system

Abstract

We provide the system. [Solution] A means of analyzing input business requirements using natural language processing technology and extracting important elements, A means to clarify business requirements based on the extracted elements and convert them into specific requirements, A means of referring to past project data and proposing the optimal technical options, A means of integrating and prioritizing requirements across departments, A means to evaluate in real time the impact of requirement changes on the entire system, 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] Due to the complexity and ambiguity of business requirements, it takes time to sort out the requirements, and it is difficult to determine an appropriate technical selection. Also, when the requirements are changed, it may not be possible to respond smoothly, which may hinder the progress of the project. It is necessary to solve such difficulties and achieve rapid and accurate requirement sorting and appropriate technical proposals.

Means for Solving the Problems

[0005] To address this challenge, the present invention provides a system that includes means for analyzing input business requirements using natural language processing technology and extracting key elements, means for clarifying business requirements based on the extracted elements and converting them into specific requirements, means for referring to past project data and proposing the optimal technical options, means for integrating and prioritizing requirements across departments, and means for evaluating the impact of requirement changes on the entire system in real time, thereby enabling the rapid and efficient resolution of business requirements.

[0006] "Natural language processing technology" is a technology that aims to analyze data written in natural language and understand its meaning and structure.

[0007] "Business requirements" are a collection of requirements that describe the functions and conditions necessary to perform a particular task.

[0008] An "element" is a fundamental or important part or component that makes up the whole.

[0009] "Requirements" are standards that specify the particular functions and performance expected of a system or project.

[0010] "Technical options" refer to multiple different choices of technologies and methods available to solve a particular problem.

[0011] "Project data" refers to a collection of information and records related to past projects.

[0012] "Priority" refers to the order in which multiple items or tasks are arranged based on their importance or urgency.

[0013] "Evaluation" is the act of analyzing data or events and judging their value or effectiveness. [Brief explanation of the drawing]

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

MODE FOR CARRYING OUT THE INVENTION

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

[0016] First, the terms 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 numbered storage is one or a plurality of non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[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 is a system that efficiently analyzes business requirements and supports the selection of appropriate technologies. It is realized by analyzing the business requirements entered by the user using natural language processing technology. The user enters the requirements in natural language using a dedicated terminal. This input is sent to a server, which uses an NLP engine to structure the requirements and extract important elements.

[0036] Based on the clarified requirements, the server references past project data to present technical options, including similar success stories. This selection is performed by the "Project Knowledge Utilization AI," which suggests suitable programming languages ​​and frameworks for implementation. Furthermore, the server integrates requirements submitted from multiple departments with the help of the "Cross-Departmental Requirements Unification AI," generating a prioritized list of requirements while making adjustments.

[0037] For example, if the sales department requests "enhanced customer management functions" and the IT department requests "improved data security," the server will optimally integrate both requirements and build a feasible set of functions. Furthermore, if requirements change, the "Requirement Change Impact Prediction AI" will evaluate the impact of the change on the overall system in real time and issue alerts for necessary adjustments and resource reallocations.

[0038] Furthermore, the server utilizes "technology trend monitoring AI" to constantly collect the latest technological information and make real-time decisions on adopting new technologies. In this way, the present invention enables rapid and accurate requirements definition and technology selection, supporting project success.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] Users input their business requirements for development in natural language using a dedicated terminal.

[0042] Step 2:

[0043] The terminal sends the entered business requirements to the server.

[0044] Step 3:

[0045] The server analyzes the received requirements using a natural language processing engine and extracts important elements and keywords from the requirements.

[0046] Step 4:

[0047] The server uses the "Requirements Clarification AI" to extract elements, convert business requirements into specific requirements, and summarize them in an easy-to-understand format.

[0048] Step 5:

[0049] The server uses "Project Knowledge Utilization AI" to refer to a database of past projects and proposes technical options and success stories that are suitable for the clarified requirements.

[0050] Step 6:

[0051] The server uses a "cross-departmental requirements unification AI" to integrate and adjust different requirements provided by multiple departments, creating a unified priority list.

[0052] Step 7:

[0053] When requirements change, the server uses "Requirements Change Impact Prediction AI" to evaluate the impact of the change in real time and calculate the degree of impact on the overall project and the need for resource reallocation.

[0054] Step 8:

[0055] The server uses "technology trend monitoring AI" to monitor the latest technology information and trends, and proposes new technologies to be reflected in the system as needed.

[0056] (Example 1)

[0057] 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."

[0058] Traditional business systems lack effective methods for analyzing business requirements and selecting technologies, leading to inefficiencies in project execution. Furthermore, they struggle to properly integrate requirements from different departments and optimize the overall system. Impact assessments of requirement changes are also inadequate, increasing the risk of project delays and failures. Moreover, there is a need for systems with the flexibility to quickly adapt to changing technological trends.

[0059] 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.

[0060] In this invention, the server includes means for analyzing business requirements using natural language processing technology and extracting core elements, means for referring to past business data and presenting optimal technical options, and means for integrating and prioritizing requirements across departments. This enables efficient analysis of business requirements and accelerated technology selection, as well as optimization of cross-departmental requirements and real-time evaluation of requirement changes. Furthermore, the application of the latest technologies can improve the success rate of projects.

[0061] "Natural language processing technology" refers to computational techniques that analyze text data to understand and generate human language.

[0062] "Business requirements" refer to the specific conditions and demands regarding the functions and performance required in a business process.

[0063] "Core elements" refer to the most important and fundamental elements or points necessary to fulfill business requirements.

[0064] "Past business data" refers to information about records and deliverables of projects that were carried out in the past.

[0065] "Technological options" refer to a collection of technologies, tools, and methods available to meet specific business needs.

[0066] "Interdepartmental requests" refer to business needs and demands originating from different departments or divisions.

[0067] "Prioritizing" refers to determining the order of multiple business needs or tasks based on their importance and urgency.

[0068] "Assessing the impact of requirements changes" is the process of analyzing and evaluating the impact that changes in business requirements have on the entire project.

[0069] "Monitoring technical information" refers to the activity of continuously collecting and evaluating information on the latest technological trends and innovations.

[0070] "Generative AI technology" is a technology that uses artificial intelligence to generate new data and information.

[0071] This invention provides a system for efficiently processing business requests and supporting appropriate technology selection. Users express business requests in natural language and input them into a dedicated terminal. The terminal sends the input requests to a server, encrypting the data using the SSL / TLS protocol to ensure secure data exchange.

[0072] The server uses natural language processing software to analyze incoming business requests. Specifically, it uses generative AI technology to convert the input requests into structured data and extract core elements. This eliminates ambiguity and clarifies the requests.

[0073] Next, the server accesses a database of past projects and references similar projects and success stories to suggest the optimal technical options. Here, technical elements such as programming languages ​​and frameworks are proposed, allowing the user to proceed with their work based on them.

[0074] Furthermore, the server integrates and analyzes differing requests from different departments to prioritize them. This enables efficient business operations aimed at overall optimization. In addition, if requests change, the server evaluates the impact in real time and issues alerts prompting necessary adjustments and resource reallocation.

[0075] The server also plays a role in "monitoring technical information," tracking the latest technological trends and quickly incorporating new technologies into the system. This system allows companies to respond flexibly to technological innovation.

[0076] For example, if a user enters "requirements for building a new customer management system," the server can refer to data and technical knowledge from past customer management systems to suggest the optimal solution. An example of a prompt message would be, "Please enter details of the functions required to build the new customer management system." In this way, rapid and accurate analysis of business requirements and better technology selection become possible.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] Users input business requests in natural language into a dedicated terminal. These inputs include requests such as enhanced customer management functions and improved data security. The terminal receives these requests as text data, performs formatting checks, and grammatical errors. This prepares the user's input data for transmission to the server.

[0080] Step 2:

[0081] The terminal encrypts verified text data and sends it to the server using a secure communication protocol. Input data is encrypted by the terminal to prevent unauthorized access or tampering during transmission. The encrypted input data is then securely delivered to the server.

[0082] Step 3:

[0083] The server decrypts the received encrypted data and passes it to the natural language processing engine. The NLP engine analyzes the input data using a generative AI model and extracts keywords and dependencies. This data processing results in structured requirements data, which is then converted into a format that can be further processed within the server.

[0084] Step 4:

[0085] The server uses structured requirements data to search its internal database for similar past project data. From the extracted data, it performs calculations to suggest the optimal technical options. The results of this process are generated in the form of programming language selections and framework suggestions, which are then compiled by the server.

[0086] Step 5:

[0087] The server integrates requirements data collected across departments by combining multiple requests into a single dataset. At this stage, similarities and duplicates of requests are detected, and consensus is reached to optimize processing. As a result of the processing, a prioritized list of requirements is output.

[0088] Step 6:

[0089] When requirements change, the server analyzes the impact using an "AI for predicting the impact of requirement changes." It compares requirement data before and after the change and evaluates the impact on the entire project. If necessary, it issues alerts to the relevant departments and reallocates resources. The results of the impact assessment are provided to the stakeholders.

[0090] Step 7:

[0091] The server utilizes "technology trend monitoring AI" to continuously collect and analyze technical information. It determines whether the latest technologies should be adopted and generates suggestions for adopting new technologies. This allows users to obtain information for implementing projects that utilize the latest technologies.

[0092] (Application Example 1)

[0093] 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."

[0094] In manufacturing, there is a need for automation to integrate diverse requirements from various departments and operate efficiently on the production line. However, it is difficult to properly integrate requests from different departments, immediately assess the impact of changes, and select the optimal work procedures. Furthermore, it is necessary to quickly adapt to new technologies and materials.

[0095] 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.

[0096] In this invention, the server includes means for analyzing input business requirements using natural language processing technology and extracting important elements, means for clarifying the business requirements based on the extracted elements and converting them into specific requirements, and means for analyzing production instructions and selecting the optimal work procedure. This enables the integration of requirements from different departments, rapid evaluation of the impact of changes, and selection of the optimal work procedure based on the latest technology.

[0097] "Natural language processing technology" is a technology that analyzes input written and spoken language data and handles it as structured information.

[0098] "Business requirements" refer to the conditions and specifications necessary to perform a particular task.

[0099] "Key elements" refer to the information and conditions that are essential for fulfilling business requirements.

[0100] "Technical options" refer to various technologies that can be adopted to solve a specific problem.

[0101] "Interdepartmental requirements" refer to the unique needs and demands submitted by different departments.

[0102] "Production instructions" refer to information used to specify the manufacturing process, including work procedures and resource allocation.

[0103] A "work procedure" refers to a combination of specific steps and methods for efficiently carrying out a particular manufacturing process.

[0104] "Impact assessment" is the process of analyzing and determining how a change in requirements will affect the entire system and processes.

[0105] This invention is implemented as a system that efficiently analyzes business requirements and supports the selection of appropriate technologies. The user inputs business requirements in natural language using a dedicated terminal. The terminal sends this input to a server. The server analyzes the business requirements using an NLP engine and extracts key elements. Through this analysis, the business requirements are converted into specific requirements.

[0106] The server uses the extracted elements to reference past project data and present the optimal technical options. If production instructions are included, the server optimizes the work procedures and makes suggestions for efficient execution on the production line. These suggestions include technology selection and procedure selection.

[0107] In addition, the server has the ability to integrate requirements sent from different departments and generate a prioritized list. If requirements change, it evaluates the impact of the change on the overall process in real time and performs an impact analysis. It also constantly monitors the latest technical information and incorporates new technologies into the system as needed.

[0108] For example, if requirements for creating a prototype of a new product are entered, the server analyzes those requirements, selects and proposes the optimal materials and processes. If the use of new materials is decided, the impact can be immediately evaluated, necessary adjustments can be made, and the system can be applied to the exported product.

[0109] Example of a prompt:

[0110] "Please enter the assembly requirements for the new product: Example: 'Assemble the protein shaker lid in 3 steps'"

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] Users use a dedicated terminal to input business requirements in natural language. This input is done by the user through the terminal, providing specific requirements and preferences as free-form text input. The entered data is sent to the server, which serves as the starting point for subsequent processes.

[0114] Step 2:

[0115] The server analyzes the received business requirements using a natural language processing (NLP) engine. Specifically, the server processes the text using the NLP engine and extracts important elements from the requirements. In this step, the raw input data is analyzed and converted into a list of extracted important elements, which is then output.

[0116] Step 3:

[0117] The server references past project data based on the extracted key elements and presents the optimal technical options. The server searches the database to find similar success stories. The input is a list of key elements, and the output is a list of recommended technologies and methods. A generative AI model is then used to improve the accuracy of the suggestions.

[0118] Step 4:

[0119] The server integrates and prioritizes requirements from each department. At this stage, the server runs an integration algorithm to organize the different needs into a consistent list. It takes a list of requirements from each department as input and outputs it as a unified, prioritized list. This process is designed to resolve inconsistencies that may arise during the coordination process.

[0120] Step 5:

[0121] The server evaluates the impact of requirement changes in real time. It then processes these changes and simulates their impact on the entire system. The input here is the change in requirements, and the output is the predicted impact and recommended adjustments.

[0122] Step 6:

[0123] The server monitors the latest technology information and automatically selects technologies to reflect in the system. Specifically, the server interacts with external technology information databases to incorporate new technologies and trends. A continuous feedback loop is used to maintain an up-to-date state. The input is a feed of technology information, and the output is an updated list of technology selections.

[0124] 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.

[0125] This invention is a system that accurately analyzes business requirements and proposes the optimal technical selection, further improving its accuracy by incorporating an emotion engine that recognizes user emotions. Users input business requirements in natural language using a dedicated terminal. This input is sent to a server, which uses natural language processing technology to analyze the business requirements and extract important elements.

[0126] The server is equipped with an emotion engine that recognizes the user's emotions from the input requirements. Based on this emotional information, the interpretation of the requirements is optimized. For example, if the user has an emotion indicating urgency, the server will focus its analysis on that part of the requirements and prioritize suggesting technical options that can be addressed quickly.

[0127] Furthermore, the server adjusts existing technical options based on the user's emotions recognized by the emotion engine, providing suggestions that better align with the user's intentions. It references past project data, incorporates lessons learned from success stories, and delivers optimized solutions. In this process, it becomes easier to address emotional needs and improves user satisfaction.

[0128] For example, if a user indicates that "a quick response is important," the server will select technology that excels at real-time processing. Features for integrating and prioritizing requirements across departments can also be designed to address each department's needs in a balanced way by considering user sentiment.

[0129] When requirements change, the "Requirements Change Impact Prediction AI" is used to evaluate the overall impact of the change, and risk assessment is performed based on user sentiment information obtained from the sentiment engine, and countermeasures are determined. In this way, the introduction of the sentiment engine enables precise analysis of business requirements and rapid technology selection, providing a system that further supports project success.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] Users input business requirements and expected results in natural language using a dedicated terminal. During input, the terminal also captures the user's facial expressions and voice tone.

[0133] Step 2:

[0134] The terminal sends the entered business requirements data and user sentiment data to the server.

[0135] Step 3:

[0136] The server uses a natural language processing engine to analyze the input business requirements and extract important elements and keywords.

[0137] Step 4:

[0138] The server analyzes user emotional data using an emotion engine to understand the emotional tendencies the user exhibits. This allows for a complementary evaluation of requirements, such as urgency and importance, from an emotional perspective.

[0139] Step 5:

[0140] Based on the extracted elements and sentiment data, the server clarifies business requirements and lists specific priority requirements. This list will reflect emotional needs.

[0141] Step 6:

[0142] The server utilizes "Project Knowledge Utilization AI" to refer to a database of past projects and present the most suitable technical options based on the user's requests and emotional tendencies.

[0143] Step 7:

[0144] The server uses a "cross-departmental requirements unification AI" to integrate overall requirements, including requests from other departments, and prioritizes them while also considering the emotional needs of each department.

[0145] Step 8:

[0146] When requirements change, the server uses an "AI for predicting the impact of requirement changes" to evaluate the impact of the change, and also uses emotional information obtained from an emotional engine to conduct a new risk assessment, thereby providing the most appropriate countermeasure for the user.

[0147] (Example 2)

[0148] 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".

[0149] In today's business environment, business requirements are becoming increasingly complex, and a variety of emotions are influencing them. However, traditional systems often fail to propose technical options without considering user emotions, resulting in insufficient solutions that align with user intentions. Furthermore, they struggle to respond quickly to changes in requirements, lowering project success rates. To address this challenge, a system is needed that incorporates user emotions during the business requirements analysis process and optimizes requirements interpretation.

[0150] 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.

[0151] In this invention, the server includes means for analyzing input business requirements using natural language processing technology and extracting important elements, means for clarifying the business requirements based on the extracted elements and converting them into specific requirements, and sentiment analysis means for recognizing emotions from the input business requirements and optimizing the interpretation based on those emotions. This enables the provision of quick and appropriate technical selections that take into account the user's emotions and allows for the proposal of optimal solutions to business requirements.

[0152] "Natural language processing technology" is a technique that analyzes input text data to understand the meaning and structure of words and phrases.

[0153] "Business requirements" refer to information that indicates the conditions and specifications necessary to execute a particular business process.

[0154] "Emotion analysis" is a technology that uses user input to understand the type and intensity of emotions and utilize that information.

[0155] A "technical option" is a list that enumerates the advantages of technologies and methods that can be used to solve a particular problem.

[0156] A "database" is a software system that systematically stores and manages information, making it easily accessible.

[0157] "Requirements change" refers to the process of adding, modifying, or deleting existing business requirements and specifications.

[0158] "Emotional information" refers to data that indicates a user's emotional state, and is the information necessary for interpreting that information.

[0159] "Risk assessment" is the process of analyzing the risks that may arise when a specific event occurs and evaluating their impact.

[0160] The system for implementing this invention consists of a dedicated terminal, a server, and a communication network connecting them. Users can input business requirements in natural language using the dedicated terminal. The terminal is equipped with a user interface designed to allow users to input information efficiently.

[0161] The terminal transmits the entered business requirements data to the server in real time. The server uses natural language processing technology to process the received data, analyzing and extracting important elements of the input text. This analysis generally utilizes general-purpose NLP libraries and generative AI models to achieve high accuracy.

[0162] Next, the server uses an emotion engine to recognize the user's emotions from the input business requirements. The emotion engine uses common emotion analysis software to perform emotion analysis, optimizing the interpretation of requirements by instantly analyzing the user's emotional information.

[0163] The server references past project data from the database and compares it to successful cases to suggest the most appropriate technical options for the user. This process utilizes a database management system to facilitate data retrieval. The server then provides feedback to the user with the adjusted technical options, making suggestions based on business requirements. Specifically, it uses general database software and incorporates emotional information regarding the user's intent during the feedback process.

[0164] For example, if a user inputs a requirement such as "a quick response is important," the system uses its sentiment analysis function to extract high-priority emotions related to urgency, and then proposes specific real-time processing techniques based on the analysis results.

[0165] An example of a prompt message to be input into the generating AI model is: "There is a need for a rapid response regarding specific business requirements. The user is emphasizing urgency. What is the optimal technical option?"

[0166] With this configuration, the present invention enables efficient analysis of complex business requirements and the selection of the optimal technology while taking user emotions into consideration.

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

[0168] Step 1:

[0169] Users input business requirements in natural language using a dedicated terminal. The entered requirements are formatted as text data. The terminal displays guidelines on the screen to assist the user in inputting information and immediately provides feedback to the user if there are any input errors.

[0170] Step 2:

[0171] The terminal sends the entered business requirements data to the server. The data is encrypted via a communication protocol and delivered to the server in real time over a secure network.

[0172] Step 3:

[0173] The server passes the received data to a natural language processing engine to analyze business requirements. The input text data is tokenized, and important elements are extracted. A generative AI model analyzes the meaning of the text based on context, extracting keywords and structuring the requirements.

[0174] Step 4:

[0175] The server uses an emotion engine to recognize the user's emotions from the input business requirements. Emotion analysis software analyzes emotional expressions contained in the text, identifying the type and intensity of those emotions. The analyzed emotion data is used to prioritize in the next processing step.

[0176] Step 5:

[0177] The server optimizes the interpretation of business requirements based on emotional information, prioritizing key elements and emotions. For example, if emotional information including urgency is recognized, it prioritizes interpreting that requirement and considers technical options for immediate response. In this process, it uses generated prompt statements to explore the optimal technical choice.

[0178] Step 6:

[0179] The server references a database of past projects, matching them with successful case studies to propose the optimal technical options. Using a database management system, it searches for highly similar projects and incorporates insights gained from their implementation results. The proposed technical options are generated as information optimized according to business requirements.

[0180] Step 7:

[0181] The server sends the analysis results and proposed technical options to the terminal and provides feedback to the user. The feedback includes the interpreted requirements, related technical proposals, and the rationale behind their selection. The user can then make a final decision based on this feedback.

[0182] (Application Example 2)

[0183] 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".

[0184] In analyzing business requirements, conventional systems cannot consider the user's emotional state, resulting in a problem where optimal technical proposals cannot be made in accordance with the user's intentions and urgency. Furthermore, in physical stores, there is a lack of product and service proposals that utilize customer emotional information, posing a challenge to improving customer satisfaction.

[0185] 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.

[0186] In this invention, the server includes means for analyzing input business requirements using natural language processing technology and extracting important elements; means for recognizing the user's emotional state, interpreting business requirements based on that emotional information, and proposing the optimal technical options; and means for utilizing emotional information to improve the customer experience and propose the optimal products and services. This enables accurate interpretation of business requirements and technical proposals while considering the user's emotions, and in physical stores, it enables improved customer satisfaction through product and service proposals based on customer emotions.

[0187] "Natural language processing technology" is a technique that analyzes input text data and extracts and structures information in a way that is easy for humans to understand.

[0188] "Business requirements" refer to the conditions and specifications necessary to perform a specific task.

[0189] "Recognizing emotional states" means having the ability to judge a person's emotions and changes in those emotions based on their input and circumstances.

[0190] "Technical options" refer to the selection of technologies and methods available to achieve a specific objective.

[0191] "User sentiment information" refers to data about a user's psychological or emotional state, obtained from their actions and statements.

[0192] "Customer experience" refers to the overall satisfaction and impression that customers have of a product or service.

[0193] "Proposing products and services" refers to the act of selecting products and services that are deemed optimal based on the specific needs and desires of a particular customer.

[0194] The system that realizes this invention mainly consists of a server, a user terminal (such as a smartphone or smart glasses), and software modules that are responsible for emotion recognition and natural language processing.

[0195] The server receives business requirements and customer input information sent from user terminals. This information is analyzed using a natural language processing engine. For natural language processing, a generative AI model utilizing modern AI technology (e.g., GPT-4®) is used. This model extracts important elements from the input text and clarifies them as business requirements.

[0196] Next, the server utilizes an emotion recognition engine to evaluate the user's emotional state. This engine is particularly adept at capturing emotions related to urgency and priority, and can utilize Microsoft® and other cloud-based emotion analysis services. Based on the emotional information, the interpretation of business requirements is optimized, and the server generates the optimal technical options.

[0197] Furthermore, the server accesses a database of past projects and leverages insights from successful cases to propose technologies and solutions that best suit the user's intentions, tailored to their customer experience. In this process, it also makes emotionally-driven suggestions for products and services.

[0198] For example, if a user enters "I urgently need new sneakers," the emotion engine will recognize the urgency and suggest inventory checks at the nearest store and the fastest way to obtain them.

[0199] Examples of prompt statements are as follows:

[0200] User input: 'I urgently need new sneakers.'

[0201] Prompt: 'We are seeking the best product suggestions and store information for this request. Urgency is emphasized in the user's message. Please provide advice that includes a prompt response that takes their feelings into consideration.'

[0202] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0203] Step 1:

[0204] The server receives information from the user's terminal. The input consists of business requirements and requests entered by the user via a smartphone or smart glasses. This includes natural language data in text format, which the server receives and stores appropriately.

[0205] Step 2:

[0206] The server uses a natural language processing engine to analyze the received text data. The input is the text data obtained in step 1. The server passes this input data to a generating AI model, which converts the business requirements into a structured format and extracts the key elements. The output is the analyzed data.

[0207] Step 3:

[0208] The server analyzes the user's emotional state using an emotion recognition engine. The input is the output data from step 2, which contains emotional cues and expressions. The server extracts emotional information using the emotion recognition engine and determines urgency and priority. The output is data related to the user's emotions.

[0209] Step 4:

[0210] The server generates optimal technical options based on emotional information and analyzed business requirements. The input is the output data from steps 2 and 3. The server references a database of past projects and selects the best technical solution based on successful case studies. The output is the proposed technical options.

[0211] Step 5:

[0212] The server uses emotional information to suggest appropriate products and services with the aim of improving the customer experience. The inputs are the emotional data from step 3 and the technical options from step 4. Through this, the server provides optimal purchasing guidance to specific users and suggests specific product information and available services. The output is the product and service suggestions presented to the user.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] [Second Embodiment]

[0217] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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".

[0229] This invention is a system that efficiently analyzes business requirements and supports the selection of appropriate technologies. It is realized by analyzing the business requirements entered by the user using natural language processing technology. The user enters the requirements in natural language using a dedicated terminal. This input is sent to a server, which uses an NLP engine to structure the requirements and extract important elements.

[0230] Based on the clarified requirements, the server references past project data to present technical options, including similar success stories. This selection is performed by the "Project Knowledge Utilization AI," which suggests suitable programming languages ​​and frameworks for implementation. Furthermore, the server integrates requirements submitted from multiple departments with the help of the "Cross-Departmental Requirements Unification AI," generating a prioritized list of requirements while making adjustments.

[0231] For example, if the sales department requests "enhanced customer management functions" and the IT department requests "improved data security," the server will optimally integrate both requirements and build a feasible set of functions. Furthermore, if requirements change, the "Requirement Change Impact Prediction AI" will evaluate the impact of the change on the overall system in real time and issue alerts for necessary adjustments and resource reallocations.

[0232] Furthermore, the server utilizes "technology trend monitoring AI" to constantly collect the latest technological information and make real-time decisions on adopting new technologies. In this way, the present invention enables rapid and accurate requirements definition and technology selection, supporting project success.

[0233] The following describes the processing flow.

[0234] Step 1:

[0235] Users input their business requirements for development in natural language using a dedicated terminal.

[0236] Step 2:

[0237] The terminal sends the entered business requirements to the server.

[0238] Step 3:

[0239] The server analyzes the received requirements using a natural language processing engine and extracts important elements and keywords from the requirements.

[0240] Step 4:

[0241] The server uses the "Requirements Clarification AI" to extract elements, convert business requirements into specific requirements, and summarize them in an easy-to-understand format.

[0242] Step 5:

[0243] The server uses "Project Knowledge Utilization AI" to refer to a database of past projects and proposes technical options and success stories that are suitable for the clarified requirements.

[0244] Step 6:

[0245] The server uses a "cross-departmental requirements unification AI" to integrate and adjust different requirements provided by multiple departments, creating a unified priority list.

[0246] Step 7:

[0247] When requirements change, the server uses "Requirements Change Impact Prediction AI" to evaluate the impact of the change in real time and calculate the degree of impact on the overall project and the need for resource reallocation.

[0248] Step 8:

[0249] The server uses "technology trend monitoring AI" to monitor the latest technology information and trends, and proposes new technologies to be reflected in the system as needed.

[0250] (Example 1)

[0251] 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."

[0252] Traditional business systems lack effective methods for analyzing business requirements and selecting technologies, leading to inefficiencies in project execution. Furthermore, they struggle to properly integrate requirements from different departments and optimize the overall system. Impact assessments of requirement changes are also inadequate, increasing the risk of project delays and failures. Moreover, there is a need for systems with the flexibility to quickly adapt to changing technological trends.

[0253] 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.

[0254] In this invention, the server includes means for analyzing business requirements using natural language processing technology and extracting core elements, means for referring to past business data and presenting optimal technical options, and means for integrating and prioritizing requirements across departments. This enables efficient analysis of business requirements and accelerated technology selection, as well as optimization of cross-departmental requirements and real-time evaluation of requirement changes. Furthermore, the application of the latest technologies can improve the success rate of projects.

[0255] "Natural language processing technology" refers to computational techniques that analyze text data to understand and generate human language.

[0256] "Business requirements" refer to the specific conditions and demands regarding the functions and performance required in a business process.

[0257] "Core elements" refer to the most important and fundamental elements or points necessary to fulfill business requirements.

[0258] "Past business data" refers to information about records and deliverables of projects that were carried out in the past.

[0259] "Technological options" refer to a collection of technologies, tools, and methods available to meet specific business needs.

[0260] "Interdepartmental requests" refer to business needs and demands originating from different departments or divisions.

[0261] "Prioritizing" refers to determining the order of multiple business needs or tasks based on their importance and urgency.

[0262] "Assessing the impact of requirements changes" is the process of analyzing and evaluating the impact that changes in business requirements have on the entire project.

[0263] "Monitoring technical information" refers to the activity of continuously collecting and evaluating information on the latest technological trends and innovations.

[0264] "Generative AI technology" is a technology that uses artificial intelligence to generate new data and information.

[0265] This invention provides a system for efficiently processing business requests and supporting appropriate technology selection. Users express business requests in natural language and input them into a dedicated terminal. The terminal sends the input requests to a server, encrypting the data using the SSL / TLS protocol to ensure secure data exchange.

[0266] The server uses natural language processing software to analyze incoming business requests. Specifically, it uses generative AI technology to convert the input requests into structured data and extract core elements. This eliminates ambiguity and clarifies the requests.

[0267] Next, the server accesses a database of past projects and references similar projects and success stories to suggest the optimal technical options. Here, technical elements such as programming languages ​​and frameworks are proposed, allowing the user to proceed with their work based on them.

[0268] Furthermore, the server integrates and analyzes differing requests from different departments to prioritize them. This enables efficient business operations aimed at overall optimization. In addition, if requests change, the server evaluates the impact in real time and issues alerts prompting necessary adjustments and resource reallocation.

[0269] The server also plays a role in "monitoring technical information," tracking the latest technological trends and quickly incorporating new technologies into the system. This system allows companies to respond flexibly to technological innovation.

[0270] For example, if a user enters "requirements for building a new customer management system," the server can refer to data and technical knowledge from past customer management systems to suggest the optimal solution. An example of a prompt message would be, "Please enter details of the functions required to build the new customer management system." In this way, rapid and accurate analysis of business requirements and better technology selection become possible.

[0271] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0272] Step 1:

[0273] Users input business requests in natural language into a dedicated terminal. These inputs include requests such as enhanced customer management functions and improved data security. The terminal receives these requests as text data, performs formatting checks, and grammatical errors. This prepares the user's input data for transmission to the server.

[0274] Step 2:

[0275] The terminal encrypts verified text data and sends it to the server using a secure communication protocol. Input data is encrypted by the terminal to prevent unauthorized access or tampering during transmission. The encrypted input data is then securely delivered to the server.

[0276] Step 3:

[0277] The server decrypts the received encrypted data and passes it to the natural language processing engine. The NLP engine analyzes the input data using the generative AI model and extracts keywords and dependencies. Through this data processing, it is output as structured requirement data and converted into a format that can be further processed within the server.

[0278] Step 4:

[0279] Based on the structured requirement data, the server refers to the internal database to search for similar past project data. An operation to propose the optimal technical options is performed from the extracted data. The result of this process is generated in the form of selection of programming languages and framework proposals and summarized by the server.

[0280] Step 5:

[0281] The server consolidates multiple requests into one dataset to integrate the requirement data collected across departments. At this stage, the similarity and duplication of requests are detected, and consensus formation is carried out to optimize the processing. As a result of the processing, a prioritized requirement list is output.

[0282] Step 6:

[0283] When a requirement change occurs, the server analyzes the impact using the "Requirement Change Impact Prediction AI". The requirement data before and after the change is compared to evaluate the degree of impact on the entire project. If necessary, alerts are issued to relevant departments and resource redistribution is carried out. The result of the impact assessment is provided to the relevant parties.

[0284] Step 7:

[0285] The server constantly collects and analyzes technology information by leveraging "Technology Trend Monitoring AI". It determines whether to adopt the latest technologies and generates proposals for the adoption of new technologies. As a result, users can obtain information for implementing projects that utilize the latest technologies.

[0286] (Application Example 1)

[0287] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0288] In the manufacturing industry, there is a need for automation to integrate different requirements from each department and operate efficiently on the production line. However, it is difficult to appropriately integrate the requests of different departments, promptly evaluate the impact when changes occur, and select the optimal working procedure. Additionally, it is also necessary to quickly adapt to new technologies and new materials.

[0289] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0290] In this invention, the server includes means for analyzing the business requirements input using natural language processing technology and extracting important elements, means for clarifying the business requirements based on the extracted elements and converting them into specific requirements, and means for analyzing production instructions and selecting the optimal working procedure. As a result, it becomes possible to integrate requirements from different departments, promptly evaluate the impact of changes, and select the optimal working procedure based on the latest technologies.

[0291] "Natural language processing technology" is a technology for analyzing the data of input written or spoken language and handling it as structured information.

[0292] "Business requirements" refer to the conditions and specifications required to perform a specific business.

[0293] "Key elements" refer to the information and conditions that are essential for fulfilling business requirements.

[0294] "Technical options" refer to various technologies that can be adopted to solve a specific problem.

[0295] "Interdepartmental requirements" refer to the unique needs and demands submitted by different departments.

[0296] "Production instructions" refer to information used to specify the manufacturing process, including work procedures and resource allocation.

[0297] A "work procedure" refers to a combination of specific steps and methods for efficiently carrying out a particular manufacturing process.

[0298] "Impact assessment" is the process of analyzing and determining how a change in requirements will affect the entire system and processes.

[0299] This invention is implemented as a system that efficiently analyzes business requirements and supports the selection of appropriate technologies. The user inputs business requirements in natural language using a dedicated terminal. The terminal sends this input to a server. The server analyzes the business requirements using an NLP engine and extracts key elements. Through this analysis, the business requirements are converted into specific requirements.

[0300] The server uses the extracted elements to reference past project data and present the optimal technical options. If production instructions are included, the server optimizes the work procedures and makes suggestions for efficient execution on the production line. These suggestions include technology selection and procedure selection.

[0301] In addition, the server has the function of integrating requirements sent between departments and generating a prioritized list. When a change in requirements occurs, it evaluates in real time the impact of the change on the overall process and conducts an impact analysis. At the same time, it constantly monitors the latest technical information and reflects new technologies in the system as needed.

[0302] As a specific example, when requirements for creating a prototype of a new product are input, the server analyzes the requirements and selects and proposes the optimal materials and processes. If the use of new materials is determined, the impact is immediately evaluated, necessary adjustments are made, and it can be applied to the exported products.

[0303] Example of prompt sentence:

[0304] "Please input the assembly requirements for the new product: Example 'Assemble the lid of the protein shaker in 3 steps'"

[0305] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0306] Step 1:

[0307] The user uses a dedicated terminal to input business requirements in natural language. This input is done by the user through the terminal, providing specific requirements and wishes in free-form text input. The input data is sent to the server. This becomes the starting point for the subsequent process.

[0308] Step 2:

[0309] The server analyzes the received business requirements using a natural language processing engine. As a specific operation, the server processes the text using the NLP engine and extracts important elements from the requirements. In this step, the raw input data is analyzed and converted into a list of extracted important elements for output.

[0310] Step 3:

[0311] The server references past project data based on the extracted key elements and presents the optimal technical options. The server searches the database to find similar success stories. The input is a list of key elements, and the output is a list of recommended technologies and methods. A generative AI model is then used to improve the accuracy of the suggestions.

[0312] Step 4:

[0313] The server integrates and prioritizes requirements from each department. At this stage, the server runs an integration algorithm to organize the different needs into a consistent list. It takes a list of requirements from each department as input and outputs it as a unified, prioritized list. This process is designed to resolve inconsistencies that may arise during the coordination process.

[0314] Step 5:

[0315] The server evaluates the impact of requirement changes in real time. It then processes these changes and simulates their impact on the entire system. The input here is the change in requirements, and the output is the predicted impact and recommended adjustments.

[0316] Step 6:

[0317] The server monitors the latest technology information and automatically selects technologies to reflect in the system. Specifically, the server interacts with external technology information databases to incorporate new technologies and trends. A continuous feedback loop is used to maintain an up-to-date state. The input is a feed of technology information, and the output is an updated list of technology selections.

[0318] 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.

[0319] This invention is a system that accurately analyzes business requirements and proposes the optimal technical selection, further improving its accuracy by incorporating an emotion engine that recognizes user emotions. Users input business requirements in natural language using a dedicated terminal. This input is sent to a server, which uses natural language processing technology to analyze the business requirements and extract important elements.

[0320] The server is equipped with an emotion engine that recognizes the user's emotions from the input requirements. Based on this emotional information, the interpretation of the requirements is optimized. For example, if the user has an emotion indicating urgency, the server will focus its analysis on that part of the requirements and prioritize suggesting technical options that can be addressed quickly.

[0321] Furthermore, the server adjusts existing technical options based on the user's emotions recognized by the emotion engine, providing suggestions that better align with the user's intentions. It references past project data, incorporates lessons learned from success stories, and delivers optimized solutions. In this process, it becomes easier to address emotional needs and improves user satisfaction.

[0322] For example, if a user indicates that "a quick response is important," the server will select technology that excels at real-time processing. Features for integrating and prioritizing requirements across departments can also be designed to address each department's needs in a balanced way by considering user sentiment.

[0323] When requirements change, the "Requirements Change Impact Prediction AI" is used to evaluate the overall impact of the change, and risk assessment is performed based on user sentiment information obtained from the sentiment engine, and countermeasures are determined. In this way, the introduction of the sentiment engine enables precise analysis of business requirements and rapid technology selection, providing a system that further supports project success.

[0324] The following describes the processing flow.

[0325] Step 1:

[0326] Users input business requirements and expected results in natural language using a dedicated terminal. During input, the terminal also captures the user's facial expressions and voice tone.

[0327] Step 2:

[0328] The terminal sends the entered business requirements data and user sentiment data to the server.

[0329] Step 3:

[0330] The server uses a natural language processing engine to analyze the input business requirements and extract important elements and keywords.

[0331] Step 4:

[0332] The server analyzes user emotional data using an emotion engine to understand the emotional tendencies the user exhibits. This allows for a complementary evaluation of requirements, such as urgency and importance, from an emotional perspective.

[0333] Step 5:

[0334] Based on the extracted elements and sentiment data, the server clarifies business requirements and lists specific priority requirements. This list will reflect emotional needs.

[0335] Step 6:

[0336] The server utilizes "Project Knowledge Utilization AI" to refer to a database of past projects and present the most suitable technical options based on the user's requests and emotional tendencies.

[0337] Step 7:

[0338] The server uses a "cross-departmental requirements unification AI" to integrate overall requirements, including requests from other departments, and prioritizes them while also considering the emotional needs of each department.

[0339] Step 8:

[0340] When requirements change, the server uses an "AI for predicting the impact of requirement changes" to evaluate the impact of the change, and also uses emotional information obtained from an emotional engine to conduct a new risk assessment, thereby providing the most appropriate countermeasure for the user.

[0341] (Example 2)

[0342] 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".

[0343] In today's business environment, business requirements are becoming increasingly complex, and a variety of emotions are influencing them. However, traditional systems often fail to propose technical options without considering user emotions, resulting in insufficient solutions that align with user intentions. Furthermore, they struggle to respond quickly to changes in requirements, lowering project success rates. To address this challenge, a system is needed that incorporates user emotions during the business requirements analysis process and optimizes requirements interpretation.

[0344] 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.

[0345] In this invention, the server includes means for analyzing input business requirements using natural language processing technology and extracting important elements, means for clarifying the business requirements based on the extracted elements and converting them into specific requirements, and sentiment analysis means for recognizing emotions from the input business requirements and optimizing the interpretation based on those emotions. This enables the provision of quick and appropriate technical selections that take into account the user's emotions and allows for the proposal of optimal solutions to business requirements.

[0346] "Natural language processing technology" is a technique that analyzes input text data to understand the meaning and structure of words and phrases.

[0347] "Business requirements" refer to information that indicates the conditions and specifications necessary to execute a particular business process.

[0348] "Emotion analysis" is a technology that uses user input to understand the type and intensity of emotions and utilize that information.

[0349] A "technical option" is a list that enumerates the advantages of technologies and methods that can be used to solve a particular problem.

[0350] A "database" is a software system that systematically stores and manages information, making it easily accessible.

[0351] "Requirements change" refers to the process of adding, modifying, or deleting existing business requirements and specifications.

[0352] "Emotional information" refers to data that indicates a user's emotional state, and is the information necessary for interpreting that information.

[0353] "Risk assessment" is the process of analyzing the risks that may arise when a specific event occurs and evaluating their impact.

[0354] The system for implementing this invention consists of a dedicated terminal, a server, and a communication network connecting them. Users can input business requirements in natural language using the dedicated terminal. The terminal is equipped with a user interface designed to allow users to input information efficiently.

[0355] The terminal transmits the entered business requirements data to the server in real time. The server uses natural language processing technology to process the received data, analyzing and extracting important elements of the input text. This analysis generally utilizes general-purpose NLP libraries and generative AI models to achieve high accuracy.

[0356] Next, the server uses an emotion engine to recognize the user's emotions from the input business requirements. The emotion engine uses common emotion analysis software to perform emotion analysis, optimizing the interpretation of requirements by instantly analyzing the user's emotional information.

[0357] The server references past project data from the database and compares it to successful cases to suggest the most appropriate technical options for the user. This process utilizes a database management system to facilitate data retrieval. The server then provides feedback to the user with the adjusted technical options, making suggestions based on business requirements. Specifically, it uses general database software and incorporates emotional information regarding the user's intent during the feedback process.

[0358] For example, if a user inputs a requirement such as "a quick response is important," the system uses its sentiment analysis function to extract high-priority emotions related to urgency, and then proposes specific real-time processing techniques based on the analysis results.

[0359] An example of a prompt message to be input into the generating AI model is: "There is a need for a rapid response regarding specific business requirements. The user is emphasizing urgency. What is the optimal technical option?"

[0360] With this configuration, the present invention enables efficient analysis of complex business requirements and the selection of the optimal technology while taking user emotions into consideration.

[0361] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0362] Step 1:

[0363] Users input business requirements in natural language using a dedicated terminal. The entered requirements are formatted as text data. The terminal displays guidelines on the screen to assist the user in inputting information and immediately provides feedback to the user if there are any input errors.

[0364] Step 2:

[0365] The terminal sends the entered business requirements data to the server. The data is encrypted via a communication protocol and delivered to the server in real time over a secure network.

[0366] Step 3:

[0367] The server passes the received data to a natural language processing engine to analyze business requirements. The input text data is tokenized, and important elements are extracted. A generative AI model analyzes the meaning of the text based on context, extracting keywords and structuring the requirements.

[0368] Step 4:

[0369] The server uses an emotion engine to recognize the user's emotions from the input business requirements. Emotion analysis software analyzes emotional expressions contained in the text, identifying the type and intensity of those emotions. The analyzed emotion data is used to prioritize in the next processing step.

[0370] Step 5:

[0371] The server optimizes the interpretation of business requirements based on emotional information, prioritizing key elements and emotions. For example, if emotional information including urgency is recognized, it prioritizes interpreting that requirement and considers technical options for immediate response. In this process, it uses generated prompt statements to explore the optimal technical choice.

[0372] Step 6:

[0373] The server references a database of past projects, matching them with successful case studies to propose the optimal technical options. Using a database management system, it searches for highly similar projects and incorporates insights gained from their implementation results. The proposed technical options are generated as information optimized according to business requirements.

[0374] Step 7:

[0375] The server sends the analysis results and proposed technical options to the terminal and provides feedback to the user. The feedback includes the interpreted requirements, related technical proposals, and the rationale behind their selection. The user can then make a final decision based on this feedback.

[0376] (Application Example 2)

[0377] 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."

[0378] In analyzing business requirements, conventional systems cannot consider the user's emotional state, resulting in a problem where optimal technical proposals cannot be made in accordance with the user's intentions and urgency. Furthermore, in physical stores, there is a lack of product and service proposals that utilize customer emotional information, posing a challenge to improving customer satisfaction.

[0379] 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.

[0380] In this invention, the server includes means for analyzing input business requirements using natural language processing technology and extracting important elements; means for recognizing the user's emotional state, interpreting business requirements based on that emotional information, and proposing the optimal technical options; and means for utilizing emotional information to improve the customer experience and propose the optimal products and services. This enables accurate interpretation of business requirements and technical proposals while considering the user's emotions, and in physical stores, it enables improved customer satisfaction through product and service proposals based on customer emotions.

[0381] "Natural language processing technology" is a technique that analyzes input text data and extracts and structures information in a way that is easy for humans to understand.

[0382] "Business requirements" refer to the conditions and specifications necessary to perform a specific task.

[0383] "Recognizing emotional states" means having the ability to judge a person's emotions and changes in those emotions based on their input and circumstances.

[0384] "Technical options" refer to the selection of technologies and methods available to achieve a specific objective.

[0385] "User sentiment information" refers to data about a user's psychological or emotional state, obtained from their actions and statements.

[0386] "Customer experience" refers to the overall satisfaction and impression that customers have of a product or service.

[0387] "Proposing products and services" refers to the act of selecting products and services that are deemed optimal based on the specific needs and desires of a particular customer.

[0388] The system that realizes this invention mainly consists of a server, a user terminal (such as a smartphone or smart glasses), and software modules that are responsible for emotion recognition and natural language processing.

[0389] The server receives business requirements and customer input information sent from user terminals. This information is analyzed using a natural language processing engine. For natural language processing, a generative AI model (e.g., GPT-4) utilizing modern AI technology is used. This model extracts important elements from the input text and clarifies them as business requirements.

[0390] Next, the server utilizes an emotion recognition engine to evaluate the user's emotional state. This engine is particularly adept at capturing emotions related to urgency and priority, and can utilize Microsoft or other cloud-based emotion analysis services. Based on the emotional information, the interpretation of business requirements is optimized, and the server generates the optimal technical options.

[0391] Furthermore, the server accesses a database of past projects and leverages insights from successful cases to propose technologies and solutions that best suit the user's intentions, tailored to their customer experience. In this process, it also makes emotionally-driven suggestions for products and services.

[0392] For example, if a user enters "I urgently need new sneakers," the emotion engine will recognize the urgency and suggest inventory checks at the nearest store and the fastest way to obtain them.

[0393] Examples of prompt statements are as follows:

[0394] User input: 'I urgently need new sneakers.'

[0395] Prompt: 'We are seeking the best product suggestions and store information for this request. Urgency is emphasized in the user's message. Please provide advice that includes a prompt response that takes their feelings into consideration.'

[0396] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0397] Step 1:

[0398] The server receives information from the user's terminal. The input consists of business requirements and requests entered by the user via a smartphone or smart glasses. This includes natural language data in text format, which the server receives and stores appropriately.

[0399] Step 2:

[0400] The server uses a natural language processing engine to analyze the received text data. The input is the text data obtained in step 1. The server passes this input data to a generating AI model, which converts the business requirements into a structured format and extracts the key elements. The output is the analyzed data.

[0401] Step 3:

[0402] The server analyzes the user's emotional state using an emotion recognition engine. The input is the output data from step 2, which contains emotional cues and expressions. The server extracts emotional information using the emotion recognition engine and determines urgency and priority. The output is data related to the user's emotions.

[0403] Step 4:

[0404] The server generates optimal technical options based on emotional information and analyzed business requirements. The input is the output data from steps 2 and 3. The server references a database of past projects and selects the best technical solution based on successful case studies. The output is the proposed technical options.

[0405] Step 5:

[0406] The server uses emotional information to suggest appropriate products and services with the aim of improving the customer experience. The inputs are the emotional data from step 3 and the technical options from step 4. Through this, the server provides optimal purchasing guidance to specific users and suggests specific product information and available services. The output is the product and service suggestions presented to the user.

[0407] 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.

[0408] 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.

[0409] 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.

[0410] [Third Embodiment]

[0411] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0412] 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.

[0413] 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).

[0414] 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.

[0415] 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.

[0416] 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).

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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".

[0423] This invention is a system that efficiently analyzes business requirements and supports the selection of appropriate technologies. It is realized by analyzing the business requirements entered by the user using natural language processing technology. The user enters the requirements in natural language using a dedicated terminal. This input is sent to a server, which uses an NLP engine to structure the requirements and extract important elements.

[0424] Based on the clarified requirements, the server references past project data to present technical options, including similar success stories. This selection is performed by the "Project Knowledge Utilization AI," which suggests suitable programming languages ​​and frameworks for implementation. Furthermore, the server integrates requirements submitted from multiple departments with the help of the "Cross-Departmental Requirements Unification AI," generating a prioritized list of requirements while making adjustments.

[0425] For example, if the sales department requests "enhanced customer management functions" and the IT department requests "improved data security," the server will optimally integrate both requirements and build a feasible set of functions. Furthermore, if requirements change, the "Requirement Change Impact Prediction AI" will evaluate the impact of the change on the overall system in real time and issue alerts for necessary adjustments and resource reallocations.

[0426] Furthermore, the server utilizes "technology trend monitoring AI" to constantly collect the latest technological information and make real-time decisions on adopting new technologies. In this way, the present invention enables rapid and accurate requirements definition and technology selection, supporting project success.

[0427] The following describes the processing flow.

[0428] Step 1:

[0429] Users input their business requirements for development in natural language using a dedicated terminal.

[0430] Step 2:

[0431] The terminal sends the entered business requirements to the server.

[0432] Step 3:

[0433] The server analyzes the received requirements using a natural language processing engine and extracts important elements and keywords from the requirements.

[0434] Step 4:

[0435] The server uses the "Requirements Clarification AI" to extract elements, convert business requirements into specific requirements, and summarize them in an easy-to-understand format.

[0436] Step 5:

[0437] The server uses "Project Knowledge Utilization AI" to refer to a database of past projects and proposes technical options and success stories that are suitable for the clarified requirements.

[0438] Step 6:

[0439] The server uses a "cross-departmental requirements unification AI" to integrate and adjust different requirements provided by multiple departments, creating a unified priority list.

[0440] Step 7:

[0441] When requirements change, the server uses "Requirements Change Impact Prediction AI" to evaluate the impact of the change in real time and calculate the degree of impact on the overall project and the need for resource reallocation.

[0442] Step 8:

[0443] The server uses "technology trend monitoring AI" to monitor the latest technology information and trends, and proposes new technologies to be reflected in the system as needed.

[0444] (Example 1)

[0445] 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."

[0446] Traditional business systems lack effective methods for analyzing business requirements and selecting technologies, leading to inefficiencies in project execution. Furthermore, they struggle to properly integrate requirements from different departments and optimize the overall system. Impact assessments of requirement changes are also inadequate, increasing the risk of project delays and failures. Moreover, there is a need for systems with the flexibility to quickly adapt to changing technological trends.

[0447] 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.

[0448] In this invention, the server includes means for analyzing business requirements using natural language processing technology and extracting core elements, means for referring to past business data and presenting optimal technical options, and means for integrating and prioritizing requirements across departments. This enables efficient analysis of business requirements and accelerated technology selection, as well as optimization of cross-departmental requirements and real-time evaluation of requirement changes. Furthermore, the application of the latest technologies can improve the success rate of projects.

[0449] "Natural language processing technology" refers to computational techniques that analyze text data to understand and generate human language.

[0450] "Business requirements" refer to the specific conditions and demands regarding the functions and performance required in a business process.

[0451] "Core elements" refer to the most important and fundamental elements or points necessary to fulfill business requirements.

[0452] "Past business data" refers to information about records and deliverables of projects that were carried out in the past.

[0453] "Technological options" refer to a collection of technologies, tools, and methods available to meet specific business needs.

[0454] "Interdepartmental requests" refer to business needs and demands originating from different departments or divisions.

[0455] "Prioritizing" refers to determining the order of multiple business needs or tasks based on their importance and urgency.

[0456] "Assessing the impact of requirements changes" is the process of analyzing and evaluating the impact that changes in business requirements have on the entire project.

[0457] "Monitoring technical information" refers to the activity of continuously collecting and evaluating information on the latest technological trends and innovations.

[0458] "Generative AI technology" is a technology that uses artificial intelligence to generate new data and information.

[0459] This invention provides a system for efficiently processing business requests and supporting appropriate technology selection. Users express business requests in natural language and input them into a dedicated terminal. The terminal sends the input requests to a server, encrypting the data using the SSL / TLS protocol to ensure secure data exchange.

[0460] The server uses natural language processing software to analyze incoming business requests. Specifically, it uses generative AI technology to convert the input requests into structured data and extract core elements. This eliminates ambiguity and clarifies the requests.

[0461] Next, the server accesses a database of past projects and references similar projects and success stories to suggest the optimal technical options. Here, technical elements such as programming languages ​​and frameworks are proposed, allowing the user to proceed with their work based on them.

[0462] Furthermore, the server integrates and analyzes differing requests from different departments to prioritize them. This enables efficient business operations aimed at overall optimization. In addition, if requests change, the server evaluates the impact in real time and issues alerts prompting necessary adjustments and resource reallocation.

[0463] The server also plays a role in "monitoring technical information," tracking the latest technological trends and quickly incorporating new technologies into the system. This system allows companies to respond flexibly to technological innovation.

[0464] For example, if a user enters "requirements for building a new customer management system," the server can refer to data and technical knowledge from past customer management systems to suggest the optimal solution. An example of a prompt message would be, "Please enter details of the functions required to build the new customer management system." In this way, rapid and accurate analysis of business requirements and better technology selection become possible.

[0465] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0466] Step 1:

[0467] Users input business requests in natural language into a dedicated terminal. These inputs include requests such as enhanced customer management functions and improved data security. The terminal receives these requests as text data, performs formatting checks, and grammatical errors. This prepares the user's input data for transmission to the server.

[0468] Step 2:

[0469] The terminal encrypts verified text data and sends it to the server using a secure communication protocol. Input data is encrypted by the terminal to prevent unauthorized access or tampering during transmission. The encrypted input data is then securely delivered to the server.

[0470] Step 3:

[0471] The server decrypts the received encrypted data and passes it to the natural language processing engine. The NLP engine analyzes the input data using a generative AI model and extracts keywords and dependencies. This data processing results in structured requirements data, which is then converted into a format that can be further processed within the server.

[0472] Step 4:

[0473] The server uses structured requirements data to search its internal database for similar past project data. From the extracted data, it performs calculations to suggest the optimal technical options. The results of this process are generated in the form of programming language selections and framework suggestions, which are then compiled by the server.

[0474] Step 5:

[0475] The server integrates requirements data collected across departments by combining multiple requests into a single dataset. At this stage, similarities and duplicates of requests are detected, and consensus is reached to optimize processing. As a result of the processing, a prioritized list of requirements is output.

[0476] Step 6:

[0477] When requirements change, the server analyzes the impact using an "AI for predicting the impact of requirement changes." It compares requirement data before and after the change and evaluates the impact on the entire project. If necessary, it issues alerts to the relevant departments and reallocates resources. The results of the impact assessment are provided to the stakeholders.

[0478] Step 7:

[0479] The server utilizes "technology trend monitoring AI" to continuously collect and analyze technical information. It determines whether the latest technologies should be adopted and generates suggestions for adopting new technologies. This allows users to obtain information for implementing projects that utilize the latest technologies.

[0480] (Application Example 1)

[0481] 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."

[0482] In manufacturing, there is a need for automation to integrate diverse requirements from various departments and operate efficiently on the production line. However, it is difficult to properly integrate requests from different departments, immediately assess the impact of changes, and select the optimal work procedures. Furthermore, it is necessary to quickly adapt to new technologies and materials.

[0483] 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.

[0484] In this invention, the server includes means for analyzing input business requirements using natural language processing technology and extracting important elements, means for clarifying the business requirements based on the extracted elements and converting them into specific requirements, and means for analyzing production instructions and selecting the optimal work procedure. This enables the integration of requirements from different departments, rapid evaluation of the impact of changes, and selection of the optimal work procedure based on the latest technology.

[0485] "Natural language processing technology" is a technology that analyzes input written and spoken language data and handles it as structured information.

[0486] "Business requirements" refer to the conditions and specifications necessary to perform a particular task.

[0487] "Key elements" refer to the information and conditions that are essential for fulfilling business requirements.

[0488] "Technical options" refer to various technologies that can be adopted to solve a specific problem.

[0489] "Interdepartmental requirements" refer to the unique needs and demands submitted by different departments.

[0490] "Production instructions" refer to information used to specify the manufacturing process, including work procedures and resource allocation.

[0491] A "work procedure" refers to a combination of specific steps and methods for efficiently carrying out a particular manufacturing process.

[0492] "Impact assessment" is the process of analyzing and determining how a change in requirements will affect the entire system and processes.

[0493] This invention is implemented as a system that efficiently analyzes business requirements and supports the selection of appropriate technologies. The user inputs business requirements in natural language using a dedicated terminal. The terminal sends this input to a server. The server analyzes the business requirements using an NLP engine and extracts key elements. Through this analysis, the business requirements are converted into specific requirements.

[0494] The server uses the extracted elements to reference past project data and present the optimal technical options. If production instructions are included, the server optimizes the work procedures and makes suggestions for efficient execution on the production line. These suggestions include technology selection and procedure selection.

[0495] In addition, the server has the ability to integrate requirements sent from different departments and generate a prioritized list. If requirements change, it evaluates the impact of the change on the overall process in real time and performs an impact analysis. It also constantly monitors the latest technical information and incorporates new technologies into the system as needed.

[0496] For example, if requirements for creating a prototype of a new product are entered, the server analyzes those requirements, selects and proposes the optimal materials and processes. If the use of new materials is decided, the impact can be immediately evaluated, necessary adjustments can be made, and the system can be applied to the exported product.

[0497] Example of a prompt:

[0498] "Please enter the assembly requirements for the new product: Example: 'Assemble the protein shaker lid in 3 steps'"

[0499] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0500] Step 1:

[0501] Users use a dedicated terminal to input business requirements in natural language. This input is done by the user through the terminal, providing specific requirements and preferences as free-form text input. The entered data is sent to the server, which serves as the starting point for subsequent processes.

[0502] Step 2:

[0503] The server analyzes the received business requirements using a natural language processing (NLP) engine. Specifically, the server processes the text using the NLP engine and extracts important elements from the requirements. In this step, the raw input data is analyzed and converted into a list of extracted important elements, which is then output.

[0504] Step 3:

[0505] The server references past project data based on the extracted key elements and presents the optimal technical options. The server searches the database to find similar success stories. The input is a list of key elements, and the output is a list of recommended technologies and methods. A generative AI model is then used to improve the accuracy of the suggestions.

[0506] Step 4:

[0507] The server integrates and prioritizes requirements from each department. At this stage, the server runs an integration algorithm to organize the different needs into a consistent list. It takes a list of requirements from each department as input and outputs it as a unified, prioritized list. This process is designed to resolve inconsistencies that may arise during the coordination process.

[0508] Step 5:

[0509] The server evaluates the impact of requirement changes in real time. It then processes these changes and simulates their impact on the entire system. The input here is the change in requirements, and the output is the predicted impact and recommended adjustments.

[0510] Step 6:

[0511] The server monitors the latest technology information and automatically selects technologies to reflect in the system. Specifically, the server interacts with external technology information databases to incorporate new technologies and trends. A continuous feedback loop is used to maintain an up-to-date state. The input is a feed of technology information, and the output is an updated list of technology selections.

[0512] 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.

[0513] This invention is a system that accurately analyzes business requirements and proposes the optimal technical selection, further improving its accuracy by incorporating an emotion engine that recognizes user emotions. Users input business requirements in natural language using a dedicated terminal. This input is sent to a server, which uses natural language processing technology to analyze the business requirements and extract important elements.

[0514] The server is equipped with an emotion engine that recognizes the user's emotions from the input requirements. Based on this emotional information, the interpretation of the requirements is optimized. For example, if the user has an emotion indicating urgency, the server will focus its analysis on that part of the requirements and prioritize suggesting technical options that can be addressed quickly.

[0515] Furthermore, the server adjusts existing technical options based on the user's emotions recognized by the emotion engine, providing suggestions that better align with the user's intentions. It references past project data, incorporates lessons learned from success stories, and delivers optimized solutions. In this process, it becomes easier to address emotional needs and improves user satisfaction.

[0516] For example, if a user indicates that "a quick response is important," the server will select technology that excels at real-time processing. Features for integrating and prioritizing requirements across departments can also be designed to address each department's needs in a balanced way by considering user sentiment.

[0517] When requirements change, the "Requirements Change Impact Prediction AI" is used to evaluate the overall impact of the change, and risk assessment is performed based on user sentiment information obtained from the sentiment engine, and countermeasures are determined. In this way, the introduction of the sentiment engine enables precise analysis of business requirements and rapid technology selection, providing a system that further supports project success.

[0518] The following describes the processing flow.

[0519] Step 1:

[0520] Users input business requirements and expected results in natural language using a dedicated terminal. During input, the terminal also captures the user's facial expressions and voice tone.

[0521] Step 2:

[0522] The terminal sends the entered business requirements data and user sentiment data to the server.

[0523] Step 3:

[0524] The server uses a natural language processing engine to analyze the input business requirements and extract important elements and keywords.

[0525] Step 4:

[0526] The server analyzes user emotional data using an emotion engine to understand the emotional tendencies the user exhibits. This allows for a complementary evaluation of requirements, such as urgency and importance, from an emotional perspective.

[0527] Step 5:

[0528] Based on the extracted elements and sentiment data, the server clarifies business requirements and lists specific priority requirements. This list will reflect emotional needs.

[0529] Step 6:

[0530] The server utilizes "Project Knowledge Utilization AI" to refer to a database of past projects and present the most suitable technical options based on the user's requests and emotional tendencies.

[0531] Step 7:

[0532] The server uses a "cross-departmental requirements unification AI" to integrate overall requirements, including requests from other departments, and prioritizes them while also considering the emotional needs of each department.

[0533] Step 8:

[0534] When requirements change, the server uses an "AI for predicting the impact of requirement changes" to evaluate the impact of the change, and also uses emotional information obtained from an emotional engine to conduct a new risk assessment, thereby providing the most appropriate countermeasure for the user.

[0535] (Example 2)

[0536] 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."

[0537] In today's business environment, business requirements are becoming increasingly complex, and a variety of emotions are influencing them. However, traditional systems often fail to propose technical options without considering user emotions, resulting in insufficient solutions that align with user intentions. Furthermore, they struggle to respond quickly to changes in requirements, lowering project success rates. To address this challenge, a system is needed that incorporates user emotions during the business requirements analysis process and optimizes requirements interpretation.

[0538] 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.

[0539] In this invention, the server includes means for analyzing input business requirements using natural language processing technology and extracting important elements, means for clarifying the business requirements based on the extracted elements and converting them into specific requirements, and sentiment analysis means for recognizing emotions from the input business requirements and optimizing the interpretation based on those emotions. This enables the provision of quick and appropriate technical selections that take into account the user's emotions and allows for the proposal of optimal solutions to business requirements.

[0540] "Natural language processing technology" is a technique that analyzes input text data to understand the meaning and structure of words and phrases.

[0541] "Business requirements" refer to information that indicates the conditions and specifications necessary to execute a particular business process.

[0542] "Emotion analysis" is a technology that uses user input to understand the type and intensity of emotions and utilize that information.

[0543] A "technical option" is a list that enumerates the advantages of technologies and methods that can be used to solve a particular problem.

[0544] A "database" is a software system that systematically stores and manages information, making it easily accessible.

[0545] "Requirements change" refers to the process of adding, modifying, or deleting existing business requirements and specifications.

[0546] "Emotional information" refers to data that indicates a user's emotional state, and is the information necessary for interpreting that information.

[0547] "Risk assessment" is the process of analyzing the risks that may arise when a specific event occurs and evaluating their impact.

[0548] The system for implementing this invention consists of a dedicated terminal, a server, and a communication network connecting them. Users can input business requirements in natural language using the dedicated terminal. The terminal is equipped with a user interface designed to allow users to input information efficiently.

[0549] The terminal transmits the entered business requirements data to the server in real time. The server uses natural language processing technology to process the received data, analyzing and extracting important elements of the input text. This analysis generally utilizes general-purpose NLP libraries and generative AI models to achieve high accuracy.

[0550] Next, the server uses an emotion engine to recognize the user's emotions from the input business requirements. The emotion engine uses common emotion analysis software to perform emotion analysis, optimizing the interpretation of requirements by instantly analyzing the user's emotional information.

[0551] The server references past project data from the database and compares it to successful cases to suggest the most appropriate technical options for the user. This process utilizes a database management system to facilitate data retrieval. The server then provides feedback to the user with the adjusted technical options, making suggestions based on business requirements. Specifically, it uses general database software and incorporates emotional information regarding the user's intent during the feedback process.

[0552] For example, if a user inputs a requirement such as "a quick response is important," the system uses its sentiment analysis function to extract high-priority emotions related to urgency, and then proposes specific real-time processing techniques based on the analysis results.

[0553] An example of a prompt message to be input into the generating AI model is: "There is a need for a rapid response regarding specific business requirements. The user is emphasizing urgency. What is the optimal technical option?"

[0554] With this configuration, the present invention enables efficient analysis of complex business requirements and the selection of the optimal technology while taking user emotions into consideration.

[0555] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0556] Step 1:

[0557] Users input business requirements in natural language using a dedicated terminal. The entered requirements are formatted as text data. The terminal displays guidelines on the screen to assist the user in inputting information and immediately provides feedback to the user if there are any input errors.

[0558] Step 2:

[0559] The terminal sends the entered business requirements data to the server. The data is encrypted via a communication protocol and delivered to the server in real time over a secure network.

[0560] Step 3:

[0561] The server passes the received data to a natural language processing engine to analyze business requirements. The input text data is tokenized, and important elements are extracted. A generative AI model analyzes the meaning of the text based on context, extracting keywords and structuring the requirements.

[0562] Step 4:

[0563] The server uses an emotion engine to recognize the user's emotions from the input business requirements. Emotion analysis software analyzes emotional expressions contained in the text, identifying the type and intensity of those emotions. The analyzed emotion data is used to prioritize in the next processing step.

[0564] Step 5:

[0565] The server optimizes the interpretation of business requirements based on emotional information, prioritizing key elements and emotions. For example, if emotional information including urgency is recognized, it prioritizes interpreting that requirement and considers technical options for immediate response. In this process, it uses generated prompt statements to explore the optimal technical choice.

[0566] Step 6:

[0567] The server references a database of past projects, matching them with successful case studies to propose the optimal technical options. Using a database management system, it searches for highly similar projects and incorporates insights gained from their implementation results. The proposed technical options are generated as information optimized according to business requirements.

[0568] Step 7:

[0569] The server sends the analysis results and proposed technical options to the terminal and provides feedback to the user. The feedback includes the interpreted requirements, related technical proposals, and the rationale behind their selection. The user can then make a final decision based on this feedback.

[0570] (Application Example 2)

[0571] 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."

[0572] In analyzing business requirements, conventional systems cannot consider the user's emotional state, resulting in a problem where optimal technical proposals cannot be made in accordance with the user's intentions and urgency. Furthermore, in physical stores, there is a lack of product and service proposals that utilize customer emotional information, posing a challenge to improving customer satisfaction.

[0573] 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.

[0574] In this invention, the server includes means for analyzing input business requirements using natural language processing technology and extracting important elements; means for recognizing the user's emotional state, interpreting business requirements based on that emotional information, and proposing the optimal technical options; and means for utilizing emotional information to improve the customer experience and propose the optimal products and services. This enables accurate interpretation of business requirements and technical proposals while considering the user's emotions, and in physical stores, it enables improved customer satisfaction through product and service proposals based on customer emotions.

[0575] "Natural language processing technology" is a technique that analyzes input text data and extracts and structures information in a way that is easy for humans to understand.

[0576] "Business requirements" refer to the conditions and specifications necessary to perform a specific task.

[0577] "Recognizing emotional states" means having the ability to judge a person's emotions and changes in those emotions based on their input and circumstances.

[0578] "Technical options" refer to the selection of technologies and methods available to achieve a specific objective.

[0579] "User sentiment information" refers to data about a user's psychological or emotional state, obtained from their actions and statements.

[0580] "Customer experience" refers to the overall satisfaction and impression that customers have of a product or service.

[0581] "Proposing products and services" refers to the act of selecting products and services that are deemed optimal based on the specific needs and desires of a particular customer.

[0582] The system that realizes this invention mainly consists of a server, a user terminal (such as a smartphone or smart glasses), and software modules that are responsible for emotion recognition and natural language processing.

[0583] The server receives business requirements and customer input information sent from user terminals. This information is analyzed using a natural language processing engine. For natural language processing, a generative AI model (e.g., GPT-4) utilizing modern AI technology is used. This model extracts important elements from the input text and clarifies them as business requirements.

[0584] Next, the server utilizes an emotion recognition engine to evaluate the user's emotional state. This engine is particularly adept at capturing emotions related to urgency and priority, and can utilize Microsoft or other cloud-based emotion analysis services. Based on the emotional information, the interpretation of business requirements is optimized, and the server generates the optimal technical options.

[0585] Furthermore, the server accesses a database of past projects and leverages insights from successful cases to propose technologies and solutions that best suit the user's intentions, tailored to their customer experience. In this process, it also makes emotionally-driven suggestions for products and services.

[0586] For example, if a user enters "I urgently need new sneakers," the emotion engine will recognize the urgency and suggest inventory checks at the nearest store and the fastest way to obtain them.

[0587] Examples of prompt statements are as follows:

[0588] User input: 'I urgently need new sneakers.'

[0589] Prompt: 'We are seeking the best product suggestions and store information for this request. Urgency is emphasized in the user's message. Please provide advice that includes a prompt response that takes their feelings into consideration.'

[0590] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0591] Step 1:

[0592] The server receives information from the user's terminal. The input consists of business requirements and requests entered by the user via a smartphone or smart glasses. This includes natural language data in text format, which the server receives and stores appropriately.

[0593] Step 2:

[0594] The server uses a natural language processing engine to analyze the received text data. The input is the text data obtained in step 1. The server passes this input data to a generating AI model, which converts the business requirements into a structured format and extracts the key elements. The output is the analyzed data.

[0595] Step 3:

[0596] The server analyzes the user's emotional state using an emotion recognition engine. The input is the output data from step 2, which contains emotional cues and expressions. The server extracts emotional information using the emotion recognition engine and determines urgency and priority. The output is data related to the user's emotions.

[0597] Step 4:

[0598] The server generates optimal technical options based on emotional information and analyzed business requirements. The input is the output data from steps 2 and 3. The server references a database of past projects and selects the best technical solution based on successful case studies. The output is the proposed technical options.

[0599] Step 5:

[0600] The server uses emotional information to suggest appropriate products and services with the aim of improving the customer experience. The inputs are the emotional data from step 3 and the technical options from step 4. Through this, the server provides optimal purchasing guidance to specific users and suggests specific product information and available services. The output is the product and service suggestions presented to the user.

[0601] 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.

[0602] 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.

[0603] 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.

[0604] [Fourth Embodiment]

[0605] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0606] 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.

[0607] 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).

[0608] 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.

[0609] 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.

[0610] 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).

[0611] 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.

[0612] 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.

[0613] 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.

[0614] 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.

[0615] 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.

[0616] 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.

[0617] 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".

[0618] This invention is a system that efficiently analyzes business requirements and supports the selection of appropriate technologies. It is realized by analyzing the business requirements entered by the user using natural language processing technology. The user enters the requirements in natural language using a dedicated terminal. This input is sent to a server, which uses an NLP engine to structure the requirements and extract important elements.

[0619] Based on the clarified requirements, the server references past project data to present technical options, including similar success stories. This selection is performed by the "Project Knowledge Utilization AI," which suggests suitable programming languages ​​and frameworks for implementation. Furthermore, the server integrates requirements submitted from multiple departments with the help of the "Cross-Departmental Requirements Unification AI," generating a prioritized list of requirements while making adjustments.

[0620] For example, if the sales department requests "enhanced customer management functions" and the IT department requests "improved data security," the server will optimally integrate both requirements and build a feasible set of functions. Furthermore, if requirements change, the "Requirement Change Impact Prediction AI" will evaluate the impact of the change on the overall system in real time and issue alerts for necessary adjustments and resource reallocations.

[0621] Furthermore, the server utilizes "technology trend monitoring AI" to constantly collect the latest technological information and make real-time decisions on adopting new technologies. In this way, the present invention enables rapid and accurate requirements definition and technology selection, supporting project success.

[0622] The following describes the processing flow.

[0623] Step 1:

[0624] Users input their business requirements for development in natural language using a dedicated terminal.

[0625] Step 2:

[0626] The terminal sends the entered business requirements to the server.

[0627] Step 3:

[0628] The server analyzes the received requirements using a natural language processing engine and extracts important elements and keywords from the requirements.

[0629] Step 4:

[0630] The server uses the "Requirements Clarification AI" to extract elements, convert business requirements into specific requirements, and summarize them in an easy-to-understand format.

[0631] Step 5:

[0632] The server uses "Project Knowledge Utilization AI" to refer to a database of past projects and proposes technical options and success stories that are suitable for the clarified requirements.

[0633] Step 6:

[0634] The server uses a "cross-departmental requirements unification AI" to integrate and adjust different requirements provided by multiple departments, creating a unified priority list.

[0635] Step 7:

[0636] When requirements change, the server uses "Requirements Change Impact Prediction AI" to evaluate the impact of the change in real time and calculate the degree of impact on the overall project and the need for resource reallocation.

[0637] Step 8:

[0638] The server uses "technology trend monitoring AI" to monitor the latest technology information and trends, and proposes new technologies to be reflected in the system as needed.

[0639] (Example 1)

[0640] 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".

[0641] Traditional business systems lack effective methods for analyzing business requirements and selecting technologies, leading to inefficiencies in project execution. Furthermore, they struggle to properly integrate requirements from different departments and optimize the overall system. Impact assessments of requirement changes are also inadequate, increasing the risk of project delays and failures. Moreover, there is a need for systems with the flexibility to quickly adapt to changing technological trends.

[0642] 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.

[0643] In this invention, the server includes means for analyzing business requirements using natural language processing technology and extracting core elements, means for referring to past business data and presenting optimal technical options, and means for integrating and prioritizing requirements across departments. This enables efficient analysis of business requirements and accelerated technology selection, as well as optimization of cross-departmental requirements and real-time evaluation of requirement changes. Furthermore, the application of the latest technologies can improve the success rate of projects.

[0644] "Natural language processing technology" refers to computational techniques that analyze text data to understand and generate human language.

[0645] "Business requirements" refer to the specific conditions and demands regarding the functions and performance required in a business process.

[0646] "Core elements" refer to the most important and fundamental elements or points necessary to fulfill business requirements.

[0647] "Past business data" refers to information about records and deliverables of projects that were carried out in the past.

[0648] "Technological options" refer to a collection of technologies, tools, and methods available to meet specific business needs.

[0649] "Interdepartmental requests" refer to business needs and demands originating from different departments or divisions.

[0650] "Prioritizing" refers to determining the order of multiple business needs or tasks based on their importance and urgency.

[0651] "Assessing the impact of requirements changes" is the process of analyzing and evaluating the impact that changes in business requirements have on the entire project.

[0652] "Monitoring technical information" refers to the activity of continuously collecting and evaluating information on the latest technological trends and innovations.

[0653] "Generative AI technology" is a technology that uses artificial intelligence to generate new data and information.

[0654] This invention provides a system for efficiently processing business requests and supporting appropriate technology selection. Users express business requests in natural language and input them into a dedicated terminal. The terminal sends the input requests to a server, encrypting the data using the SSL / TLS protocol to ensure secure data exchange.

[0655] The server uses natural language processing software to analyze incoming business requests. Specifically, it uses generative AI technology to convert the input requests into structured data and extract core elements. This eliminates ambiguity and clarifies the requests.

[0656] Next, the server accesses a database of past projects and references similar projects and success stories to suggest the optimal technical options. Here, technical elements such as programming languages ​​and frameworks are proposed, allowing the user to proceed with their work based on them.

[0657] Furthermore, the server integrates and analyzes differing requests from different departments to prioritize them. This enables efficient business operations aimed at overall optimization. In addition, if requests change, the server evaluates the impact in real time and issues alerts prompting necessary adjustments and resource reallocation.

[0658] The server also plays a role in "monitoring technical information," tracking the latest technological trends and quickly incorporating new technologies into the system. This system allows companies to respond flexibly to technological innovation.

[0659] For example, if a user enters "requirements for building a new customer management system," the server can refer to data and technical knowledge from past customer management systems to suggest the optimal solution. An example of a prompt message would be, "Please enter details of the functions required to build the new customer management system." In this way, rapid and accurate analysis of business requirements and better technology selection become possible.

[0660] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0661] Step 1:

[0662] Users input business requests in natural language into a dedicated terminal. These inputs include requests such as enhanced customer management functions and improved data security. The terminal receives these requests as text data, performs formatting checks, and grammatical errors. This prepares the user's input data for transmission to the server.

[0663] Step 2:

[0664] The terminal encrypts verified text data and sends it to the server using a secure communication protocol. Input data is encrypted by the terminal to prevent unauthorized access or tampering during transmission. The encrypted input data is then securely delivered to the server.

[0665] Step 3:

[0666] The server decrypts the received encrypted data and passes it to the natural language processing engine. The NLP engine analyzes the input data using a generative AI model and extracts keywords and dependencies. This data processing results in structured requirements data, which is then converted into a format that can be further processed within the server.

[0667] Step 4:

[0668] The server uses structured requirements data to search its internal database for similar past project data. From the extracted data, it performs calculations to suggest the optimal technical options. The results of this process are generated in the form of programming language selections and framework suggestions, which are then compiled by the server.

[0669] Step 5:

[0670] The server integrates requirements data collected across departments by combining multiple requests into a single dataset. At this stage, similarities and duplicates of requests are detected, and consensus is reached to optimize processing. As a result of the processing, a prioritized list of requirements is output.

[0671] Step 6:

[0672] When requirements change, the server analyzes the impact using an "AI for predicting the impact of requirement changes." It compares requirement data before and after the change and evaluates the impact on the entire project. If necessary, it issues alerts to the relevant departments and reallocates resources. The results of the impact assessment are provided to the stakeholders.

[0673] Step 7:

[0674] The server utilizes "technology trend monitoring AI" to continuously collect and analyze technical information. It determines whether the latest technologies should be adopted and generates suggestions for adopting new technologies. This allows users to obtain information for implementing projects that utilize the latest technologies.

[0675] (Application Example 1)

[0676] 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".

[0677] In manufacturing, there is a need for automation to integrate diverse requirements from various departments and operate efficiently on the production line. However, it is difficult to properly integrate requests from different departments, immediately assess the impact of changes, and select the optimal work procedures. Furthermore, it is necessary to quickly adapt to new technologies and materials.

[0678] 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.

[0679] In this invention, the server includes means for analyzing input business requirements using natural language processing technology and extracting important elements, means for clarifying the business requirements based on the extracted elements and converting them into specific requirements, and means for analyzing production instructions and selecting the optimal work procedure. This enables the integration of requirements from different departments, rapid evaluation of the impact of changes, and selection of the optimal work procedure based on the latest technology.

[0680] "Natural language processing technology" is a technology that analyzes input written and spoken language data and handles it as structured information.

[0681] "Business requirements" refer to the conditions and specifications necessary to perform a particular task.

[0682] "Key elements" refer to the information and conditions that are essential for fulfilling business requirements.

[0683] "Technical options" refer to various technologies that can be adopted to solve a specific problem.

[0684] "Interdepartmental requirements" refer to the unique needs and demands submitted by different departments.

[0685] "Production instructions" refer to information used to specify the manufacturing process, including work procedures and resource allocation.

[0686] A "work procedure" refers to a combination of specific steps and methods for efficiently carrying out a particular manufacturing process.

[0687] "Impact assessment" is the process of analyzing and determining how a change in requirements will affect the entire system and processes.

[0688] This invention is implemented as a system that efficiently analyzes business requirements and supports the selection of appropriate technologies. The user inputs business requirements in natural language using a dedicated terminal. The terminal sends this input to a server. The server analyzes the business requirements using an NLP engine and extracts key elements. Through this analysis, the business requirements are converted into specific requirements.

[0689] The server uses the extracted elements to reference past project data and present the optimal technical options. If production instructions are included, the server optimizes the work procedures and makes suggestions for efficient execution on the production line. These suggestions include technology selection and procedure selection.

[0690] In addition, the server has the ability to integrate requirements sent from different departments and generate a prioritized list. If requirements change, it evaluates the impact of the change on the overall process in real time and performs an impact analysis. It also constantly monitors the latest technical information and incorporates new technologies into the system as needed.

[0691] For example, if requirements for creating a prototype of a new product are entered, the server analyzes those requirements, selects and proposes the optimal materials and processes. If the use of new materials is decided, the impact can be immediately evaluated, necessary adjustments can be made, and the system can be applied to the exported product.

[0692] Example of a prompt:

[0693] "Please enter the assembly requirements for the new product: Example: 'Assemble the protein shaker lid in 3 steps'"

[0694] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0695] Step 1:

[0696] Users use a dedicated terminal to input business requirements in natural language. This input is done by the user through the terminal, providing specific requirements and preferences as free-form text input. The entered data is sent to the server, which serves as the starting point for subsequent processes.

[0697] Step 2:

[0698] The server analyzes the received business requirements using a natural language processing (NLP) engine. Specifically, the server processes the text using the NLP engine and extracts important elements from the requirements. In this step, the raw input data is analyzed and converted into a list of extracted important elements, which is then output.

[0699] Step 3:

[0700] The server references past project data based on the extracted key elements and presents the optimal technical options. The server searches the database to find similar success stories. The input is a list of key elements, and the output is a list of recommended technologies and methods. A generative AI model is then used to improve the accuracy of the suggestions.

[0701] Step 4:

[0702] The server integrates and prioritizes requirements from each department. At this stage, the server runs an integration algorithm to organize the different needs into a consistent list. It takes a list of requirements from each department as input and outputs it as a unified, prioritized list. This process is designed to resolve inconsistencies that may arise during the coordination process.

[0703] Step 5:

[0704] The server evaluates the impact of requirement changes in real time. It then processes these changes and simulates their impact on the entire system. The input here is the change in requirements, and the output is the predicted impact and recommended adjustments.

[0705] Step 6:

[0706] The server monitors the latest technology information and automatically selects technologies to reflect in the system. Specifically, the server interacts with external technology information databases to incorporate new technologies and trends. A continuous feedback loop is used to maintain an up-to-date state. The input is a feed of technology information, and the output is an updated list of technology selections.

[0707] 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.

[0708] This invention is a system that accurately analyzes business requirements and proposes the optimal technical selection, further improving its accuracy by incorporating an emotion engine that recognizes user emotions. Users input business requirements in natural language using a dedicated terminal. This input is sent to a server, which uses natural language processing technology to analyze the business requirements and extract important elements.

[0709] The server is equipped with an emotion engine that recognizes the user's emotions from the input requirements. Based on this emotional information, the interpretation of the requirements is optimized. For example, if the user has an emotion indicating urgency, the server will focus its analysis on that part of the requirements and prioritize suggesting technical options that can be addressed quickly.

[0710] Furthermore, the server adjusts existing technical options based on the user's emotions recognized by the emotion engine, providing suggestions that better align with the user's intentions. It references past project data, incorporates lessons learned from success stories, and delivers optimized solutions. In this process, it becomes easier to address emotional needs and improves user satisfaction.

[0711] For example, if a user indicates that "a quick response is important," the server will select technology that excels at real-time processing. Features for integrating and prioritizing requirements across departments can also be designed to address each department's needs in a balanced way by considering user sentiment.

[0712] When requirements change, the "Requirements Change Impact Prediction AI" is used to evaluate the overall impact of the change, and risk assessment is performed based on user sentiment information obtained from the sentiment engine, and countermeasures are determined. In this way, the introduction of the sentiment engine enables precise analysis of business requirements and rapid technology selection, providing a system that further supports project success.

[0713] The following describes the processing flow.

[0714] Step 1:

[0715] Users input business requirements and expected results in natural language using a dedicated terminal. During input, the terminal also captures the user's facial expressions and voice tone.

[0716] Step 2:

[0717] The terminal sends the entered business requirements data and user sentiment data to the server.

[0718] Step 3:

[0719] The server uses a natural language processing engine to analyze the input business requirements and extract important elements and keywords.

[0720] Step 4:

[0721] The server analyzes user emotional data using an emotion engine to understand the emotional tendencies the user exhibits. This allows for a complementary evaluation of requirements, such as urgency and importance, from an emotional perspective.

[0722] Step 5:

[0723] Based on the extracted elements and sentiment data, the server clarifies business requirements and lists specific priority requirements. This list will reflect emotional needs.

[0724] Step 6:

[0725] The server utilizes "Project Knowledge Utilization AI" to refer to a database of past projects and present the most suitable technical options based on the user's requests and emotional tendencies.

[0726] Step 7:

[0727] The server uses a "cross-departmental requirements unification AI" to integrate overall requirements, including requests from other departments, and prioritizes them while also considering the emotional needs of each department.

[0728] Step 8:

[0729] When requirements change, the server uses an "AI for predicting the impact of requirement changes" to evaluate the impact of the change, and also uses emotional information obtained from an emotional engine to conduct a new risk assessment, thereby providing the most appropriate countermeasure for the user.

[0730] (Example 2)

[0731] 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".

[0732] In today's business environment, business requirements are becoming increasingly complex, and a variety of emotions are influencing them. However, traditional systems often fail to propose technical options without considering user emotions, resulting in insufficient solutions that align with user intentions. Furthermore, they struggle to respond quickly to changes in requirements, lowering project success rates. To address this challenge, a system is needed that incorporates user emotions during the business requirements analysis process and optimizes requirements interpretation.

[0733] 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.

[0734] In this invention, the server includes means for analyzing input business requirements using natural language processing technology and extracting important elements, means for clarifying the business requirements based on the extracted elements and converting them into specific requirements, and sentiment analysis means for recognizing emotions from the input business requirements and optimizing the interpretation based on those emotions. This enables the provision of quick and appropriate technical selections that take into account the user's emotions and allows for the proposal of optimal solutions to business requirements.

[0735] "Natural language processing technology" is a technique that analyzes input text data to understand the meaning and structure of words and phrases.

[0736] "Business requirements" refer to information that indicates the conditions and specifications necessary to execute a particular business process.

[0737] "Emotion analysis" is a technology that uses user input to understand the type and intensity of emotions and utilize that information.

[0738] A "technical option" is a list that enumerates the advantages of technologies and methods that can be used to solve a particular problem.

[0739] A "database" is a software system that systematically stores and manages information, making it easily accessible.

[0740] "Requirements change" refers to the process of adding, modifying, or deleting existing business requirements and specifications.

[0741] "Emotional information" refers to data that indicates a user's emotional state, and is the information necessary for interpreting that information.

[0742] "Risk assessment" is the process of analyzing the risks that may arise when a specific event occurs and evaluating their impact.

[0743] The system for implementing this invention consists of a dedicated terminal, a server, and a communication network connecting them. Users can input business requirements in natural language using the dedicated terminal. The terminal is equipped with a user interface designed to allow users to input information efficiently.

[0744] The terminal transmits the entered business requirements data to the server in real time. The server uses natural language processing technology to process the received data, analyzing and extracting important elements of the input text. This analysis generally utilizes general-purpose NLP libraries and generative AI models to achieve high accuracy.

[0745] Next, the server uses an emotion engine to recognize the user's emotions from the input business requirements. The emotion engine uses common emotion analysis software to perform emotion analysis, optimizing the interpretation of requirements by instantly analyzing the user's emotional information.

[0746] The server references past project data from the database and compares it to successful cases to suggest the most appropriate technical options for the user. This process utilizes a database management system to facilitate data retrieval. The server then provides feedback to the user with the adjusted technical options, making suggestions based on business requirements. Specifically, it uses general database software and incorporates emotional information regarding the user's intent during the feedback process.

[0747] For example, if a user inputs a requirement such as "a quick response is important," the system uses its sentiment analysis function to extract high-priority emotions related to urgency, and then proposes specific real-time processing techniques based on the analysis results.

[0748] An example of a prompt message to be input into the generating AI model is: "There is a need for a rapid response regarding specific business requirements. The user is emphasizing urgency. What is the optimal technical option?"

[0749] With this configuration, the present invention enables efficient analysis of complex business requirements and the selection of the optimal technology while taking user emotions into consideration.

[0750] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0751] Step 1:

[0752] Users input business requirements in natural language using a dedicated terminal. The entered requirements are formatted as text data. The terminal displays guidelines on the screen to assist the user in inputting information and immediately provides feedback to the user if there are any input errors.

[0753] Step 2:

[0754] The terminal sends the entered business requirements data to the server. The data is encrypted via a communication protocol and delivered to the server in real time over a secure network.

[0755] Step 3:

[0756] The server passes the received data to a natural language processing engine to analyze business requirements. The input text data is tokenized, and important elements are extracted. A generative AI model analyzes the meaning of the text based on context, extracting keywords and structuring the requirements.

[0757] Step 4:

[0758] The server uses an emotion engine to recognize the user's emotions from the input business requirements. Emotion analysis software analyzes emotional expressions contained in the text, identifying the type and intensity of those emotions. The analyzed emotion data is used to prioritize in the next processing step.

[0759] Step 5:

[0760] The server optimizes the interpretation of business requirements based on emotional information, prioritizing key elements and emotions. For example, if emotional information including urgency is recognized, it prioritizes interpreting that requirement and considers technical options for immediate response. In this process, it uses generated prompt statements to explore the optimal technical choice.

[0761] Step 6:

[0762] The server references a database of past projects, matching them with successful case studies to propose the optimal technical options. Using a database management system, it searches for highly similar projects and incorporates insights gained from their implementation results. The proposed technical options are generated as information optimized according to business requirements.

[0763] Step 7:

[0764] The server sends the analysis results and proposed technical options to the terminal and provides feedback to the user. The feedback includes the interpreted requirements, related technical proposals, and the rationale behind their selection. The user can then make a final decision based on this feedback.

[0765] (Application Example 2)

[0766] 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".

[0767] In analyzing business requirements, conventional systems cannot consider the user's emotional state, resulting in a problem where optimal technical proposals cannot be made in accordance with the user's intentions and urgency. Furthermore, in physical stores, there is a lack of product and service proposals that utilize customer emotional information, posing a challenge to improving customer satisfaction.

[0768] 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.

[0769] In this invention, the server includes means for analyzing input business requirements using natural language processing technology and extracting important elements; means for recognizing the user's emotional state, interpreting business requirements based on that emotional information, and proposing the optimal technical options; and means for utilizing emotional information to improve the customer experience and propose the optimal products and services. This enables accurate interpretation of business requirements and technical proposals while considering the user's emotions, and in physical stores, it enables improved customer satisfaction through product and service proposals based on customer emotions.

[0770] "Natural language processing technology" is a technique that analyzes input text data and extracts and structures information in a way that is easy for humans to understand.

[0771] "Business requirements" refer to the conditions and specifications necessary to perform a specific task.

[0772] "Recognizing emotional states" means having the ability to judge a person's emotions and changes in those emotions based on their input and circumstances.

[0773] "Technical options" refer to the selection of technologies and methods available to achieve a specific objective.

[0774] "User sentiment information" refers to data about a user's psychological or emotional state, obtained from their actions and statements.

[0775] "Customer experience" refers to the overall satisfaction and impression that customers have of a product or service.

[0776] "Proposing products and services" refers to the act of selecting products and services that are deemed optimal based on the specific needs and desires of a particular customer.

[0777] The system that realizes this invention mainly consists of a server, a user terminal (such as a smartphone or smart glasses), and software modules that are responsible for emotion recognition and natural language processing.

[0778] The server receives business requirements and customer input information sent from user terminals. This information is analyzed using a natural language processing engine. For natural language processing, a generative AI model (e.g., GPT-4) utilizing modern AI technology is used. This model extracts important elements from the input text and clarifies them as business requirements.

[0779] Next, the server utilizes an emotion recognition engine to evaluate the user's emotional state. This engine is particularly adept at capturing emotions related to urgency and priority, and can utilize Microsoft or other cloud-based emotion analysis services. Based on the emotional information, the interpretation of business requirements is optimized, and the server generates the optimal technical options.

[0780] Furthermore, the server accesses a database of past projects and leverages insights from successful cases to propose technologies and solutions that best suit the user's intentions, tailored to their customer experience. In this process, it also makes emotionally-driven suggestions for products and services.

[0781] For example, if a user enters "I urgently need new sneakers," the emotion engine will recognize the urgency and suggest inventory checks at the nearest store and the fastest way to obtain them.

[0782] Examples of prompt statements are as follows:

[0783] User input: 'I urgently need new sneakers.'

[0784] Prompt: 'We are seeking the best product suggestions and store information for this request. Urgency is emphasized in the user's message. Please provide advice that includes a prompt response that takes their feelings into consideration.'

[0785] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0786] Step 1:

[0787] The server receives information from the user's terminal. The input consists of business requirements and requests entered by the user via a smartphone or smart glasses. This includes natural language data in text format, which the server receives and stores appropriately.

[0788] Step 2:

[0789] The server uses a natural language processing engine to analyze the received text data. The input is the text data obtained in step 1. The server passes this input data to a generating AI model, which converts the business requirements into a structured format and extracts the key elements. The output is the analyzed data.

[0790] Step 3:

[0791] The server analyzes the user's emotional state using an emotion recognition engine. The input is the output data from step 2, which contains emotional cues and expressions. The server extracts emotional information using the emotion recognition engine and determines urgency and priority. The output is data related to the user's emotions.

[0792] Step 4:

[0793] The server generates optimal technical options based on emotional information and analyzed business requirements. The input is the output data from steps 2 and 3. The server references a database of past projects and selects the best technical solution based on successful case studies. The output is the proposed technical options.

[0794] Step 5:

[0795] The server uses emotional information to suggest appropriate products and services with the aim of improving the customer experience. The inputs are the emotional data from step 3 and the technical options from step 4. Through this, the server provides optimal purchasing guidance to specific users and suggests specific product information and available services. The output is the product and service suggestions presented to the user.

[0796] 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.

[0797] 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.

[0798] 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 robot 414.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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."

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] The following is further disclosed regarding the embodiments described above.

[0818] (Claim 1)

[0819] A means of analyzing input business requirements using natural language processing technology and extracting important elements,

[0820] A means to clarify business requirements based on the extracted elements and convert them into specific requirements,

[0821] A means of referring to past project data and proposing the optimal technical options,

[0822] A means of integrating and prioritizing requirements across departments,

[0823] A means to evaluate in real time the impact of requirement changes on the entire system,

[0824] A system that includes this.

[0825] (Claim 2)

[0826] The system according to claim 1, which monitors the latest technological information and automatically selects technologies to be reflected in the system.

[0827] (Claim 3)

[0828] The system according to claim 1, which utilizes knowledge from past projects and applies successful patterns to the requirements specifications.

[0829] "Example 1"

[0830] (Claim 1)

[0831] A means of analyzing input business requirements using natural language processing technology and extracting core elements,

[0832] A means to clarify business requirements based on the extracted elements and convert them into specific requirements,

[0833] A means of referring to past business data and presenting the optimal technical options,

[0834] A means of integrating and prioritizing requirements across departments,

[0835] A means to evaluate in real time the impact of changes in requirements on the entire system,

[0836] Means for monitoring technical information and applying new technologies to systems,

[0837] A means of encrypting the communication channel and transmitting data,

[0838] An information processing system that includes this.

[0839] (Claim 2)

[0840] An information processing system according to claim 1, which utilizes knowledge gained from past operations and applies successful patterns to the requirements specifications.

[0841] (Claim 3)

[0842] The information processing system according to claim 1, which applies generative AI technology to perform the conversion of requests into structured data.

[0843] "Application Example 1"

[0844] (Claim 1)

[0845] A means of analyzing input business requirements using natural language processing technology and extracting important elements,

[0846] A means to clarify business requirements based on the extracted elements and convert them into specific requirements,

[0847] A means of referring to past project data and proposing the optimal technical options,

[0848] A means of integrating and prioritizing requirements across departments,

[0849] A means to evaluate in real time the impact of requirement changes on the entire system,

[0850] A means of analyzing production instructions and selecting the optimal work procedure,

[0851] A system that includes this.

[0852] (Claim 2)

[0853] The system according to claim 1, which monitors the latest technological information and automatically selects technologies to be reflected in the system.

[0854] (Claim 3)

[0855] The system according to claim 1, which utilizes knowledge from past projects and applies successful patterns to the requirements specifications.

[0856] "Example 2 of combining an emotion engine"

[0857] (Claim 1)

[0858] A means of analyzing input business requirements using natural language processing technology and extracting important elements,

[0859] A means to clarify business requirements based on the extracted elements and convert them into specific requirements,

[0860] A sentiment analysis tool that recognizes emotions from input business requirements and optimizes interpretations based on those emotions,

[0861] A means of referring to past project data and proposing the optimal technical options,

[0862] A means of integrating and prioritizing requirements across departments,

[0863] A means to evaluate in real time the impact of requirement changes on the entire system,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, which uses emotional information to adjust technological options.

[0867] (Claim 3)

[0868] The system according to claim 1, which improves user satisfaction by making suggestions that align with the user's intentions based on emotion recognition.

[0869] "Application example 2 when combining with an emotional engine"

[0870] (Claim 1)

[0871] A means of analyzing input business requirements using natural language processing technology and extracting important elements,

[0872] A means to clarify business requirements based on the extracted elements and convert them into specific requirements,

[0873] A means of referring to past project data and proposing the optimal technical options,

[0874] A means of integrating and prioritizing requirements across departments,

[0875] A means to evaluate in real time the impact of requirement changes on the entire system,

[0876] A means of recognizing the user's emotional state, interpreting business requirements based on that emotional information, and proposing the optimal technical options,

[0877] To improve the customer experience, we utilize emotional information to propose the most suitable products and services.

[0878] A system that includes this.

[0879] (Claim 2)

[0880] The system according to claim 1, which monitors the latest technological information and automatically selects technologies to be reflected in the system.

[0881] (Claim 3)

[0882] The system according to claim 1, which utilizes knowledge from past projects and applies successful patterns to the requirements specifications. [Explanation of Symbols]

[0883] 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 of analyzing input business requirements using natural language processing technology and extracting important elements, A means to clarify business requirements based on the extracted elements and convert them into specific requirements, A means of referring to past project data and proposing the optimal technical options, A means of integrating and prioritizing requirements across departments, A means to evaluate in real time the impact of requirement changes on the entire system, A system that includes this.

2. The system according to claim 1, which monitors the latest technological information and automatically selects technologies to be reflected in the system.

3. The system according to claim 1, which utilizes knowledge from past projects and applies successful patterns to the requirements specifications.

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