system

Abstract

The system according to this embodiment aims to streamline the upstream processes of system development and improve quality. [Solution] The system according to the embodiment comprises a requirements extraction unit, a design unit, and a management unit. The requirements extraction unit analyzes the user's requests and extracts specific requirements. The design unit designs the system based on the requirements extracted by the requirements extraction unit. The management unit monitors the progress of the project based on the design made by the design unit and makes necessary adjustments.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is 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 character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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] In the conventional technology, the processes of requirement definition, design, and project management in the upstream process of system development are not efficiently performed, and there is room for improvement.

[0005] The system according to the embodiment aims to improve the efficiency of the upstream process of system development and enhance the quality.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a requirements extraction unit, a design unit, and a management unit. The requirements extraction unit analyzes user requests and extracts specific requirements. The design unit designs the system based on the requirements extracted by the requirements extraction unit. The management unit monitors the project progress based on the design made by the design unit and makes necessary adjustments. [Effects of the Invention]

[0007] The system according to this embodiment can streamline the upstream processes of system development and improve quality. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The system according to the embodiment of the present invention provides three schemes for organizing the upstream processes of system development by utilizing generative AI. This system aims to streamline processes such as requirements definition, design, and project management, and to achieve higher quality system development. By utilizing generative AI, the upstream processes of system development can be significantly streamlined and quality can be improved. Considering the introduction of generative AI in each of the processes of requirements definition, design, and project management will be an important element in future system development. First, in the requirements definition process, the generative AI analyzes the user's requests and extracts specific requirements. For example, if the user inputs the functions and performance they require from the system in natural language, the generative AI analyzes the content and organizes it into specific requirements. This streamlines the requirements definition process and yields requirements that accurately reflect the user's requests. Next, in the design process, the generative AI designs the system based on the requirements. For example, the generative AI designs the system architecture and data flow based on the requirements. This streamlines the design process and yields a high-quality design. Furthermore, in the project management process, the generative AI monitors the progress of the project and makes necessary adjustments. For example, a generative AI analyzes project progress data and suggests appropriate countermeasures if delays occur. This streamlines the project management process and improves the project's success rate. In this way, utilizing generative AI can significantly streamline the upstream processes of system development and improve quality. Considering the introduction of generative AI in each process of requirements definition, design, and project management will be a crucial element in future system development. This will enable systems to streamline the upstream processes of system development and improve quality.

[0029] The system according to this embodiment comprises a requirements extraction unit, a design unit, and a management unit. The requirements extraction unit analyzes user requests and extracts specific requirements. For example, if the user inputs the functions and performance they require from the system in natural language, the generation AI analyzes the content and organizes it into specific requirements. The requirements extraction unit can also collect user requests through interviews or questionnaires, and the generation AI analyzes the content to extract requirements. Furthermore, the requirements extraction unit can observe user requests, and the generation AI analyzes the content to extract requirements. For example, the requirements extraction unit inputs the functions the user requires from the system in natural language, and the generation AI analyzes the content to extract specific requirements. Furthermore, the requirements extraction unit can also collect user requests through interviews or questionnaires, and the generation AI analyzes the content to extract requirements. Furthermore, the requirements extraction unit can observe user requests, and the generation AI analyzes the content to extract requirements. The design unit designs the system based on the requirements extracted by the requirements extraction unit. For example, the design unit uses the generation AI to design the system architecture and data flow based on the requirements. Furthermore, the design department can use the generative AI to design interfaces based on requirements. Additionally, the design department can use the generative AI to design databases based on requirements. For example, the design department can use the generative AI to design the system architecture based on requirements. The design department can also use the generative AI to design data flows based on requirements. Furthermore, the design department can use the generative AI to design interfaces based on requirements. The management department monitors the project progress based on the design by the design department and makes necessary adjustments. For example, the management department can use the generative AI to analyze project progress data and propose appropriate countermeasures if delays occur. The management department can also use the generative AI to analyze project progress data and propose resource reallocations. Furthermore, the management department can use the generative AI to analyze project progress data and propose schedule changes. For example, the management department can use the generative AI to analyze project progress data and propose appropriate countermeasures if delays occur.Furthermore, the management department can have the generating AI analyze project progress data and propose resource reallocations. Additionally, the management department can have the generating AI analyze project progress data and propose schedule changes. As a result, the system according to this embodiment can streamline the upstream processes of system development and improve quality.

[0030] The requirements extraction unit analyzes user requests and extracts specific requirements. For example, if a user inputs the functions and performance they require from the system in natural language, the generation AI analyzes the content and organizes it into specific requirements. The generation AI utilizes natural language processing technology to grammatically and semantically analyze the user's input and extract important keywords and phrases. This allows for an accurate understanding of user requests and their conversion into specific requirements. The requirements extraction unit can also collect user requests through interviews and surveys, and the generation AI analyzes the content to extract requirements. In interviews, the generation AI asks users questions based on a pre-set list of questions and analyzes the responses in real time. In the case of surveys, the generation AI statistically analyzes the response data to identify common and unique requirements. Furthermore, the requirements extraction unit can also observe user requests, and the generation AI analyzes the content to extract requirements. For example, the user's use of the system can be recorded on video, and the generation AI analyzes the footage to understand the user's behavior patterns and usage. This allows for the extraction of potential requirements that the user has not explicitly stated. The requirements extraction unit can combine these diverse methods to comprehensively understand user requirements and organize them into specific requirements. This improves the accuracy of requirements definition in the early stages of system development and reduces rework in subsequent design and development processes.

[0031] The design department designs the system based on the requirements extracted by the requirements extraction department. For example, the design department uses generative AI to design the system architecture and data flow based on the requirements. The generative AI analyzes the requirements and refers to past design patterns and best practices to propose the optimal system architecture. This enables efficient and scalable system design. The design department can also use generative AI to design interfaces based on the requirements. The generative AI applies usability principles and the latest design guidelines to design an intuitive and easy-to-use user interface based on user requirements. Furthermore, the design department can use generative AI to design databases based on the requirements. The generative AI builds data models and designs efficient database schemas based on the requirements. This optimizes data integrity and performance. For example, the design department can use generative AI to design the system architecture based on the requirements. The generative AI defines the roles and interactions of each component of the system based on the requirements and builds the overall architecture. The design department can also use generative AI to design data flows based on the requirements. The generative AI visualizes the data flow based on the requirements and optimizes data exchange between each process. Furthermore, the design department can also use generative AI to design interfaces based on requirements. The generative AI applies usability principles and the latest design guidelines to design intuitive and user-friendly interfaces based on user requirements. This allows the design department to quickly produce high-quality system designs based on requirements.

[0032] The management department monitors project progress based on the design created by the design department and makes necessary adjustments. For example, the management department uses a generative AI to analyze project progress data and propose appropriate countermeasures if delays occur. The generative AI analyzes data collected from project management tools to understand task progress and resource usage in real time. This allows for the identification of the cause of delays and the implementation of countermeasures quickly. The management department can also use the generative AI to analyze project progress data and propose resource reallocations. The generative AI considers the importance and dependencies of each task and calculates the optimal allocation of resources. This maximizes the overall efficiency of the project and minimizes delays. Furthermore, the management department can use the generative AI to analyze project progress data and propose schedule changes. The generative AI reviews the schedule based on project progress and resource usage and proposes an optimal schedule. For example, the management department uses the generative AI to analyze project progress data and propose appropriate countermeasures if delays occur. The generative AI identifies the cause of delays and proposes specific countermeasures such as resource reallocation and changes in task prioritization. Furthermore, the management department can use the generative AI to analyze project progress data and propose resource reallocations. The generative AI considers the importance and dependencies of each task and calculates the optimal resource allocation. In addition, the management department can use the generative AI to analyze project progress data and propose schedule changes. The generative AI reviews the schedule based on project progress and resource usage and proposes an optimal schedule. This allows the management department to efficiently manage project progress and minimize delays and wasted resources.

[0033] The requirements extraction unit can accept user requests in natural language. For example, if the user inputs the functions and performance they require from the system in natural language, the generation AI analyzes the content and organizes it into specific requirements. The requirements extraction unit can also collect user requests through interviews or questionnaires, and the generation AI analyzes the content to extract requirements. Furthermore, the requirements extraction unit can observe user requests, and the generation AI analyzes the content to extract requirements. This improves the accuracy of requirements extraction by inputting user requests in natural language. Natural language includes, but is not limited to, Japanese, English, and other languages. Some or all of the above-described processes in the requirements extraction unit may be performed using the generation AI, or they may not be performed using the generation AI. For example, the requirements extraction unit can accept user input of functions they require from the system in natural language, and the generation AI analyzes the content to extract specific requirements.

[0034] The design department can design the system architecture and data flow based on the requirements. For example, the design department can use generative AI to design the system architecture based on the requirements. The design department can also use generative AI to design the data flow based on the requirements. Furthermore, the design department can use generative AI to design the interfaces based on the requirements. For example, the design department can use generative AI to design the system architecture based on the requirements. The design department can also use generative AI to design the data flow based on the requirements. Furthermore, the design department can use generative AI to design the interfaces based on the requirements. This allows for high-quality design by designing the system based on the requirements. Examples of architecture include, but are not limited to, client-server models and microservice architectures. Examples of data flow include, but are not limited to, data flow and data transformation processes. Some or all of the above processes in the design department may be performed using generative AI or not. For example, the design department can design the system architecture based on the requirements, and a generative AI can analyze its contents and perform a concrete design.

[0035] The management department can analyze project progress data and propose appropriate measures if delays occur. For example, the management department can use a generative AI to analyze project progress data and propose appropriate measures if delays occur. The management department can also use a generative AI to analyze project progress data and propose resource reallocation. Furthermore, the management department can use a generative AI to analyze project progress data and propose schedule changes. For example, the management department can use a generative AI to analyze project progress data and propose appropriate measures if delays occur. The management department can also use a generative AI to analyze project progress data and propose resource reallocation. Furthermore, the management department can use a generative AI to analyze project progress data and propose schedule changes. This improves the project success rate by monitoring project progress and proposing appropriate measures when delays occur. Progress data includes, but is not limited to, task completion status and schedule achievement. Appropriate measures include, but are not limited to, resource reallocation and schedule changes. Some or all of the above processing in the management department may be performed using a generative AI or not. For example, the management department can input project progress data into a generating AI, which can then analyze the data and propose appropriate countermeasures.

[0036] The requirements extraction unit can analyze a user's past request history and automatically extract similar requirements. For example, the requirements extraction unit's generating AI can extract similar requirements based on functions previously requested by the user. The requirements extraction unit can also analyze a user's past project history and extract common requirements. Furthermore, the requirements extraction unit's generating AI can extract relevant requirements based on feedback previously submitted by the user. For example, the requirements extraction unit's generating AI can extract similar requirements based on functions previously requested by the user. Furthermore, the requirements extraction unit can analyze a user's past project history and extract common requirements. Furthermore, the requirements extraction unit's generating AI can extract relevant requirements based on feedback previously submitted by the user. This allows for the efficient extraction of similar requirements by analyzing past request history. Past request history includes, but is not limited to, database queries and log analysis. Similar requirements include, but is not limited to, text mining and clustering. Some or all of the above processing in the requirements extraction unit may be performed using or without the generating AI. For example, the requirements extraction unit can input the user's past request history into the generation AI, which then analyzes the content to extract similar requirements.

[0037] The requirements extraction unit can organize requirements while considering the user's industry-specific terminology and expressions. For example, the requirements extraction unit can have its generation AI automatically recognize industry-specific terminology used by the user and reflect it in the requirements. The requirements extraction unit can also organize requirements using templates specific to the user's industry. Furthermore, the requirements extraction unit can organize requirements while considering industry-specific regulations and standards. For example, the requirements extraction unit can have its generation AI automatically recognize industry-specific terminology used by the user and reflect it in the requirements. Furthermore, the requirements extraction unit can also organize requirements using templates specific to the user's industry. Furthermore, the requirements extraction unit can organize requirements while considering industry-specific regulations and standards. This makes the requirements organization more accurate by considering industry-specific terminology and expressions. Industry-specific terminology and expressions include, but are not limited to, specialized terminology dictionaries and industry guidelines. Some or all of the above processing in the requirements extraction unit may be performed using or without the generation AI. For example, the requirements extraction unit can input industry-specific terminology used by the user into the generation AI, which can then analyze its content and reflect it in the requirements.

[0038] The requirements extraction unit can prioritize extracting region-specific requirements by considering the user's geographical location information during requirements extraction. For example, if the user is in a specific region, the requirements extraction unit will extract requirements considering the regulations and standards of that region. The requirements extraction unit can also extract requirements that reflect region-specific needs based on the user's geographical location information. Furthermore, the requirements extraction unit can organize requirements by considering the culture and customs of the region. For example, if the user is in a specific region, the requirements extraction unit will extract requirements considering the regulations and standards of that region. The requirements extraction unit can also extract requirements that reflect region-specific needs based on the user's geographical location information. Furthermore, the requirements extraction unit can organize requirements by considering the culture and customs of the region. This allows for the efficient extraction of region-specific requirements by considering geographical location information. Geographical location information includes, but is not limited to, GPS data and address information. Region-specific requirements include, but are not limited to, local laws, culture, and infrastructure. Some or all of the above processing in the requirements extraction unit may be performed using or without generative AI. For example, the requirements extraction unit can input the user's geographical location information into the generating AI, which then analyzes the data to extract region-specific requirements.

[0039] The requirements extraction unit can analyze a user's social media activity and extract relevant requirements during requirements extraction. For example, the requirements extraction unit can analyze a user's social media posts and extract relevant requirements. The requirements extraction unit can also use a generative AI to extract requirements based on the activity of accounts the user follows. Furthermore, the requirements extraction unit can use a generative AI to organize requirements based on the user's social media feedback. For example, the requirements extraction unit can analyze a user's social media posts and extract relevant requirements. The requirements extraction unit can also use a generative AI to extract requirements based on the activity of accounts the user follows. Furthermore, the requirements extraction unit can use a generative AI to organize requirements based on the user's social media feedback. This allows for the efficient extraction of relevant requirements by analyzing social media activity. Social media activity includes, but is not limited to, analysis of post content and follower analysis. Relevant requirements include, but are not limited to, keyword matching and topic modeling. Some or all of the above-described processes in the requirements extraction unit may be performed using a generative AI or not. For example, the requirements extraction unit can input user social media activity data into a generating AI, which then analyzes the data to extract relevant requirements.

[0040] The design department can select the optimal design pattern by referring to past design data during the design process. For example, the design department can use generative AI to select the optimal design pattern based on design data from past successful projects. Alternatively, the design department can analyze data from past failed projects, and the generative AI can select a design pattern that avoids risks. Furthermore, the design department can refer to design data from similar past projects, and the generative AI can provide the optimal design. For example, the design department can use generative AI to select the optimal design pattern based on design data from past successful projects. Alternatively, the design department can analyze data from past failed projects, and the generative AI can select a design pattern that avoids risks. Furthermore, the design department can refer to design data from similar past projects, and the generative AI can provide the optimal design. This allows for the selection of the optimal design pattern by referring to past design data. Past design data includes, but is not limited to, database queries and references to design documents. Optimal design patterns include, but are not limited to, performance, scalability, and maintainability. Some or all of the above processes in the design department may be performed using generative AI or not. For example, the design department can input past design data into a generation AI, which can then analyze the data and select the optimal design pattern.

[0041] The design department can design the system while considering its scalability. For example, the design department can use a generative AI to provide a scalable design that takes into account future increases in the number of users. The design department can also use a generative AI to design the system while considering load balancing. Furthermore, the design department can use a generative AI to provide a design that takes scalability in a cloud environment into account. For example, the design department can use a generative AI to provide a scalable design that takes into account future increases in the number of users. The design department can also use a generative AI to design the system while considering load balancing. Furthermore, the design department can use a generative AI to provide a design that takes scalability in a cloud environment into account. By considering the scalability of the system, future expansion becomes easier. Scalability includes, but is not limited to, load balancing, scalability, and redundancy. Some or all of the above processes in the design department may be performed using a generative AI or not. For example, the design department can input the system's scalability into a generative AI, which then analyzes the content and provides a scalable design.

[0042] The design department can customize the design during the design phase, taking into account the user's industry-specific requirements. For example, the design department can customize the design considering the user's industry-specific regulations. The design department can also use industry-specific templates for the design. Furthermore, the design department can customize the design considering industry-specific terminology and expressions. This allows for a more appropriate design by considering industry-specific requirements. Industry-specific requirements include, but are not limited to, industry guidelines and technical glossaries. Some or all of the above processes in the design department may be performed using or without generative AI. For example, the design department can input the user's industry-specific requirements into a generative AI, which then analyzes the content and customizes the design.

[0043] The design department can reflect region-specific design requirements by considering the user's geographical location information during the design process. For example, if the user is in a specific region, the design department will design while considering the regulations and standards of that region. The design department can also provide designs that reflect region-specific needs based on the user's geographical location information. Furthermore, the design department can customize designs by considering local culture and customs. For example, if the user is in a specific region, the design department will design while considering the regulations and standards of that region. The design department can also provide designs that reflect region-specific needs based on the user's geographical location information. Furthermore, the design department can customize designs by considering local culture and customs. This allows for the efficient reflection of region-specific design requirements by considering geographical location information. Geographical location information includes, but is not limited to, GPS data and address information. Region-specific design requirements include, but are not limited to, local laws, culture, and infrastructure. Some or all of the above processing in the design department may be performed using generative AI or not. For example, the design department can input the user's geographical location information into a generating AI, which can then analyze the data to reflect region-specific design requirements.

[0044] The management department can optimize risk management by referring to past project data during project management. For example, the management department can use generative AI to predict risks and propose countermeasures based on past project data. The management department can also analyze data from past failed projects, and generative AI can propose methods for risk avoidance. Furthermore, the management department can refer to data from past successful projects, and generative AI can provide best practices for risk management. For example, the management department can use generative AI to predict risks and propose countermeasures based on past project data. The management department can also analyze data from past failed projects, and generative AI can propose methods for risk avoidance. Furthermore, the management department can refer to data from past successful projects, and generative AI can provide best practices for risk management. In this way, risk management can be optimized by referring to past project data. Past project data includes, but is not limited to, database queries and project report references. Risk management includes, but is not limited to, risk identification, risk assessment, and risk countermeasures. Some or all of the above processes in the management department may be performed using generative AI or not. For example, the management department can input past project data into a generating AI, which then analyzes the data to optimize risk management.

[0045] The management department can assign tasks while considering the skill sets of team members during project management. For example, the management department can use a generative AI to assign the most suitable tasks based on the skill sets of team members. Alternatively, the management department can refer to the past performance data of team members and have the generative AI assign tasks. Furthermore, the management department can use a generative AI to assign tasks while considering the current skill levels of team members. For example, the management department can use a generative AI to assign the most suitable tasks based on the skill sets of team members. Alternatively, the management department can refer to the past performance data of team members and have the generative AI assign tasks. Furthermore, the management department can use a generative AI to assign tasks while considering the current skill levels of team members. This makes it possible to assign tasks optimally by considering the skill sets of team members. Skill sets include, but are not limited to, technical skills, experience, and qualifications. Task assignment includes, but are not limited to, skill matching, task importance, and task urgency. Some or all of the above processes in the management department may be performed using or without generative AI. For example, the management department can input the skill sets of team members into a generating AI, which can then analyze the data and assign the most suitable tasks.

[0046] The management department can incorporate project management methodologies specific to the user's industry when managing projects. For example, the management department can have a generating AI automatically apply project management methodologies specific to the user's industry. Alternatively, the management department can use industry-specific templates for project management. Furthermore, the management department can manage projects while considering industry-specific regulations and standards. This allows for more appropriate project management by incorporating industry-specific project management methodologies. Some or all of the above processes in the management department may be performed using a generating AI, or not. For example, the management department can input project management methodologies specific to the user's industry into a generating AI, which can then analyze and perform project management.

[0047] The management department can manage region-specific risks by considering the user's geographical location information during project management. For example, if a user is in a specific region, the management department will manage the project while considering the risks of that region. The management department can also predict region-specific risks and propose countermeasures based on the user's geographical location information. Furthermore, the management department can manage risks while considering the local culture and customs. For example, if a user is in a specific region, the management department will manage the project while considering the risks of that region. The management department can also predict region-specific risks and propose countermeasures based on the user's geographical location information. Furthermore, the management department can manage risks while considering the local culture and customs. This allows for efficient management of region-specific risks by considering geographical location information. Geographical location information includes, but is not limited to, GPS data and address information. Region-specific risks include, but are not limited to, natural disasters, infrastructure vulnerabilities, and local laws. Some or all of the above processing by the management department may be performed using generative AI, or not. For example, the management department can input users' geographical location information into a generating AI, which can then analyze the data to manage region-specific risks.

[0048] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0049] The requirements extraction unit can analyze a user's past request history and automatically extract similar requirements. For example, the requirements extraction unit can use a generating AI to extract similar requirements based on features previously requested by the user. The requirements extraction unit can also analyze a user's past project history and extract common requirements. Furthermore, the requirements extraction unit can use a generating AI to extract relevant requirements based on feedback previously submitted by the user. This allows for the efficient extraction of similar requirements by analyzing past request history. Past request history includes, but is not limited to, database queries and log analysis. Similar requirements include, but are not limited to, text mining and clustering. Some or all of the above processing in the requirements extraction unit may be performed using a generating AI or not. For example, the requirements extraction unit can input a user's past request history into a generating AI, which can then analyze its contents and extract similar requirements.

[0050] The design department can select the optimal design pattern by referring to past design data during the design process. For example, the design department can use a generative AI to select the optimal design pattern based on design data from past successful projects. Alternatively, the design department can analyze data from past failed projects, and the generative AI can select a design pattern that avoids risks. Furthermore, the design department can refer to design data from similar past projects, and the generative AI can provide the optimal design. In this way, the optimal design pattern can be selected by referring to past design data. Past design data includes, but is not limited to, database queries and references to design documents. Optimal design patterns include, but are not limited to, performance, scalability, and maintainability. Some or all of the above processes in the design department may be performed using a generative AI, or they may not be performed using a generative AI. For example, the design department can input past design data into a generative AI, which can then analyze its contents and select the optimal design pattern.

[0051] The management department can optimize risk management by referring to past project data during project management. For example, the management department can use generative AI to predict risks and propose countermeasures based on past project data. The management department can also analyze data from past failed projects, and the generative AI can propose ways to avoid risks. Furthermore, the management department can refer to data from past successful projects, and the generative AI can provide best practices for risk management. In this way, risk management can be optimized by referring to past project data. Past project data includes, but is not limited to, database queries and project report references. Risk management includes, but is not limited to, risk identification, risk assessment, and risk countermeasures. Some or all of the above processes in the management department may be performed using generative AI, or not. For example, the management department can input past project data into a generative AI, and the generative AI can analyze its contents to optimize risk management.

[0052] The management department can assign tasks to team members while considering their skill sets during project management. For example, the management department can use a generative AI to assign optimal tasks based on the team members' skill sets. The management department can also refer to the team members' past performance data and have the generative AI assign tasks based on that. Furthermore, the management department can have the generative AI assign tasks while considering the team members' current skill levels. This makes it possible to assign tasks optimally by considering the team members' skill sets. Skill sets include, but are not limited to, technical skills, experience, and qualifications. Task assignments include, but are not limited to, skill matching, task importance, and task urgency. Some or all of the above processes in the management department may be performed using a generative AI, or they may not. For example, the management department can input the team members' skill sets into a generative AI, which can then analyze the data and assign optimal tasks.

[0053] The management department can incorporate project management methodologies specific to the user's industry during project management. For example, the management department can use a generative AI to automatically apply project management methodologies specific to the user's industry. The management department can also use templates tailored to the user's industry for project management. Furthermore, the management department can manage projects while considering industry-specific regulations and standards. This allows for more appropriate project management by incorporating industry-specific project management methodologies. Some or all of the above processes in the management department may be performed using a generative AI, or they may not. For example, the management department can input project management methodologies specific to the user's industry into a generative AI, which can then analyze and perform project management.

[0054] The management department can manage region-specific risks by considering the user's geographical location information during project management. For example, if a user is in a specific region, the management department will manage the project while considering the risks of that region. The management department can also predict region-specific risks and propose countermeasures based on the user's geographical location information. Furthermore, the management department can manage risks by considering local culture and customs. In this way, region-specific risks can be managed efficiently by considering geographical location information. Geographical location information includes, but is not limited to, GPS data and address information. Region-specific risks include, but are not limited to, natural disasters, infrastructure vulnerabilities, and local laws. Some or all of the above processing by the management department may be performed using generative AI, or not using generative AI. For example, the management department can input the user's geographical location information into a generative AI, and the generative AI can analyze the content to manage region-specific risks.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The requirements extraction unit analyzes user requests and extracts specific requirements. For example, if a user inputs the functions and performance they require from the system in natural language, the generating AI analyzes the content and organizes it into specific requirements. The requirements extraction unit can also collect user requests through interviews and questionnaires, and the generating AI analyzes the content to extract requirements. Furthermore, the requirements extraction unit can observe user requests, and the generating AI analyzes the content to extract requirements. Step 2: The design department designs the system based on the requirements extracted by the requirements extraction department. For example, a generative AI designs the system architecture and data flow based on the requirements. The design department can also have the generative AI design the interfaces based on the requirements. Furthermore, the design department can have the generative AI design the database based on the requirements. Step 3: The management department monitors the project's progress based on the design created by the design department and makes necessary adjustments. For example, the generative AI analyzes the project's progress data and proposes appropriate countermeasures if delays occur. The management department can also have the generative AI analyze the project's progress data and propose resource reallocations. Furthermore, the management department can have the generative AI analyze the project's progress data and propose schedule changes.

[0057] (Example of form 2) The system according to the embodiment of the present invention provides three schemes for organizing the upstream processes of system development by utilizing generative AI. This system aims to streamline processes such as requirements definition, design, and project management, and to achieve higher quality system development. By utilizing generative AI, the upstream processes of system development can be significantly streamlined and quality can be improved. Considering the introduction of generative AI in each of the processes of requirements definition, design, and project management will be an important element in future system development. First, in the requirements definition process, the generative AI analyzes the user's requests and extracts specific requirements. For example, if the user inputs the functions and performance they require from the system in natural language, the generative AI analyzes the content and organizes it into specific requirements. This streamlines the requirements definition process and yields requirements that accurately reflect the user's requests. Next, in the design process, the generative AI designs the system based on the requirements. For example, the generative AI designs the system architecture and data flow based on the requirements. This streamlines the design process and yields a high-quality design. Furthermore, in the project management process, the generative AI monitors the progress of the project and makes necessary adjustments. For example, a generative AI analyzes project progress data and suggests appropriate countermeasures if delays occur. This streamlines the project management process and improves the project's success rate. In this way, utilizing generative AI can significantly streamline the upstream processes of system development and improve quality. Considering the introduction of generative AI in each process of requirements definition, design, and project management will be a crucial element in future system development. This will enable systems to streamline the upstream processes of system development and improve quality.

[0058] The system according to this embodiment comprises a requirements extraction unit, a design unit, and a management unit. The requirements extraction unit analyzes user requests and extracts specific requirements. For example, if the user inputs the functions and performance they require from the system in natural language, the generation AI analyzes the content and organizes it into specific requirements. The requirements extraction unit can also collect user requests through interviews or questionnaires, and the generation AI analyzes the content to extract requirements. Furthermore, the requirements extraction unit can observe user requests, and the generation AI analyzes the content to extract requirements. For example, the requirements extraction unit inputs the functions the user requires from the system in natural language, and the generation AI analyzes the content to extract specific requirements. Furthermore, the requirements extraction unit can also collect user requests through interviews or questionnaires, and the generation AI analyzes the content to extract requirements. Furthermore, the requirements extraction unit can observe user requests, and the generation AI analyzes the content to extract requirements. The design unit designs the system based on the requirements extracted by the requirements extraction unit. For example, the design unit uses the generation AI to design the system architecture and data flow based on the requirements. Furthermore, the design department can use the generative AI to design interfaces based on requirements. Additionally, the design department can use the generative AI to design databases based on requirements. For example, the design department can use the generative AI to design the system architecture based on requirements. The design department can also use the generative AI to design data flows based on requirements. Furthermore, the design department can use the generative AI to design interfaces based on requirements. The management department monitors the project progress based on the design by the design department and makes necessary adjustments. For example, the management department can use the generative AI to analyze project progress data and propose appropriate countermeasures if delays occur. The management department can also use the generative AI to analyze project progress data and propose resource reallocations. Furthermore, the management department can use the generative AI to analyze project progress data and propose schedule changes. For example, the management department can use the generative AI to analyze project progress data and propose appropriate countermeasures if delays occur.Furthermore, the management department can have the generating AI analyze project progress data and propose resource reallocations. Additionally, the management department can have the generating AI analyze project progress data and propose schedule changes. As a result, the system according to this embodiment can streamline the upstream processes of system development and improve quality.

[0059] The requirements extraction unit analyzes user requests and extracts specific requirements. For example, if a user inputs the functions and performance they require from the system in natural language, the generation AI analyzes the content and organizes it into specific requirements. The generation AI utilizes natural language processing technology to grammatically and semantically analyze the user's input and extract important keywords and phrases. This allows for an accurate understanding of user requests and their conversion into specific requirements. The requirements extraction unit can also collect user requests through interviews and surveys, and the generation AI analyzes the content to extract requirements. In interviews, the generation AI asks users questions based on a pre-set list of questions and analyzes the responses in real time. In the case of surveys, the generation AI statistically analyzes the response data to identify common and unique requirements. Furthermore, the requirements extraction unit can also observe user requests, and the generation AI analyzes the content to extract requirements. For example, the user's use of the system can be recorded on video, and the generation AI analyzes the footage to understand the user's behavior patterns and usage. This allows for the extraction of potential requirements that the user has not explicitly stated. The requirements extraction unit can combine these diverse methods to comprehensively understand user requirements and organize them into specific requirements. This improves the accuracy of requirements definition in the early stages of system development and reduces rework in subsequent design and development processes.

[0060] The design department designs the system based on the requirements extracted by the requirements extraction department. For example, the design department uses generative AI to design the system architecture and data flow based on the requirements. The generative AI analyzes the requirements and refers to past design patterns and best practices to propose the optimal system architecture. This enables efficient and scalable system design. The design department can also use generative AI to design interfaces based on the requirements. The generative AI applies usability principles and the latest design guidelines to design an intuitive and easy-to-use user interface based on user requirements. Furthermore, the design department can use generative AI to design databases based on the requirements. The generative AI builds data models and designs efficient database schemas based on the requirements. This optimizes data integrity and performance. For example, the design department can use generative AI to design the system architecture based on the requirements. The generative AI defines the roles and interactions of each component of the system based on the requirements and builds the overall architecture. The design department can also use generative AI to design data flows based on the requirements. The generative AI visualizes the data flow based on the requirements and optimizes data exchange between each process. Furthermore, the design department can also use generative AI to design interfaces based on requirements. The generative AI applies usability principles and the latest design guidelines to design intuitive and user-friendly interfaces based on user requirements. This allows the design department to quickly produce high-quality system designs based on requirements.

[0061] The management department monitors project progress based on the design created by the design department and makes necessary adjustments. For example, the management department uses a generative AI to analyze project progress data and propose appropriate countermeasures if delays occur. The generative AI analyzes data collected from project management tools to understand task progress and resource usage in real time. This allows for the identification of the cause of delays and the implementation of countermeasures quickly. The management department can also use the generative AI to analyze project progress data and propose resource reallocations. The generative AI considers the importance and dependencies of each task and calculates the optimal allocation of resources. This maximizes the overall efficiency of the project and minimizes delays. Furthermore, the management department can use the generative AI to analyze project progress data and propose schedule changes. The generative AI reviews the schedule based on project progress and resource usage and proposes an optimal schedule. For example, the management department uses the generative AI to analyze project progress data and propose appropriate countermeasures if delays occur. The generative AI identifies the cause of delays and proposes specific countermeasures such as resource reallocation and changes in task prioritization. Furthermore, the management department can use the generative AI to analyze project progress data and propose resource reallocations. The generative AI considers the importance and dependencies of each task and calculates the optimal resource allocation. In addition, the management department can use the generative AI to analyze project progress data and propose schedule changes. The generative AI reviews the schedule based on project progress and resource usage and proposes an optimal schedule. This allows the management department to efficiently manage project progress and minimize delays and wasted resources.

[0062] The requirements extraction unit can accept user requests in natural language. For example, if the user inputs the functions and performance they require from the system in natural language, the generation AI analyzes the content and organizes it into specific requirements. The requirements extraction unit can also collect user requests through interviews or questionnaires, and the generation AI analyzes the content to extract requirements. Furthermore, the requirements extraction unit can observe user requests, and the generation AI analyzes the content to extract requirements. This improves the accuracy of requirements extraction by inputting user requests in natural language. Natural language includes, but is not limited to, Japanese, English, and other languages. Some or all of the above-described processes in the requirements extraction unit may be performed using the generation AI, or they may not be performed using the generation AI. For example, the requirements extraction unit can accept user input of functions they require from the system in natural language, and the generation AI analyzes the content to extract specific requirements.

[0063] The design department can design the system architecture and data flow based on the requirements. For example, the design department can use generative AI to design the system architecture based on the requirements. The design department can also use generative AI to design the data flow based on the requirements. Furthermore, the design department can use generative AI to design the interfaces based on the requirements. For example, the design department can use generative AI to design the system architecture based on the requirements. The design department can also use generative AI to design the data flow based on the requirements. Furthermore, the design department can use generative AI to design the interfaces based on the requirements. This allows for high-quality design by designing the system based on the requirements. Examples of architecture include, but are not limited to, client-server models and microservice architectures. Examples of data flow include, but are not limited to, data flow and data transformation processes. Some or all of the above processes in the design department may be performed using generative AI or not. For example, the design department can design the system architecture based on the requirements, and a generative AI can analyze its contents and perform a concrete design.

[0064] The management department can analyze project progress data and propose appropriate measures if delays occur. For example, the management department can use a generative AI to analyze project progress data and propose appropriate measures if delays occur. The management department can also use a generative AI to analyze project progress data and propose resource reallocation. Furthermore, the management department can use a generative AI to analyze project progress data and propose schedule changes. For example, the management department can use a generative AI to analyze project progress data and propose appropriate measures if delays occur. The management department can also use a generative AI to analyze project progress data and propose resource reallocation. Furthermore, the management department can use a generative AI to analyze project progress data and propose schedule changes. This improves the project success rate by monitoring project progress and proposing appropriate measures when delays occur. Progress data includes, but is not limited to, task completion status and schedule achievement. Appropriate measures include, but are not limited to, resource reallocation and schedule changes. Some or all of the above processing in the management department may be performed using a generative AI or not. For example, the management department can input project progress data into a generating AI, which can then analyze the data and propose appropriate countermeasures.

[0065] The requirements extraction unit can estimate the user's emotions and determine the priority of requirements extraction based on the estimated emotions. For example, if the user is feeling anxious, the generation AI will prioritize extracting important requirements and provide a sense of security. Furthermore, if the user is excited, the generation AI can proactively extract new ideas and requirements. Additionally, if the user is tired, the generation AI can prioritize extracting simple and clear requirements. This allows for more appropriate requirements extraction by determining the priority of requirements extraction based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the requirements extraction unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the requirements extraction unit can input user emotion data into a generation AI, which can then analyze the data to determine the priority of requirements extraction.

[0066] The requirements extraction unit can analyze a user's past request history and automatically extract similar requirements. For example, the requirements extraction unit's generating AI can extract similar requirements based on functions previously requested by the user. The requirements extraction unit can also analyze a user's past project history and extract common requirements. Furthermore, the requirements extraction unit's generating AI can extract relevant requirements based on feedback previously submitted by the user. For example, the requirements extraction unit's generating AI can extract similar requirements based on functions previously requested by the user. Furthermore, the requirements extraction unit can analyze a user's past project history and extract common requirements. Furthermore, the requirements extraction unit's generating AI can extract relevant requirements based on feedback previously submitted by the user. This allows for the efficient extraction of similar requirements by analyzing past request history. Past request history includes, but is not limited to, database queries and log analysis. Similar requirements include, but is not limited to, text mining and clustering. Some or all of the above processing in the requirements extraction unit may be performed using or without the generating AI. For example, the requirements extraction unit can input the user's past request history into the generation AI, which then analyzes the content to extract similar requirements.

[0067] The requirements extraction unit can organize requirements while considering the user's industry-specific terminology and expressions. For example, the requirements extraction unit can have its generation AI automatically recognize industry-specific terminology used by the user and reflect it in the requirements. The requirements extraction unit can also organize requirements using templates specific to the user's industry. Furthermore, the requirements extraction unit can organize requirements while considering industry-specific regulations and standards. For example, the requirements extraction unit can have its generation AI automatically recognize industry-specific terminology used by the user and reflect it in the requirements. Furthermore, the requirements extraction unit can also organize requirements using templates specific to the user's industry. Furthermore, the requirements extraction unit can organize requirements while considering industry-specific regulations and standards. This makes the requirements organization more accurate by considering industry-specific terminology and expressions. Industry-specific terminology and expressions include, but are not limited to, specialized terminology dictionaries and industry guidelines. Some or all of the above processing in the requirements extraction unit may be performed using or without the generation AI. For example, the requirements extraction unit can input industry-specific terminology used by the user into the generation AI, which can then analyze its content and reflect it in the requirements.

[0068] The requirements extraction unit can estimate the user's emotions and adjust the way requirements are expressed based on the estimated emotions. For example, if the user is nervous, the generating AI will use simple and clear expressions. If the user is relaxed, the generating AI can also use expressions that include detailed explanations. Furthermore, if the user is in a hurry, the generating AI can also use concise and to the point. This allows for more appropriate requirements extraction by adjusting the expression based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the requirements extraction unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the requirements extraction unit can input user emotion data into a generation AI, which can then analyze the data and adjust the way the requirements are expressed.

[0069] The requirements extraction unit can prioritize extracting region-specific requirements by considering the user's geographical location information during requirements extraction. For example, if the user is in a specific region, the requirements extraction unit will extract requirements considering the regulations and standards of that region. The requirements extraction unit can also extract requirements that reflect region-specific needs based on the user's geographical location information. Furthermore, the requirements extraction unit can organize requirements by considering the culture and customs of the region. For example, if the user is in a specific region, the requirements extraction unit will extract requirements considering the regulations and standards of that region. The requirements extraction unit can also extract requirements that reflect region-specific needs based on the user's geographical location information. Furthermore, the requirements extraction unit can organize requirements by considering the culture and customs of the region. This allows for the efficient extraction of region-specific requirements by considering geographical location information. Geographical location information includes, but is not limited to, GPS data and address information. Region-specific requirements include, but are not limited to, local laws, culture, and infrastructure. Some or all of the above processing in the requirements extraction unit may be performed using or without generative AI. For example, the requirements extraction unit can input the user's geographical location information into the generating AI, which then analyzes the data to extract region-specific requirements.

[0070] The requirements extraction unit can analyze a user's social media activity and extract relevant requirements during requirements extraction. For example, the requirements extraction unit can analyze a user's social media posts and extract relevant requirements. The requirements extraction unit can also use a generative AI to extract requirements based on the activity of accounts the user follows. Furthermore, the requirements extraction unit can use a generative AI to organize requirements based on the user's social media feedback. For example, the requirements extraction unit can analyze a user's social media posts and extract relevant requirements. The requirements extraction unit can also use a generative AI to extract requirements based on the activity of accounts the user follows. Furthermore, the requirements extraction unit can use a generative AI to organize requirements based on the user's social media feedback. This allows for the efficient extraction of relevant requirements by analyzing social media activity. Social media activity includes, but is not limited to, analysis of post content and follower analysis. Relevant requirements include, but are not limited to, keyword matching and topic modeling. Some or all of the above-described processes in the requirements extraction unit may be performed using a generative AI or not. For example, the requirements extraction unit can input user social media activity data into a generating AI, which then analyzes the data to extract relevant requirements.

[0071] The design department can estimate the user's emotions and adjust the level of detail in the design based on the estimated emotions. For example, if the user is relaxed, the design department can use a generative AI to provide a detailed design. If the user is in a hurry, the design department can use a generative AI to provide a concise design. Furthermore, if the user is feeling anxious, the design department can use a generative AI to provide a design that includes detailed explanations to reassure them. For example, if the user is relaxed, the design department can use a generative AI to provide a detailed design. If the user is in a hurry, the design department can use a generative AI to provide a concise design. Furthermore, if the user is feeling anxious, the design department can use a generative AI to provide a design that includes detailed explanations to reassure them. This allows for more appropriate designs by adjusting the level of detail in the design based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the design department may be performed using a generative AI or not. For example, the design department can input user emotion data into a generating AI, which can then analyze the data and adjust the level of detail in the design.

[0072] The design department can select the optimal design pattern by referring to past design data during the design process. For example, the design department can use generative AI to select the optimal design pattern based on design data from past successful projects. Alternatively, the design department can analyze data from past failed projects, and the generative AI can select a design pattern that avoids risks. Furthermore, the design department can refer to design data from similar past projects, and the generative AI can provide the optimal design. For example, the design department can use generative AI to select the optimal design pattern based on design data from past successful projects. Alternatively, the design department can analyze data from past failed projects, and the generative AI can select a design pattern that avoids risks. Furthermore, the design department can refer to design data from similar past projects, and the generative AI can provide the optimal design. This allows for the selection of the optimal design pattern by referring to past design data. Past design data includes, but is not limited to, database queries and references to design documents. Optimal design patterns include, but are not limited to, performance, scalability, and maintainability. Some or all of the above processes in the design department may be performed using generative AI or not. For example, the design department can input past design data into a generation AI, which can then analyze the data and select the optimal design pattern.

[0073] The design department can design the system while considering its scalability. For example, the design department can use a generative AI to provide a scalable design that takes into account future increases in the number of users. The design department can also use a generative AI to design the system while considering load balancing. Furthermore, the design department can use a generative AI to provide a design that takes scalability in a cloud environment into account. For example, the design department can use a generative AI to provide a scalable design that takes into account future increases in the number of users. The design department can also use a generative AI to design the system while considering load balancing. Furthermore, the design department can use a generative AI to provide a design that takes scalability in a cloud environment into account. By considering the scalability of the system, future expansion becomes easier. Scalability includes, but is not limited to, load balancing, scalability, and redundancy. Some or all of the above processes in the design department may be performed using a generative AI or not. For example, the design department can input the system's scalability into a generative AI, which then analyzes the content and provides a scalable design.

[0074] The design department can estimate the user's emotions and determine design priorities based on those emotions. For example, if the user is feeling anxious, the design department can use generative AI to prioritize important design elements. If the user is relaxed, the design department can use generative AI to provide detailed design elements. Furthermore, if the user is in a hurry, the design department can use generative AI to prioritize concise design elements. This allows for more appropriate designs by prioritizing design based on user emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processes described above in the design department may be performed using a generative AI, or they may not be performed using a generative AI. For example, the design department can input user emotion data into a generative AI, which can then analyze the data to determine design priorities.

[0075] The design department can customize the design during the design phase, taking into account the user's industry-specific requirements. For example, the design department can customize the design considering the user's industry-specific regulations. The design department can also use industry-specific templates for the design. Furthermore, the design department can customize the design considering industry-specific terminology and expressions. This allows for a more appropriate design by considering industry-specific requirements. Industry-specific requirements include, but are not limited to, industry guidelines and technical glossaries. Some or all of the above processes in the design department may be performed using or without generative AI. For example, the design department can input the user's industry-specific requirements into a generative AI, which then analyzes the content and customizes the design.

[0076] The design department can reflect region-specific design requirements by considering the user's geographical location information during the design process. For example, if the user is in a specific region, the design department will design while considering the regulations and standards of that region. The design department can also provide designs that reflect region-specific needs based on the user's geographical location information. Furthermore, the design department can customize designs by considering local culture and customs. For example, if the user is in a specific region, the design department will design while considering the regulations and standards of that region. The design department can also provide designs that reflect region-specific needs based on the user's geographical location information. Furthermore, the design department can customize designs by considering local culture and customs. This allows for the efficient reflection of region-specific design requirements by considering geographical location information. Geographical location information includes, but is not limited to, GPS data and address information. Region-specific design requirements include, but are not limited to, local laws, culture, and infrastructure. Some or all of the above processing in the design department may be performed using generative AI or not. For example, the design department can input the user's geographical location information into a generating AI, which can then analyze the data to reflect region-specific design requirements.

[0077] The management department can estimate the user's emotions and adjust the frequency of project progress reports based on the estimated emotions. For example, if the user is feeling anxious, the management department can have the generative AI provide frequent progress reports. Alternatively, if the user is relaxed, the management department can have the generative AI provide progress reports at a moderate frequency. Furthermore, if the user is in a hurry, the management department can have the generative AI provide rapid progress reports. This allows for more appropriate project management by adjusting the frequency of progress reports based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using or without generative AI. For example, the management department can input user emotion data into a generating AI, which can then analyze the data and adjust the frequency of progress reports.

[0078] The management department can optimize risk management by referring to past project data during project management. For example, the management department can use generative AI to predict risks and propose countermeasures based on past project data. The management department can also analyze data from past failed projects, and generative AI can propose methods for risk avoidance. Furthermore, the management department can refer to data from past successful projects, and generative AI can provide best practices for risk management. For example, the management department can use generative AI to predict risks and propose countermeasures based on past project data. The management department can also analyze data from past failed projects, and generative AI can propose methods for risk avoidance. Furthermore, the management department can refer to data from past successful projects, and generative AI can provide best practices for risk management. In this way, risk management can be optimized by referring to past project data. Past project data includes, but is not limited to, database queries and project report references. Risk management includes, but is not limited to, risk identification, risk assessment, and risk countermeasures. Some or all of the above processes in the management department may be performed using generative AI or not. For example, the management department can input past project data into a generating AI, which then analyzes the data to optimize risk management.

[0079] The management department can assign tasks while considering the skill sets of team members during project management. For example, the management department can use a generative AI to assign the most suitable tasks based on the skill sets of team members. Alternatively, the management department can refer to the past performance data of team members and have the generative AI assign tasks. Furthermore, the management department can use a generative AI to assign tasks while considering the current skill levels of team members. For example, the management department can use a generative AI to assign the most suitable tasks based on the skill sets of team members. Alternatively, the management department can refer to the past performance data of team members and have the generative AI assign tasks. Furthermore, the management department can use a generative AI to assign tasks while considering the current skill levels of team members. This makes it possible to assign tasks optimally by considering the skill sets of team members. Skill sets include, but are not limited to, technical skills, experience, and qualifications. Task assignment includes, but are not limited to, skill matching, task importance, and task urgency. Some or all of the above processes in the management department may be performed using or without generative AI. For example, the management department can input the skill sets of team members into a generating AI, which can then analyze the data and assign the most suitable tasks.

[0080] The management department can estimate the user's emotions and adjust the format of project progress reports based on those emotions. For example, if the user is stressed, the generative AI can provide a simple and easy-to-read report format. If the user is relaxed, the generative AI can provide a report format that includes more detailed information. Furthermore, if the user is in a hurry, the generative AI can provide a report format that gets straight to the point. This allows for more effective project management by adjusting the report format based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the management department may be performed using a generative AI, or they may not be performed using a generative AI. For example, the management department can input user emotion data into a generative AI, which can then analyze the data and adjust the format of the progress report.

[0081] The management department can incorporate project management methodologies specific to the user's industry when managing projects. For example, the management department can have a generating AI automatically apply project management methodologies specific to the user's industry. Alternatively, the management department can use industry-specific templates for project management. Furthermore, the management department can manage projects while considering industry-specific regulations and standards. This allows for more appropriate project management by incorporating industry-specific project management methodologies. Some or all of the above processes in the management department may be performed using a generating AI, or not. For example, the management department can input project management methodologies specific to the user's industry into a generating AI, which can then analyze and perform project management.

[0082] The management department can manage region-specific risks by considering the user's geographical location information during project management. For example, if a user is in a specific region, the management department will manage the project while considering the risks of that region. The management department can also predict region-specific risks and propose countermeasures based on the user's geographical location information. Furthermore, the management department can manage risks while considering the local culture and customs. For example, if a user is in a specific region, the management department will manage the project while considering the risks of that region. The management department can also predict region-specific risks and propose countermeasures based on the user's geographical location information. Furthermore, the management department can manage risks while considering the local culture and customs. This allows for efficient management of region-specific risks by considering geographical location information. Geographical location information includes, but is not limited to, GPS data and address information. Region-specific risks include, but are not limited to, natural disasters, infrastructure vulnerabilities, and local laws. Some or all of the above processing by the management department may be performed using generative AI, or not. For example, the management department can input users' geographical location information into a generating AI, which can then analyze the data to manage region-specific risks.

[0083] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0084] The requirements extraction unit can estimate the user's emotions and determine the priority of requirements extraction based on the estimated user emotions. For example, if the user is feeling anxious, the requirements extraction unit's generating AI can prioritize extracting important requirements and provide a sense of security. Furthermore, if the user is excited, the requirements extraction unit's generating AI can proactively extract new ideas and requirements. Additionally, if the user is tired, the requirements extraction unit's generating AI can prioritize extracting simple and clear requirements. This allows for more appropriate requirements extraction by determining the priority of requirements extraction based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the requirements extraction unit may be performed using or without a generating AI. For example, the requirements extraction unit can input user emotion data into a generating AI, which can then analyze the data to determine the priority of requirements extraction.

[0085] The requirements extraction unit can analyze a user's past request history and automatically extract similar requirements. For example, the requirements extraction unit can use a generating AI to extract similar requirements based on features previously requested by the user. The requirements extraction unit can also analyze a user's past project history and extract common requirements. Furthermore, the requirements extraction unit can use a generating AI to extract relevant requirements based on feedback previously submitted by the user. This allows for the efficient extraction of similar requirements by analyzing past request history. Past request history includes, but is not limited to, database queries and log analysis. Similar requirements include, but are not limited to, text mining and clustering. Some or all of the above processing in the requirements extraction unit may be performed using a generating AI or not. For example, the requirements extraction unit can input a user's past request history into a generating AI, which can then analyze its contents and extract similar requirements.

[0086] The design department can estimate the user's emotions and adjust the level of detail in the design based on the estimated emotions. For example, if the user is relaxed, the design department can use a generative AI to provide a detailed design. If the user is in a hurry, the design department can use a generative AI to provide a concise design. Furthermore, if the user is feeling anxious, the design department can use a generative AI to provide a design that includes detailed explanations to reassure them. This allows for more appropriate designs by adjusting the level of detail in the design based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the design department may be performed using a generative AI or not. For example, the design department can input user emotion data into a generative AI, which can then analyze the data and adjust the level of detail in the design.

[0087] The design department can select the optimal design pattern by referring to past design data during the design process. For example, the design department can use a generative AI to select the optimal design pattern based on design data from past successful projects. Alternatively, the design department can analyze data from past failed projects, and the generative AI can select a design pattern that avoids risks. Furthermore, the design department can refer to design data from similar past projects, and the generative AI can provide the optimal design. In this way, the optimal design pattern can be selected by referring to past design data. Past design data includes, but is not limited to, database queries and references to design documents. Optimal design patterns include, but are not limited to, performance, scalability, and maintainability. Some or all of the above processes in the design department may be performed using a generative AI, or they may not be performed using a generative AI. For example, the design department can input past design data into a generative AI, which can then analyze its contents and select the optimal design pattern.

[0088] The management department can estimate the user's emotions and adjust the frequency of project progress reports based on the estimated emotions. For example, if the user is feeling anxious, the management department can have the generative AI provide frequent progress reports. Alternatively, if the user is relaxed, the management department can have the generative AI provide progress reports at a moderate frequency. Furthermore, if the user is in a hurry, the management department can have the generative AI provide rapid progress reports. This allows for more appropriate project management by adjusting the frequency of progress reports based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using or without a generative AI. For example, the management department can input user emotion data into a generative AI, which can then analyze the data to adjust the frequency of progress reports.

[0089] The management department can optimize risk management by referring to past project data during project management. For example, the management department can use generative AI to predict risks and propose countermeasures based on past project data. The management department can also analyze data from past failed projects, and the generative AI can propose ways to avoid risks. Furthermore, the management department can refer to data from past successful projects, and the generative AI can provide best practices for risk management. In this way, risk management can be optimized by referring to past project data. Past project data includes, but is not limited to, database queries and project report references. Risk management includes, but is not limited to, risk identification, risk assessment, and risk countermeasures. Some or all of the above processes in the management department may be performed using generative AI, or not. For example, the management department can input past project data into a generative AI, and the generative AI can analyze its contents to optimize risk management.

[0090] The management department can assign tasks to team members while considering their skill sets during project management. For example, the management department can use a generative AI to assign optimal tasks based on the team members' skill sets. The management department can also refer to the team members' past performance data and have the generative AI assign tasks based on that. Furthermore, the management department can have the generative AI assign tasks while considering the team members' current skill levels. This makes it possible to assign tasks optimally by considering the team members' skill sets. Skill sets include, but are not limited to, technical skills, experience, and qualifications. Task assignments include, but are not limited to, skill matching, task importance, and task urgency. Some or all of the above processes in the management department may be performed using a generative AI, or they may not. For example, the management department can input the team members' skill sets into a generative AI, which can then analyze the data and assign optimal tasks.

[0091] The management department can estimate the user's emotions and adjust the format of project progress reports based on those emotions. For example, if the user is stressed, the management department can use a generative AI to provide a simple and easy-to-read report format. If the user is relaxed, the management department can use the generative AI to provide a report format that includes detailed information. Furthermore, if the user is in a hurry, the management department can use the generative AI to provide a report format that gets straight to the point. This allows for more effective project management by adjusting the report format based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using or without the generative AI. For example, the management department can input user emotion data into the generative AI, which can then analyze the data and adjust the format of the progress report.

[0092] The management department can incorporate project management methodologies specific to the user's industry during project management. For example, the management department can use a generative AI to automatically apply project management methodologies specific to the user's industry. The management department can also use templates tailored to the user's industry for project management. Furthermore, the management department can manage projects while considering industry-specific regulations and standards. This allows for more appropriate project management by incorporating industry-specific project management methodologies. Some or all of the above processes in the management department may be performed using a generative AI, or they may not. For example, the management department can input project management methodologies specific to the user's industry into a generative AI, which can then analyze and perform project management.

[0093] The management department can manage region-specific risks by considering the user's geographical location information during project management. For example, if a user is in a specific region, the management department will manage the project while considering the risks of that region. The management department can also predict region-specific risks and propose countermeasures based on the user's geographical location information. Furthermore, the management department can manage risks by considering local culture and customs. In this way, region-specific risks can be managed efficiently by considering geographical location information. Geographical location information includes, but is not limited to, GPS data and address information. Region-specific risks include, but are not limited to, natural disasters, infrastructure vulnerabilities, and local laws. Some or all of the above processing by the management department may be performed using generative AI, or not using generative AI. For example, the management department can input the user's geographical location information into a generative AI, and the generative AI can analyze the content to manage region-specific risks.

[0094] The following briefly describes the processing flow for example form 2.

[0095] Step 1: The requirements extraction unit analyzes user requests and extracts specific requirements. For example, if a user inputs the functions and performance they require from the system in natural language, the generating AI analyzes the content and organizes it into specific requirements. The requirements extraction unit can also collect user requests through interviews and questionnaires, and the generating AI analyzes the content to extract requirements. Furthermore, the requirements extraction unit can observe user requests, and the generating AI analyzes the content to extract requirements. Step 2: The design department designs the system based on the requirements extracted by the requirements extraction department. For example, a generative AI designs the system architecture and data flow based on the requirements. The design department can also have the generative AI design the interfaces based on the requirements. Furthermore, the design department can have the generative AI design the database based on the requirements. Step 3: The management department monitors the project's progress based on the design created by the design department and makes necessary adjustments. For example, the generative AI analyzes the project's progress data and proposes appropriate countermeasures if delays occur. The management department can also have the generative AI analyze the project's progress data and propose resource reallocations. Furthermore, the management department can have the generative AI analyze the project's progress data and propose schedule changes.

[0096] 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.

[0097] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0098] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0099] Each of the multiple elements described above, including the requirements extraction unit, design unit, and management unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the requirements extraction unit is implemented by the control unit 46A of the smart device 14, which takes user requests as input in natural language, and the generating AI analyzes the content to extract specific requirements. The design unit is implemented by the specific processing unit 290 of the data processing unit 12, where the generating AI designs the system architecture and data flow based on the requirements. The management unit is implemented by the specific processing unit 290 of the data processing unit 12, where the generating AI analyzes project progress data and proposes appropriate countermeasures if delays occur. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0100] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0101] 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.

[0102] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0103] 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.

[0104] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0105] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0106] 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.

[0107] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0108] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0109] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0110] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0111] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0112] 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.

[0113] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0114] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements described above, including the requirements extraction unit, design unit, and management unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the requirements extraction unit is implemented by the control unit 46A of the smart glasses 214, which takes user requests as input in natural language and generates an AI that analyzes the content to extract specific requirements. The design unit is implemented by the specific processing unit 290 of the data processing unit 12, where the generating AI designs the system architecture and data flow based on the requirements. The management unit is implemented by the specific processing unit 290 of the data processing unit 12, where the generating AI analyzes project progress data and proposes appropriate countermeasures if delays occur. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0116] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0117] 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.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0119] 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.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0121] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] 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.

[0123] 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.

[0124] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the requirements extraction unit, design unit, and management unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the requirements extraction unit is implemented by the control unit 46A of the headset terminal 314, which takes user requests as input in natural language, and the generating AI analyzes the content to extract specific requirements. The design unit is implemented by the specific processing unit 290 of the data processing unit 12, where the generating AI designs the system architecture and data flow based on the requirements. The management unit is implemented by the specific processing unit 290 of the data processing unit 12, where the generating AI analyzes project progress data and proposes appropriate countermeasures if delays occur. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0132] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0135] 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.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0137] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] 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.

[0139] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0140] 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.

[0141] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] 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.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the requirements extraction unit, design unit, and management unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the requirements extraction unit is implemented by the control unit 46A of the robot 414, which takes user requests as input in natural language, and the generating AI analyzes the content to extract specific requirements. The design unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where the generating AI designs the system architecture and data flow based on the requirements. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where the generating AI analyzes project progress data and proposes appropriate countermeasures if delays occur. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0149] 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.

[0150] Figure 9 shows the 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.

[0151] 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.

[0152] 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.

[0153] 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, and motorcycles, 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 based, for example, 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.

[0154] 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."

[0155] 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.

[0156] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0165] 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 other things 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.

[0166] 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.

[0167] (Note 1) A requirements extraction unit analyzes user requests and extracts specific requirements, A design unit that designs the system based on the requirements extracted by the requirements extraction unit, The system includes a management unit that monitors the project's progress based on the design made by the aforementioned design unit and makes necessary adjustments. A system characterized by the following features. (Note 2) The requirements extraction unit, Input user requests in natural language. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned design department, Based on the requirements, design the system architecture and data flow. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned management department, We analyze project progress data and propose appropriate countermeasures if delays occur. The system described in Appendix 1, characterized by the features described herein. (Note 5) The requirements extraction unit, The system estimates user emotions and prioritizes requirements extraction based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The requirements extraction unit, Analyze the user's past request history and automatically extract similar requirements. The system described in Appendix 1, characterized by the features described herein. (Note 7) The requirements extraction unit, When extracting requirements, organize them while taking into account the user's industry-specific terminology and expressions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The requirements extraction unit, We estimate the user's emotions and adjust the way requirements are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The requirements extraction unit, During requirements extraction, prioritize extracting region-specific requirements by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The requirements extraction unit, During requirements extraction, analyze users' social media activity to extract relevant requirements. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned design department, We estimate user emotions and adjust the level of detail in the design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned design department, During the design phase, the optimal design pattern is selected by referring to past design data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned design department, When designing, the system's scalability should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned design department, We estimate user emotions and determine design priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned design department, During the design phase, the design is customized to take into account the user's industry-specific requirements. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned design department, During the design phase, the user's geographical location information is taken into consideration to reflect region-specific design requirements. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned management department, Estimate user sentiment and adjust the frequency of project progress reports based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned management department, Optimize risk management by referring to past project data during project management. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned management department, When managing a project, assign tasks while considering the skill sets of team members. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned management department, Estimate user sentiment and adjust the project progress report format based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned management department, When managing a project, incorporate project management methods specific to the user's industry. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned management department, During project management, consider the user's geographical location to manage region-specific risks. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A requirements extraction unit analyzes user requests and extracts specific requirements, A design unit that designs the system based on the requirements extracted by the requirements extraction unit, The system includes a management unit that monitors the project's progress based on the design made by the aforementioned design unit and makes necessary adjustments. A system characterized by the following features.

2. The requirements extraction unit, Input user requests in natural language. The system according to feature 1.

3. The aforementioned design department, Based on the requirements, design the system architecture and data flow. The system according to feature 1.

4. The aforementioned management department, We analyze project progress data and propose appropriate countermeasures if delays occur. The system according to feature 1.

5. The requirements extraction unit, The system estimates user emotions and prioritizes requirements extraction based on those estimated emotions. The system according to feature 1.

6. The requirements extraction unit, Analyze the user's past request history and automatically extract similar requirements. The system according to feature 1.

7. The requirements extraction unit, When extracting requirements, organize them while taking into account the user's industry-specific terminology and expressions. The system according to feature 1.

8. The requirements extraction unit, We estimate the user's emotions and adjust the way requirements are expressed based on those estimated emotions. The system according to feature 1.

9. The requirements extraction unit, During requirements extraction, prioritize extracting region-specific requirements by considering the user's geographical location. The system according to feature 1.

10. The requirements extraction unit, During requirements extraction, analyze users' social media activity to extract relevant requirements. The system according to feature 1.

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