed by the server to the next process, natural language analysis. The user's emotional state, corresponding to their speech and input, is also recorded simultaneously.

[0575] Step 2:

[0576] The server parses the natural language text data received from the user using a natural language processing engine (e.g., TensorFlow or PyTorch). This process extracts specific intentions and requirements of the business requirements. The output is the parsed business requirements and their corresponding data structures. Furthermore, a sentiment engine is used to extract user sentiment data from the input data.

[0577] Step 3:

[0578] The server automatically generates application programming interface (API) endpoints using a generative AI model based on the analyzed business requirements and sentiment data. At this stage, the necessary API structure is identified based on the extracted functions. As output, specific API endpoint definitions are generated, and related technical documentation is created.

[0579] Step 4:

[0580] The server generates recommended code snippets, taking into account the user's emotional state. Using emotional data and analyzed business requirements as input, the output includes code snippets designed to alleviate user emotions and improve work efficiency.

[0581] Step 5:

[0582] Users receive APIs and code snippets generated through their devices, allowing them to intuitively and effectively interact with the system. Based on sentiment data collected throughout the system, the user experience is delivered in a personalized manner, enabling responses that are better suited to the user's needs.

[0583] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0584] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0585] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0586] [Fourth Embodiment]

[0587] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0588] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0589] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0590] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0591] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0592] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0593] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0594] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0595] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0596] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0597] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0598] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0599] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0600] This invention relates to a system that automatically performs API design, authentication configuration, documentation generation, security assessment, and code snippet generation based on business requirements provided by the user.

[0601] The user uses a terminal to input the business requirements they wish to implement. This input is in natural language, describing specific needs such as, "We would like to integrate AI into our inventory management system." The server receives this input information and analyzes the requirements using natural language processing technology.

[0602] Based on the analysis results, the server automatically generates API endpoints that meet the requirements. The generated APIs provide specific functions, such as checking and updating inventory, to satisfy the user's business needs. In doing so, the server automatically defines the API parameters and data format.

[0603] Furthermore, the server must select and implement an appropriate authentication method as a security measure for accessing the generated API. This is essential to guarantee secure access for users and clients.

[0604] This system also automatically generates technical documentation outlining the usage procedures and specifications of the generated APIs. Users can then appropriately utilize the APIs based on the provided technical documentation.

[0605] From a security standpoint, the server automatically assesses the overall system security risks. It can report any vulnerabilities discovered to the user and suggest corrective measures.

[0606] Finally, the server generates and provides recommended code snippets to help users easily assemble programs. This allows users to quickly develop features.

[0607] Thus, the invented system enables easy integration with AI technology, significantly streamlining the development of digital environments. Users can quickly implement necessary functions without undertaking large-scale system development. Server-based automation allows for the deployment of systems in an accurate and secure environment.

[0608] The following describes the processing flow.

[0609] Step 1:

[0610] The user uses a terminal to input specific business requirements in natural language. For example, they might make a request such as, "I want to set up an API to automatically update product inventory."

[0611] Step 2:

[0612] The server receives business requirements sent by the user and analyzes the request using a natural language processing engine. This analysis extracts the necessary functions and API components.

[0613] Step 3:

[0614] Based on the extracted requirements, the server translates each function into a specific API endpoint and automatically designs the endpoints. This process also configures the necessary input parameters and the format of the returned data for each endpoint.

[0615] Step 4:

[0616] The server applies an appropriate authentication protocol to the generated API endpoint. This authentication protocol, such as OAuth 2.0, is selected based on the security requirements specified by the user.

[0617] Step 5:

[0618] The server automatically generates detailed technical documentation for the designed API, clearly outlining how to use it and any important considerations. This generated documentation serves as a guideline for users to correctly utilize the API.

[0619] Step 6:

[0620] The server automatically assesses the security risks of the API and the entire system. Based on the assessment results, it identifies potential vulnerabilities and security areas that need improvement, and provides them to the user in a report.

[0621] Step 7:

[0622] The server automatically generates code snippets that help users integrate the API into their own systems and applications. This allows users to implement the necessary functionality more quickly.

[0623] (Example 1)

[0624] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0625] In modern information system development, it is necessary to respond quickly and efficiently to user business requirements. However, tasks such as designing program interfaces, configuring security, creating technical documentation, and preparing code are extremely time-consuming and labor-intensive when performed manually, and are also prone to errors. This invention aims to solve these problems and provide technology that enables the rapid and accurate implementation of functions required by users.

[0626] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0627] In this invention, the server includes means for analyzing business requests received from users using natural language processing technology and automatically generating the necessary program interfaces based on the analysis results; means for selecting and setting an appropriate identification method for the automatically generated program interfaces; and means for automatically generating technical documentation regarding the use of the generated program interfaces. This makes it possible to quickly and accurately provide functions that meet the user's business requests and to improve the efficiency of information system construction.

[0628] "Business requirements" are a natural language expression of the specific functions and performance that users require from an information system in order to accomplish a particular task.

[0629] "Natural language processing technology" is a technology that analyzes text written in human language and converts its meaning into a form that a computer can understand.

[0630] A "programming interface" is a set of rules and procedures that define how different software systems communicate with each other, and includes access points for providing functionality.

[0631] "Identification method" refers to the means or procedures used to verify the authentication and authorization of users and clients accessing the system.

[0632] "Technical documentation" refers to documents that describe the usage and specifications of a system and its components, providing information to enable users to properly utilize the system.

[0633] A "communication point" is a part of a program interface and refers to an access point for receiving requests from external sources.

[0634] "Connection privileges" refer to the right of users and clients who have undergone appropriate authentication to access a system or program interface.

[0635] This invention is a system that allows users to input specific business requests in natural language using a terminal. For example, a user inputs a request such as "I would like to integrate AI into the inventory management system" into the terminal's interface. The terminal then sends this information to the server.

[0636] The server utilizes generative AI models to analyze received input using natural language processing techniques. Through this analysis, the server identifies specific functions and objectives from user requests and automatically generates appropriate program interfaces. In doing so, the server uses a software development platform to define the communication points and data formats of the program interfaces. For example, it might set up API endpoints for managing inventory data and integrating with AI.

[0637] Next, the server automatically selects and configures an appropriate identification method for the generated program interface. Specifically, it applies authentication protocols such as OAuth or API keys to ensure that users can securely access the interface.

[0638] Furthermore, the server automatically generates technical documentation describing how to use the program interface. This documentation includes interface specifications, API usage procedures, and sample requests, helping users easily utilize the system. This enables users to quickly and appropriately build the system by referring to the provided documentation.

[0639] An example of a prompt might be, "I need an API that allows me to check the inventory status of online bookstores in real time." By entering such a prompt, the server automatically prepares a program interface that matches the user's request and provides a comprehensive solution including relevant technical information and authentication settings. This system enables users to build business systems quickly and accurately, and improve the efficiency of information processing.

[0640] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0641] Step 1:

[0642] The user uses a terminal to input business requests in natural language. A concrete example is writing "I would like to integrate AI into the inventory management system" in the input form. This request is sent to the server as a prompt message.

[0643] Step 2:

[0644] The server receives the prompt message and performs natural language processing using a generative AI model. The server analyzes the prompt message to identify business requests and specific functions and objectives. At this stage, text analysis is performed to extract keywords such as "inventory management" and "AI integration." The output after analysis provides data that identifies the user's objectives and functions.

[0645] Step 3:

[0646] The server automatically designs the program interface based on the analysis results. Specifically, it defines API endpoints based on the extracted functions and sets the necessary parameters and data formats. In this process, it uses an information system construction platform to generate an endpoint such as "GET / api / inventory". The output is a specification definition.

[0647] Step 4:

[0648] The server configures an identification method for the generated program interface to ensure secure user access. It automatically sets up authentication protocols by applying OAuth or API keys. At this stage, authentication keys are generated and applied. The applied authentication configuration information is obtained as output.

[0649] Step 5:

[0650] To ensure users can properly utilize the interface, the server automatically generates technical documentation detailing how to use the program interface. This documentation includes endpoint descriptions and sample code. The output is a technical document for the user.

[0651] Step 6:

[0652] Finally, the server automatically assesses the security of the entire system and the security risks associated with the generated interfaces. Based on this assessment, it reports any vulnerabilities found and suggested mitigation measures to the user. A security report is then generated as output.

[0653] (Application Example 1)

[0654] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0655] Traditional inventory management and tracking systems at logistics centers have faced challenges in efficient operation due to difficulties in integrating with a wide variety of software platforms. Furthermore, the complexity of designing application interfaces and the cumbersome setup of authentication and security measures have been significant issues. In such environments, there is a need for overall system automation and improved security.

[0656] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0657] In this invention, the server includes means for analyzing information processing requirements received from a user using natural language processing and automatically generating the necessary application interface based on the analysis results; means for generating application interfaces for machinery and equipment deployed at logistics processing centers to cooperate with inventory management and tracking systems; and means for selecting and setting an appropriate authentication method for the automatically generated application interface. This enables efficient automation of complex logistics management operations while allowing smooth cooperation between different systems without compromising security.

[0658] "Information processing requirements" refer to the specific functions and system operation requirements that users need to perform their tasks.

[0659] "Natural language processing" is a technology that enables computers to understand and process natural human language, and is a means of extracting useful information from text data.

[0660] An "application interface" is a means of communication used to exchange functions and information between different software programs.

[0661] A "logistics processing center" is a facility that handles a series of logistics-related operations, such as receiving, storing, sorting, and shipping goods.

[0662] "Machinery and equipment" refers to a series of technological devices used to automate tasks and perform operations efficiently.

[0663] An "inventory management and tracking system" is a system that monitors the quantity and location of goods in storage and optimizes the logistics process.

[0664] "Authentication methods" refer to a set of processes and techniques used to legitimize access to a system.

[0665] An "application interface communication connection point" refers to a point of connection where different systems or programs exchange data with each other.

[0666] "Authentication rules" are procedures and rules used to verify access rights and identify users in digital systems.

[0667] To implement this invention, an information processing system including a server is required. The server receives information processing requirements provided by the user through a terminal and performs natural language processing. For this processing, spaCy, a highly accurate NLP library, is used as the natural language processing technology. Based on the results of the processing, the necessary application interfaces are designed and automatically generated. In this process, Python programs and the Flask framework are utilized to establish flexible API communication connection points.

[0668] The server then generates APIs that enable machinery and equipment deployed at logistics processing centers to interact with inventory management and tracking systems. In this process, authentication protocols to ensure security are automated using the OAuth library.

[0669] To maintain the security of the entire system, the server incorporates a process that performs security risk assessments on the generated application interfaces and automatically generates technical documentation. This allows users to smoothly utilize the API while referring to the provided technical documentation. In addition, short programs are automatically generated to enable users to proceed with development quickly.

[0670] As a concrete example, let's assume a small robot at a logistics center performs the task of "checking and updating the inventory status of product code 123." In this case, by entering the prompt message, "Please generate an API to link with the inventory management system and check the inventory of product code 123 in real time," the system will generate and operate the appropriate API. This will streamline the inventory check work within the logistics center.

[0671] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0672] Step 1:

[0673] The user enters business requirements into the terminal in natural language. The terminal sends the entered information to the server. The input for this process is a prompt from the user, such as "Please generate an API that will connect with the inventory management system and allow real-time inventory checks for product code 123," and the output is the requirement information received by the server. This requirement is sent to the server as the data that forms the basis for API generation.

[0674] Step 2:

[0675] The server inputs the received requirements information into a natural language processing engine (e.g., spaCy) and analyzes the information. In this process, the server breaks down the input natural language and extracts the functional requirements as structured data. The output consists of the specific functions and parameters included in the requirements. Specifically, the process involves breaking down the text into morphemes, analyzing the meaning of each word, and mapping them to functions.

[0676] Step 3:

[0677] The server automatically generates the necessary application interfaces based on the analysis results. The input is functional requirement data, and the output is the automatically generated API endpoints and specifications. The server uses the Flask framework to program and configure the API endpoints in Python. In this processing step, the APIs for the functions requested from the terminal are generated and immediately available.

[0678] Step 4:

[0679] For the generated application interface, the server automatically configures appropriate authentication using an authentication protocol (e.g., OAuth). The input is the generated API specification, and the output is a secure API with built-in authentication. The server then performs specific operations to add code that manages access permissions to ensure secure user access.

[0680] Step 5:

[0681] The server automatically generates technical documentation for using the generated API. The input is the endpoint and its specifications, and the output is documentation outlining the steps a user takes to use the API. This includes available endpoints, parameters, authentication information, etc. This step provides documented information to improve user convenience.

[0682] Step 6:

[0683] The server automatically generates and provides code snippets for use at logistics centers. Input is technical documentation and API endpoint information, while output is code snippets for specific implementations. Using a generation AI model, the server suggests efficient program code to help expedite work.

[0684] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0685] This invention relates to a system that enables the efficient development of digital environments using AI technology, and in particular aims to improve the user experience by incorporating an emotion engine.

[0686] Users input business requirements into the system using a terminal in natural language. At the same time, the system captures the user's emotional state from their speech and writing. For example, suppose a user inputs the requirement, "I want to resolve this inventory management delay." The server uses an emotion engine to recognize the emotion of "anxiety" from this input.

[0687] The server analyzes the received business requirements and simultaneously combines them with emotional data extracted via the emotion engine, reflecting the results in the analysis. By flexibly adjusting the analysis results according to the emotional data, more personalized solutions are provided.

[0688] Next, an API endpoint is automatically generated based on the analysis results. The authentication protocol settings used here are also adjusted as appropriate according to the user's emotional state. For example, if the user is in an unstable emotional state, a more robust authentication procedure may be adopted.

[0689] Furthermore, the server generates technical documentation on the use of the generated APIs. The tone and content of the technical documentation are optimized according to the emotions identified by the emotion engine and are provided in a user-friendly format.

[0690] In addition, recommended code snippets are automatically generated to help users develop more efficiently. These snippets are also structured based on user sentiment data and designed to reduce user stress.

[0691] Thus, by incorporating an emotion engine, the present invention provides a superior user experience and efficiency compared to conventional digital environment development systems. In particular, by incorporating emotional data into the analysis process, a more user-friendly system that meets user needs can be constructed.

[0692] The following describes the processing flow.

[0693] Step 1:

[0694] The user enters their business requirements into the terminal. Here, requests in natural language, such as "I want to integrate the inventory management system with AI immediately," are entered.

[0695] Step 2:

[0696] The device sends user input to a text analysis module, and simultaneously analyzes the user's emotions with an emotion recognition module. In this case, the expression "right now" is used to identify the emotions of "urgency" and "anxiety."

[0697] Step 3:

[0698] The server receives business requirements and sentiment information sent from the terminal and analyzes them using a natural language processing engine. This analysis extracts specific API functions related to "inventory management" and "AI integration."

[0699] Step 4:

[0700] The server automatically designs the necessary API endpoints based on the extracted functions. The design process proceeds with priority given to "urgency."

[0701] Step 5:

[0702] The server configures the authentication protocol for the generated API. Because the user is feeling "anxious," a simplified authentication procedure is selected to enable quick access.

[0703] Step 6:

[0704] The server automatically generates technical documentation to support the use of the generated API. This documentation is designed to be emotionally resonant, clear, and concise, ensuring that users can quickly understand it.

[0705] Step 7:

[0706] The server automatically generates recommended code snippets to help users utilize the system. Considering the user's "urgency," immediately usable sample code is provided.

[0707] Step 8:

[0708] The device presents the generated APIs, technical documentation, and code snippets to the user, establishing an immediately usable interaction.

[0709] This processing flow allows users to set up a system environment that enables them to efficiently solve problems.

[0710] (Example 2)

[0711] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0712] Modern information processing systems suffer from a decline in user experience quality because automatically generated information processing interfaces, based on user input, are provided without considering the user's emotional state. Furthermore, the generated interfaces and technical documentation are not optimized to meet user needs and emotions, resulting in a lack of convenience and efficiency. In addition, while rapid response is required regarding security, traditional methods have limitations.

[0713] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0714] In this invention, the server includes means for recognizing the user's emotional state and integrating emotional data into the analysis results to personalize the solution; means for selecting and setting an appropriate authentication method for the automatically generated information processing interface; and means for automatically generating technical documentation regarding the use of the generated information processing interface and optimizing it to a tone that corresponds to the user's emotions. This makes it possible to provide an interface that takes the user's emotions into consideration, improving the user experience and achieving highly secure information processing.

[0715] A "user" refers to a person who inputs business requirements into a system and receives information processing services.

[0716] "Natural language processing technology" refers to the technology used by computers to understand, analyze, and generate natural human language.

[0717] "Analysis results" refers to the output information after analyzing the received business requirements using natural language processing.

[0718] An "information processing interface" refers to a software structure that functions as a medium for different software components to exchange information.

[0719] "Emotional state" refers to the psychological and emotional characteristics recognized from the user's input.

[0720] "Emotional data" refers to information about emotions extracted from user utterances and input.

[0721] "Personalization" refers to adjusting responses and services to suit the specific needs and circumstances of a particular user.

[0722] "Authentication method" refers to the process or technology used to verify a user's identity and grant them permissions.

[0723] "Technical documentation" refers to a document that describes in detail the usage and characteristics of the generated information processing interface.

[0724] "Security risk" refers to the possibility that the security of a system may be threatened by unauthorized access, leakage, or tampering of information.

[0725] A "recommended program snippet" refers to a short code example that demonstrates the recommended way of writing code to efficiently perform a particular task.

[0726] This invention is based on an information processing system, analyzes the user's business requirements using natural language processing technology, and improves the user experience by recognizing the user's emotional state using an emotion engine. The following are specific embodiments for carrying out the present invention.

[0727] The server is equipped with high-performance data processing hardware and software that implements natural language processing algorithms. This server receives business requirements from users and analyzes the input text data. This analysis is intended to identify the problems the user is facing. In particular, it configures the necessary connection points and authentication methods based on the analysis results to enable the automatic generation of information processing interfaces.

[0728] In addition, the server uses an emotion engine to extract emotional data from user input. This data is integrated with the analysis results and used to provide personalized solutions tailored to each individual user. The user's emotional state is also taken into consideration when creating technical documentation that shows how to use the generated interface.

[0729] For example, if a user enters "I want to resolve this inventory management delay," the server analyzes the business requirements, and at the same time, its emotion engine detects the emotion of "urgency." Based on this information, a rapid response with a sense of urgency is suggested, and the authentication protocol used may also be strengthened.

[0730] Furthermore, users can receive automatically generated recommended program snippets, which are presented in a format tailored to their current emotional state. This allows users to work more efficiently.

[0731] An example of a prompt might be the text, "Please provide a template that utilizes the latest machine learning models." In response, the server provides the relevant information processing interface and, if necessary, provides appropriate technical documentation and program snippets. This system allows users to receive support that takes their emotional state into consideration and to perform their tasks with high efficiency.

[0732] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0733] Step 1:

[0734] Users input business requirements into this system using a terminal in natural language. This input is sent to the server. The server receives this input and analyzes the business requirements as text data using natural language processing technology. The output of this analysis is information about the specific problems and actions that the user seeks to solve.

[0735] Step 2:

[0736] The server applies an emotion engine to further process the analyzed business requirements. It extracts emotional states from the user's input text and integrates this emotion data with the analysis results of the business requirements. Specifically, it analyzes keywords and phrases in the text and recognizes emotions such as "anxiety" and "interest." The output of this process is a more detailed and personalized business requirement that includes emotion data.

[0737] Step 3:

[0738] The server automatically generates an appropriate information processing interface based on business requirements that integrate emotional data. This process determines which APIs are needed and which authentication protocols to use. Specifically, it implements API endpoints tailored to the business requirements and authentication settings to manage access permissions. The output is the automatically generated information processing interface.

[0739] Step 4:

[0740] The server automatically generates technical documentation regarding the use of the generated information processing interface. This includes a description of the interface's functions and usage procedures. Furthermore, the tone and content of the documentation are optimized, taking into account the user's emotional state. The output is technical documentation provided in a user-friendly format.

[0741] Step 5:

[0742] The server generates recommended program snippets to assist in the use of the information processing interface. These program snippets are optimized to efficiently meet business requirements and are constructed based on user sentiment data. At this stage, a completed program snippet is output as a response to user input, and the user can use it for further development.

[0743] (Application Example 2)

[0744] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0745] In today's digital environment, there is a need for systems that can respond quickly and accurately to users' business requirements. However, existing systems do not adequately provide personalized responses that take into account the user's emotional state. As a result, the user experience is degraded, and business efficiency is hindered. It is necessary to improve this and create a more user-friendly and less stressful interaction.

[0746] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0747] In this invention, the server includes means for analyzing business requirements received from the user using natural language processing and automatically generating the necessary application programming interface based on the analysis results; means for acquiring emotional data in real time from the customer's facial expressions and speech and generating a personalized response to the user's business requirements based on that data; and means for automatically evaluating the security risks of the generated application programming interface and the entire system. This enables flexible responses that take into account the user's emotional state, resulting in a better user experience and improved operational efficiency.

[0748] "Business requirements" are specific requirements related to the objectives that the user wants to achieve or the problems they want to solve.

[0749] "Natural language processing" is a technology that enables computers to understand and analyze the language that humans use in everyday life.

[0750] An "application programming interface" is a set of definitions and procedures that enable different software components to communicate with one another.

[0751] "Emotional data" refers to information that indicates an individual's psychological state, analyzed from nonverbal cues such as facial expressions and voice.

[0752] A "personalized response" is a reply or action that is customized according to the preferences and circumstances of a particular individual.

[0753] "Security risk" refers to potential threats and vulnerabilities related to information systems and data.

[0754] A "recommended code snippet" is a sample code snippet provided to help users solve a specific technical problem.

[0755] An "authentication protocol" is a set of procedures and rules that ensure only authorized users can access a system or data.

[0756] To implement this invention, users must access the system using a dedicated terminal and input their business requirements in natural language format. The server analyzes this input using a natural language processing engine to understand the business requirements. For analysis, advanced natural language processing is enabled, and machine learning libraries such as TensorFlow and PyTorch are used. In addition, Google Cloud Vision API and Azure Text Analytics are used to obtain user sentiment data from the input text and utterances. This identifies the user's current emotional state.

[0757] Next, the server automatically generates appropriate application programming interfaces (APIs) based on the analysis results and sentiment data. This process defines API endpoints specific to business requirements and documents their usage in technical documentation. The tone and content of this technical documentation are also customized according to the user's sentiment data. This feature allows users to utilize the system more intuitively.

[0758] Furthermore, the server generates recommended code snippets for the user, helping them quickly resolve specific technical challenges they face. These code snippets are also designed to take the user's current emotional state into consideration and reduce stress. For example, if a user is anxious about complex system configurations, the server will generate simple, step-by-step code snippets.

[0759] For example, if a customer has questions about a service at a physical store, such as "I don't know how to use this product," a store employee wearing smart glasses can instantly understand their feelings and provide a customized response to reassure them. Such a system enables seamless interaction between humans and technology in physical stores, and is expected to realize a high level of user experience.

[0760] An example of a prompt message would be: "During interactions with customers, recognize their emotions (e.g., anxiety, joy) in real time and display direct customer service advice on the employee's smart glasses."

[0761] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0762] Step 1:

[0763] The user inputs business requirements in natural language using a terminal. This input is obtained as text data in natural language format. This data is then passed by the server to the next process, natural language analysis. The user's emotional state, corresponding to their speech and input, is also recorded simultaneously.

[0764] Step 2:

[0765] The server parses the natural language text data received from the user using a natural language processing engine (e.g., TensorFlow or PyTorch). This process extracts specific intentions and requirements of the business requirements. The output is the parsed business requirements and their corresponding data structures. Furthermore, a sentiment engine is used to extract user sentiment data from the input data.

[0766] Step 3:

[0767] The server automatically generates application programming interface (API) endpoints using a generative AI model based on the analyzed business requirements and sentiment data. At this stage, the necessary API structure is identified based on the extracted functions. As output, specific API endpoint definitions are generated, and related technical documentation is created.

[0768] Step 4:

[0769] The server generates recommended code snippets, taking into account the user's emotional state. Using emotional data and analyzed business requirements as input, the output includes code snippets designed to alleviate user emotions and improve work efficiency.

[0770] Step 5:

[0771] Users receive APIs and code snippets generated through their devices, allowing them to intuitively and effectively interact with the system. Based on sentiment data collected throughout the system, the user experience is delivered in a personalized manner, enabling responses that are better suited to the user's needs.

[0772] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0773] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0774] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0775] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0776] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0777] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0778] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0779] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0780] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0781] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0782] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0783] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0784] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0785] 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.

[0786] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0787] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0788] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0789] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0790] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0791] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0792] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0793] The following is further disclosed regarding the embodiments described above.

[0794] (Claim 1)

[0795] A means for analyzing business requirements received from users using natural language processing and automatically generating the necessary application programming interfaces based on the analysis results,

[0796] A means for selecting and configuring an appropriate authentication method for an automatically generated application programming interface,

[0797] A means for automatically generating technical documentation regarding the use of the generated application programming interface,

[0798] A means for automatically evaluating the security risks of the generated application programming interface and the entire system,

[0799] A means of automatically generating code snippets for recommended code to users,

[0800] A system that includes this.

[0801] (Claim 2)

[0802] The system according to claim 1, comprising means for defining endpoints of an application programming interface based on functions extracted from the user's business requirements by natural language processing.

[0803] (Claim 3)

[0804] The system according to claim 1, comprising means for automating the configuration of an authentication protocol in order to guarantee access rights to an application programming interface.

[0805] "Example 1"

[0806] (Claim 1)

[0807] A means for analyzing business requests received from users using natural language processing technology and automatically generating the necessary program interfaces based on the analysis results,

[0808] A means for selecting and setting an appropriate identification method for an automatically generated program interface,

[0809] A means for automatically generating technical documentation regarding the use of the generated program interface,

[0810] A means for automatically evaluating the security risks of the generated program interface and the entire information system,

[0811] A means of automatically generating recommended code snippets for the user,

[0812] A system that includes this.

[0813] (Claim 2)

[0814] The system according to claim 1, comprising means for defining communication points of a program interface based on functions extracted from the user's business requirements using natural language processing technology.

[0815] (Claim 3)

[0816] The system according to claim 1, comprising means for automating the setting of an identification procedure in order to guarantee access rights to the program interface.

[0817] "Application Example 1"

[0818] (Claim 1)

[0819] A means for analyzing information processing requirements received from users using natural language processing, and for automatically generating the necessary application interfaces based on the analysis results,

[0820] A means for generating an application interface for machinery and equipment deployed at a logistics processing center to link with an inventory management and tracking system,

[0821] A means for selecting and configuring an appropriate authentication method for an automatically generated application interface,

[0822] A means for automatically generating technical documentation regarding the use of the generated application interface,

[0823] A means for automatically evaluating the safety risks of the generated application interface and the entire system,

[0824] A means of automatically generating short programs of recommended code for users,

[0825] A system that includes this.

[0826] (Claim 2)

[0827] The system according to claim 1, comprising means for defining communication connection points of an application interface based on functions extracted from the user's information processing requirements by natural language processing.

[0828] (Claim 3)

[0829] The system according to claim 1, comprising means for automating the setting of authentication rules in order to guarantee access rights to an application interface.

[0830] "Example 2 of combining an emotion engine"

[0831] (Claim 1)

[0832] A means for analyzing business requirements received from users using natural language processing technology and automatically generating the necessary information processing interfaces based on the analysis results,

[0833] A means of recognizing the user's emotional state, integrating emotional data into the analysis results, and personalizing solutions.

[0834] A means for selecting and setting an appropriate authentication method for an automatically generated information processing interface,

[0835] A means for automatically generating technical documentation on the use of the generated information processing interface and optimizing it to a tone that responds to the user's emotions,

[0836] A means for automatically evaluating the safety risks of the generated information processing interface and the entire system,

[0837] A means of automatically generating recommended program snippets for users and designing them while considering the user's emotional state,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, comprising means for defining connection points of an information processing interface based on information extracted from the user's business requirements by natural language processing.

[0841] (Claim 3)

[0842] The system according to claim 1, comprising means for automating the configuration of authentication technology in order to ensure access rights to an information processing interface.

[0843] "Application example 2 when combining with an emotional engine"

[0844] (Claim 1)

[0845] A means for analyzing business requirements received from users using natural language processing and automatically generating the necessary application programming interfaces based on the analysis results,

[0846] A means for selecting and configuring an appropriate authentication method for an automatically generated application programming interface,

[0847] A means for automatically generating technical documentation regarding the use of the generated application programming interface,

[0848] A means for automatically evaluating the security risks of the generated application programming interface and the entire system,

[0849] A means of automatically generating code snippets for recommended code to users,

[0850] A means of acquiring emotional data in real time from customer facial expressions and speech, and generating personalized responses to the user's business requirements based on that data,

[0851] A system that includes this.

[0852] (Claim 2)

[0853] The system according to claim 1, which includes means for defining endpoints of an application programming interface based on functions extracted from the user's business requirements using natural language processing, and further adapting the endpoints while taking sentiment data into consideration.

[0854] (Claim 3)

[0855] The system according to claim 1, comprising means for analyzing the user's emotional state and automatically adjusting the authentication protocol settings according to the situation in order to guarantee access rights to the application programming interface. [Explanation of Symbols]

[0856] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for analyzing business requirements received from users using natural language processing and automatically generating the necessary application programming interfaces based on the analysis results, A means for selecting and configuring an appropriate authentication method for an automatically generated application programming interface, A means for automatically generating technical documentation regarding the use of the generated application programming interface, A means for automatically evaluating the security risks of the generated application programming interface and the entire system, A means of automatically generating code snippets for recommended code to users, A system that includes this.

2. The system according to claim 1, comprising means for defining endpoints of an application programming interface based on functions extracted from the user's business requirements by natural language processing.

3. The system according to claim 1, comprising means for automating the configuration of an authentication protocol in order to guarantee access rights to an application programming interface.

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