System

Abstract

An object of a system according to an embodiment is to efficiently create and update a system specification and to propose a risk analysis and an operation check.SOLUTION: A system according to an embodiment includes a requirement analysis unit, a specification generation unit, a risk analysis unit, and a proposal unit. The requirement analysis unit collects requirements of the system. The specification generation unit creates a system specification based on the requirements collected by the requirement analysis unit. The risk analysis unit performs a risk analysis based on the system specification created by the specification generation unit. The proposal part proposes an operation confirmation part and an execution method based on the risk factor specified by the risk analysis part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, creating and updating system specifications requires a significant amount of man-hours, and there is a risk of oversight.

[0005] The system according to the embodiment aims to improve the efficiency of creating and updating system specifications and to propose risk analysis and operation checks. [Means for solving the problem]

[0006] A system according to an embodiment includes a requirements analysis unit, a specification generation unit, a risk analysis unit, and a proposal unit. The requirements analysis unit collects system requirements. The specification generation unit creates a system specification based on the requirements collected by the requirements analysis unit. The risk analysis unit performs risk analysis based on the system specification created by the specification generation unit. The proposal unit proposes operation check points and implementation methods based on the risk factors identified by the risk analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of creating and updating system specifications and can propose risk analysis and operation checks. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A system according to an embodiment of the present invention uses a generation AI to automate the creation and updating of system specifications for new system construction and modifications. This system collects system requirements through interaction with the generation AI, creates system specifications based on the collected requirements, performs risk analysis based on the system specifications, and proposes operational check points and implementation methods. For example, a user inputs requirements for new system construction or modifications in natural language, and the generation AI analyzes the requirements and creates a system specification. The generation AI then analyzes the system specification and identifies risk factors. Furthermore, the generation AI proposes operational check points and implementation methods based on the identified risk factors. This significantly reduces the effort required to create and update system specifications and achieves high-quality system management. This system significantly reduces the effort required to create and update specifications and achieves high-quality system management. For example, a user can easily collect system requirements through interaction with the generation AI and automatically create and update specifications. Furthermore, risk analysis and operational check proposals can improve system quality.

[0029] A system according to an embodiment includes a requirements analysis unit, a specification generation unit, a risk analysis unit, and a proposal unit. The requirements analysis unit collects system requirements. For example, the requirements analysis unit collects requirements related to the new construction or modification of a system input by a user in natural language. The requirements analysis unit can analyze the user's input and extract requirements using a generation AI. For example, if a user inputs "I want to add a new user management function," the requirements analysis unit analyzes the requirement and extracts it as a specific requirement. The specification generation unit creates a system specification based on the collected requirements. The specification generation unit automatically generates a system design and functional specifications based on the requirements using a generation AI. For example, the specification generation unit generates specific specifications such as "database design for adding a new user management function" and "user interface specifications." The risk analysis unit analyzes the contents of the system specification and identifies risk factors. The risk analysis unit analyzes the contents of the specification using a generation AI and identifies risk factors for the system. For example, the risk analysis unit identifies risk factors such as "database security risks" and "user interface usability issues." The proposal unit proposes operation check points and implementation methods based on the identified risk factors. The proposal unit uses a generative AI to propose which parts should be checked for the identified risk factors and how. For example, the proposal unit proposes specific operation check methods such as "database security testing" and "user interface usability testing." As a result, the system according to the embodiment automates system requirements collection, specification creation, risk analysis, and operation check proposals, reducing man-hours and achieving high-quality system management.

[0030] The requirements analysis unit can collect requirements for new system construction or modification input by the user in natural language. The requirements analysis unit, for example, collects requirements for new system construction or modification input by the user in natural language. The requirements analysis unit can analyze the user's input and extract requirements using a generation AI. For example, if the user inputs "I want to add a new user management function," the requirements analysis unit analyzes the requirements and extracts them as specific requirements. This makes it easier to collect requirements by having the user input requirements in natural language. Some or all of the above-mentioned processing in the requirements analysis unit may be performed using or without the generation AI. For example, the requirements analysis unit can input the user's natural language input into a generation AI and have the generation AI extract requirements.

[0031] The specification generation unit can automatically generate system design drawings and functional specifications based on the collected requirements. The specification generation unit, for example, automatically generates system design drawings and functional specifications based on the collected requirements. The specification generation unit can automatically generate system design drawings and functional specifications based on the requirements using a generation AI. For example, the specification generation unit generates specific specifications such as "database design for adding a new user management function" and "user interface specifications." This improves the efficiency of specification creation by automatically generating design drawings and functional specifications based on the collected requirements. Some or all of the above-mentioned processing in the specification generation unit may be performed using or without the generation AI. For example, the specification generation unit can input the collected requirements into the generation AI and cause the generation AI to generate system design drawings and functional specifications.

[0032] The risk analysis unit can analyze the contents of the system specifications and identify risk factors for the system. The risk analysis unit can, for example, analyze the contents of the system specifications and identify risk factors for the system. The risk analysis unit can use a generation AI to analyze the contents of the specifications and identify risk factors for the system. For example, the risk analysis unit can identify risk factors such as "database security risks" and "user interface usability issues." This facilitates risk management by analyzing the contents of the system specifications and identifying risk factors. Some or all of the above-mentioned processing in the risk analysis unit may be performed using or without the generation AI. For example, the risk analysis unit can input the system specifications into the generation AI and have the generation AI identify risk factors.

[0033] The suggestion unit can propose which parts should be checked for operation and how for the identified risk factors. For example, the suggestion unit can propose which parts should be checked for operation and how for the identified risk factors. The suggestion unit can use the generation AI to propose which parts should be checked for operation and how for the identified risk factors. For example, the suggestion unit can propose specific operation check methods such as "database security testing" or "user interface usability testing." This improves the quality of the system by proposing an operation check method based on the identified risk factors. Some or all of the above-mentioned processing in the suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input the identified risk factors into the generation AI and have the generation AI execute a proposed operation check method.

[0034] The requirements analysis unit can analyze the user's past requirements submission history and select the optimal collection method. For example, the requirements analysis unit analyzes the user's past requirements submission history and selects the optimal collection method. The requirements analysis unit can analyze the user's past requirements submission history and select the optimal collection method using a generation AI. For example, if the user has preferred text input in the past, the requirements analysis unit can preferentially suggest text input. Also, if the user has frequently used voice input in the past, the requirements analysis unit can recommend voice input. Also, if the user has previously submitted requirements using images, the requirements analysis unit can support image input. In this way, the optimal collection method can be selected by analyzing the user's past requirements submission history. Some or all of the above-mentioned processing in the requirements analysis unit may be performed using or without the generation AI. For example, the requirements analysis unit can input the user's past requirements submission history into the generation AI and have the generation AI select the optimal collection method.

[0035] The requirements analysis unit can perform filtering based on the user's current project or areas of interest during requirements collection. For example, the requirements analysis unit can perform filtering based on the user's current project or areas of interest during requirements collection. The requirements analysis unit can use the generation AI to perform filtering based on the user's current project or areas of interest during requirements collection. For example, the requirements analysis unit prioritizes collecting only requirements related to the user's ongoing project. The requirements analysis unit can also filter and collect related requirements based on the user's areas of interest. The requirements analysis unit can also prioritize collecting requirements related to areas in which the user has previously shown interest. This allows highly relevant requirements to be collected by filtering requirements based on the user's current project or areas of interest. Some or all of the above-described processing in the requirements analysis unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the requirements analysis unit can input data on the user's project information and areas of interest into the generation AI and have the generation AI perform the filtering.

[0036] The requirements analysis unit can select the optimal collection means depending on the user's input method when collecting requirements. For example, the requirements analysis unit selects the optimal collection means depending on the user's input method (voice, text, image, etc.) when collecting requirements. The requirements analysis unit can use the generation AI to select the optimal collection means depending on the user's input method when collecting requirements. For example, if the user selects voice input, the requirements analysis unit collects requirements using voice recognition technology. Also, if the user selects text input, the requirements analysis unit can collect requirements using text analysis technology. Also, if the user selects image input, the requirements analysis unit can collect requirements using image analysis technology. This allows for efficient collection of requirements by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the requirements analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the requirements analysis unit can input the user's input data into the generation AI and have the generation AI select the optimal collection means.

[0037] The requirements analysis unit can prioritize collecting highly relevant requirements by taking into account the user's geographical location information when collecting requirements. For example, the requirements analysis unit can prioritize collecting highly relevant requirements by taking into account the user's geographical location information when collecting requirements. The requirements analysis unit can prioritize collecting highly relevant requirements by using the generation AI by taking into account the user's geographical location information when collecting requirements. For example, if the user is in a specific area, the requirements analysis unit can prioritize collecting requirements related to that area. Furthermore, if the user is traveling, the requirements analysis unit can also collect relevant requirements based on the user's current location. Furthermore, if the user is in a specific facility, the requirements analysis unit can prioritize collecting requirements related to that facility. In this way, by taking into account the user's geographical location information, highly relevant requirements can be prioritized. Some or all of the above-described processing in the requirements analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the requirements analysis unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant requirements.

[0038] The requirements analysis unit can analyze the user's social media activities and collect related requirements during requirements collection. For example, the requirements analysis unit can analyze the user's social media activities and collect related requirements during requirements collection. The requirements analysis unit can use the generation AI to analyze the user's social media activities and collect related requirements during requirements collection. For example, the requirements analysis unit prioritizes collecting requirements mentioned by the user on social media. The requirements analysis unit can also analyze the user's social media activities and collect related requirements. The requirements analysis unit can also collect related requirements by referring to the activities of the user's friends on social media. In this way, related requirements can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the requirements analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the requirements analysis unit can input the user's social media data into the generation AI and cause the generation AI to collect related requirements.

[0039] The requirements analysis unit can customize the collection method by reflecting the user's past feedback when collecting requirements. For example, the requirements analysis unit customizes the collection method by reflecting the user's past feedback when collecting requirements. The requirements analysis unit can use the generation AI to customize the collection method by reflecting the user's past feedback when collecting requirements. For example, the requirements analysis unit adjusts the collection method based on feedback provided by the user in the past. The requirements analysis unit can also select the optimal collection means from the user's past feedback. The requirements analysis unit can also customize the collection method by reflecting the user's feedback. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the requirements analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the requirements analysis unit can input the user's past feedback data into the generation AI and have the generation AI customize the collection method.

[0040] The specification generation unit can adjust the level of detail of the specification based on the importance of the requirements when generating the specification. For example, the specification generation unit adjusts the level of detail of the specification based on the importance of the requirements when generating the specification. The specification generation unit can adjust the level of detail of the specification based on the importance of the requirements when generating the specification using a generation AI. For example, the specification generation unit generates a detailed specification for a requirement with high importance. The specification generation unit can also generate a concise specification for a requirement with low importance. The specification generation unit can also dynamically adjust the level of detail of the specification according to the importance of the requirements. This enables efficient creation of specifications by adjusting the level of detail of the specification based on the importance of the requirements. Some or all of the above-mentioned processing in the specification generation unit may be performed using or without the generation AI. For example, the specification generation unit can input requirement importance data to the generation AI and cause the generation AI to adjust the level of detail of the specification.

[0041] The specification generation unit can apply different generation algorithms depending on the category of requirements when generating specifications. For example, the specification generation unit applies different generation algorithms depending on the category of requirements when generating specifications. The specification generation unit can use a generation AI to apply different generation algorithms depending on the category of requirements when generating specifications. For example, the specification generation unit applies a generation algorithm specialized for database design to database-related requirements. The specification generation unit can also apply a generation algorithm specialized for UI design to user interface-related requirements. The specification generation unit can also apply a generation algorithm specialized for security design to security-related requirements. In this way, by applying a generation algorithm depending on the category of requirements, more appropriate specifications can be generated. Some or all of the above-mentioned processing in the specification generation unit may be performed using or without the generation AI. For example, the specification generation unit can input requirement category data to the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0042] The specification generation unit can improve the accuracy of generation when generating specifications by referring to the user's past specification results. For example, the specification generation unit can improve the accuracy of generation when generating specifications by referring to the user's past specification results. The specification generation unit can improve the accuracy of generation when generating specifications by using a generation AI by referring to the user's past specification results. For example, the specification generation unit can improve the accuracy of generation by referring to specifications created by the user in the past. The specification generation unit can also learn the optimal expression method from the user's past specification results. The specification generation unit can also adjust the generation algorithm based on the user's past specification results. In this way, the accuracy of generation is improved by referring to the user's past specification results. Some or all of the above-mentioned processing in the specification generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the specification generation unit can input the user's past specification data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0043] The specification generation unit can determine the priority of specifications based on the submission dates of requirements when generating specifications. For example, the specification generation unit determines the priority of specifications based on the submission dates of requirements when generating specifications. The specification generation unit can use a generation AI to determine the priority of specifications based on the submission dates of requirements when generating specifications. For example, the specification generation unit prioritizes generating specifications for recently submitted requirements. The specification generation unit can also postpone generating specifications for older submitted requirements. The specification generation unit can also dynamically adjust the order in which specifications are generated based on the submission dates. This enables efficient specification creation by determining the priority of specifications based on the submission dates of requirements. Some or all of the above-described processing in the specification generation unit may be performed using or without the generation AI. For example, the specification generation unit can input requirement submission date data into the generation AI and have the generation AI determine the priority of specifications.

[0044] The specification generation unit can adjust the order of the specifications based on the relevance of the requirements when generating the specifications. For example, the specification generation unit adjusts the order of the specifications based on the relevance of the requirements when generating the specifications. The specification generation unit can adjust the order of the specifications based on the relevance of the requirements when generating the specifications using a generation AI. For example, the specification generation unit prioritizes highly relevant requirements when describing them in the specifications. The specification generation unit can also postpone the description of less relevant requirements when describing them in the specifications. The specification generation unit can also dynamically adjust the order of the specifications based on the relevance of the requirements. In this way, by adjusting the order of the specifications based on the relevance of the requirements, it is possible to generate specifications that are easier to understand. Some or all of the above-mentioned processing in the specification generation unit may be performed using or without the generation AI. For example, the specification generation unit can input requirement relevance data into the generation AI and cause the generation AI to adjust the order of the specifications.

[0045] The specification generation unit can adjust the use of technical terms in the specification according to the user's level of expertise when generating the specification. For example, the specification generation unit can adjust the use of technical terms in the specification according to the user's level of expertise when generating the specification. The specification generation unit can use a generation AI to adjust the use of technical terms in the specification according to the user's level of expertise when generating the specification. For example, if the user has technical expertise, the specification generation unit can generate a specification that uses a lot of technical terms. Also, if the user does not have technical expertise, the specification generation unit can generate a specification that uses simple language. The specification generation unit can also dynamically adjust the use of technical terms in the specification according to the user's level of expertise. This makes it possible to generate a more appropriate specification by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-mentioned processing in the specification generation unit may be performed using or without the generation AI. For example, the specification generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0046] The risk analysis unit can improve the accuracy of the risk analysis by taking into account the interrelationships between requirements during risk analysis. For example, the risk analysis unit can improve the accuracy of the risk analysis by taking into account the interrelationships between requirements during risk analysis. The risk analysis unit can improve the accuracy of the risk analysis by using a generative AI to take into account the interrelationships between requirements during risk analysis. For example, the risk analysis unit performs risk analysis by taking into account dependencies between requirements. The risk analysis unit can also perform risk analysis by taking into account interactions between requirements. The risk analysis unit can also improve the accuracy of the risk analysis based on the interrelationships between requirements. In this way, the accuracy of the risk analysis is improved by taking into account the interrelationships between requirements. Some or all of the above-mentioned processing in the risk analysis unit may be performed using or without the generative AI. For example, the risk analysis unit can input requirement interrelationship data into the generative AI and cause the generative AI to improve the accuracy of the risk analysis.

[0047] The risk analysis unit can perform risk analysis by taking into account attribute information of the requirement submitter during risk analysis. For example, the risk analysis unit can perform risk analysis by taking into account attribute information of the requirement submitter during risk analysis. The risk analysis unit can perform risk analysis by using a generation AI and taking into account attribute information of the requirement submitter during risk analysis. For example, if the requirement submitter is an expert, the risk analysis unit can evaluate the risk of the requirement as low. Also, if the requirement submitter is a novice, the risk analysis unit can evaluate the risk of the requirement as high. Also, the risk analysis unit can improve the accuracy of the risk analysis based on the attribute information of the requirement submitter. In this way, the accuracy of the risk analysis is improved by taking into account the attribute information of the requirement submitter. Some or all of the above-mentioned processing in the risk analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the risk analysis unit can input attribute information of the requirement submitter into the generation AI and have the generation AI perform the risk analysis.

[0048] The risk analysis unit can weight the risk analysis based on the frequency of requirement submission during risk analysis. For example, the risk analysis unit weights the risk analysis based on the frequency of requirement submission during risk analysis. The risk analysis unit can weight the risk analysis based on the frequency of requirement submission during risk analysis using a generation AI. For example, the risk analysis unit may evaluate the risk higher for requirements that are submitted frequently. The risk analysis unit can also evaluate the risk lower for requirements that are submitted infrequently. The risk analysis unit can also dynamically adjust the weighting of the risk analysis based on the submission frequency. This enables more appropriate risk assessment by weighting the risk analysis based on the frequency of requirement submission. Some or all of the above-described processing in the risk analysis unit may be performed using or without the generation AI. For example, the risk analysis unit may input requirement submission frequency data into the generation AI and have the generation AI perform the weighting of the risk analysis.

[0049] The risk analysis unit can perform risk analysis taking into account the geographical distribution of requirements during risk analysis. For example, the risk analysis unit can perform risk analysis taking into account the geographical distribution of requirements during risk analysis. The risk analysis unit can perform risk analysis taking into account the geographical distribution of requirements using a generation AI during risk analysis. For example, the risk analysis unit can evaluate the risk higher for requirements that are geographically concentrated. The risk analysis unit can also evaluate the risk lower for requirements that are geographically dispersed. The risk analysis unit can also improve the accuracy of the risk analysis based on the geographical distribution. In this way, the accuracy of the risk analysis is improved by considering the geographical distribution of requirements. Some or all of the above-mentioned processing in the risk analysis unit may be performed using or without the generation AI. For example, the risk analysis unit can input geographical distribution data of requirements into the generation AI and have the generation AI perform the risk analysis.

[0050] The risk analysis unit can improve the accuracy of the risk analysis by referring to literature related to the requirements during risk analysis. For example, the risk analysis unit can improve the accuracy of the risk analysis by referring to literature related to the requirements during risk analysis. The risk analysis unit can improve the accuracy of the risk analysis by using the generation AI during risk analysis. For example, the risk analysis unit can evaluate the risk of the requirements by referring to literature related to the requirements. The risk analysis unit can also improve the accuracy of the risk analysis based on information in the literature related to the requirements. The risk analysis unit can also adjust the criteria for the risk analysis by referring to literature related to the requirements. In this way, the accuracy of the risk analysis is improved by referring to literature related to the requirements. Some or all of the above-mentioned processing in the risk analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the risk analysis unit can input literature data related to the requirements into the generation AI and have the generation AI perform the risk analysis.

[0051] The risk analysis unit can perform risk analysis taking into account the market value of requirements during risk analysis. For example, the risk analysis unit can perform risk analysis taking into account the market value of requirements during risk analysis. The risk analysis unit can perform risk analysis taking into account the market value of requirements using a generation AI during risk analysis. For example, the risk analysis unit can evaluate the risk higher for requirements with high market value. The risk analysis unit can also evaluate the risk lower for requirements with low market value. The risk analysis unit can also dynamically adjust the weighting of the risk analysis based on the market value. In this way, the weighting of the risk analysis can be dynamically adjusted by taking into account the market value of the requirements. Some or all of the above-mentioned processing in the risk analysis unit may be performed using or without the generation AI. For example, the risk analysis unit can input market value data of requirements to the generation AI and have the generation AI perform the risk analysis.

[0052] The proposal unit can improve the accuracy of the proposal by taking into account the interrelationships between requirements when making a proposal. For example, the proposal unit can improve the accuracy of the proposal by taking into account the interrelationships between requirements when making a proposal. The proposal unit can improve the accuracy of the proposal by using the generation AI to take into account the interrelationships between requirements when making a proposal. For example, the proposal unit makes a proposal by taking into account the dependency relationships between requirements. The proposal unit can also make a proposal by taking into account the interactions between requirements. The proposal unit can also improve the accuracy of the proposal based on the interrelationships between requirements. In this way, the accuracy of the proposal is improved by taking into account the interrelationships between requirements. Some or all of the above-mentioned processing in the proposal unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the proposal unit can input requirement interrelationship data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0053] The proposal unit can make a proposal by taking into account attribute information of the requirement submitter when making a proposal. For example, the proposal unit can make a proposal by taking into account attribute information of the requirement submitter when making a proposal. The proposal unit can use the generation AI to make a proposal by taking into account attribute information of the requirement submitter when making a proposal. For example, if the requirement submitter is an expert, the proposal unit can make an advanced proposal for the requirement. Also, if the requirement submitter is a novice, the proposal unit can make a basic proposal for the requirement. Also, the proposal unit can improve the accuracy of the proposal based on the attribute information of the requirement submitter. In this way, the accuracy of the proposal is improved by taking into account the attribute information of the requirement submitter. Some or all of the above-mentioned processing in the proposal unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the proposal unit can input attribute information of the requirement submitter to the generation AI and have the generation AI execute the proposal.

[0054] The proposal unit can weight the proposal based on the submission frequency of the requirements at the time of proposal. For example, the proposal unit weights the proposal based on the submission frequency of the requirements at the time of proposal. The proposal unit can weight the proposal based on the submission frequency of the requirements at the time of proposal using the generation AI. For example, the proposal unit increases the weight of the proposal for a requirement that is submitted frequently. The proposal unit can also decrease the weight of the proposal for a requirement that is submitted infrequently. The proposal unit can also dynamically adjust the weight of the proposal based on the submission frequency. In this way, weighting the proposal based on the submission frequency of the requirements enables more appropriate proposals. Some or all of the above-described processing in the proposal unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the proposal unit can input requirement submission frequency data to the generation AI and cause the generation AI to weight the proposals.

[0055] The proposal unit can make a proposal taking into account the geographical distribution of requirements when making a proposal. For example, the proposal unit can make a proposal taking into account the geographical distribution of requirements when making a proposal. The proposal unit can make a proposal taking into account the geographical distribution of requirements using the generation AI when making a proposal. For example, the proposal unit can assign a higher weight to requirements that are geographically concentrated. The proposal unit can also assign a lower weight to requirements that are geographically dispersed. The proposal unit can also improve the accuracy of the proposal based on the geographical distribution. In this way, the accuracy of the proposal is improved by taking into account the geographical distribution of requirements. Some or all of the above-mentioned processing in the proposal unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the proposal unit can input geographical distribution data of requirements to the generation AI and have the generation AI execute the proposal.

[0056] The proposal unit can improve the accuracy of the proposal by referring to literature related to the requirements when making a proposal. For example, the proposal unit can improve the accuracy of the proposal by referring to literature related to the requirements when making a proposal. The proposal unit can improve the accuracy of the proposal by using the generation AI when making a proposal. For example, the proposal unit makes a proposal for a requirement by referring to literature related to the requirements. The proposal unit can also improve the accuracy of the proposal based on information in the literature related to the requirements. The proposal unit can also adjust the criteria for the proposal by referring to literature related to the requirements. In this way, the accuracy of the proposal is improved by referring to literature related to the requirements. Some or all of the above-mentioned processing in the proposal unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the proposal unit can input literature data related to the requirements into the generation AI and have the generation AI execute the proposal.

[0057] The proposal unit can make a proposal taking into account the market value of the requirements when making a proposal. For example, the proposal unit can make a proposal taking into account the market value of the requirements when making a proposal. The proposal unit can make a proposal taking into account the market value of the requirements when making a proposal using the generation AI. For example, the proposal unit can increase the weighting of the proposal for a requirement with a high market value. The proposal unit can also decrease the weighting of the proposal for a requirement with a low market value. The proposal unit can also dynamically adjust the weighting of the proposal based on the market value. In this way, the weighting of the proposal can be dynamically adjusted by taking into account the market value of the requirements. Some or all of the above-mentioned processing in the proposal unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the proposal unit can input market value data of the requirements to the generation AI and have the generation AI execute the proposal.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The requirements analysis unit can analyze the user's past behavioral patterns and propose the optimal requirements collection method. For example, the requirements analysis unit can analyze how the user has submitted requirements in the past and propose the most efficient collection method. The requirements analysis unit can also analyze when the user has submitted requirements in the past and propose the optimal collection timing. Furthermore, the requirements analysis unit can analyze in what format the user has submitted requirements in the past and propose the optimal collection format. This enables more efficient requirements collection by taking into account the user's past behavioral patterns.

[0060] The specification generation unit can customize the method for generating specifications by reflecting past user feedback. For example, the specification generation unit adjusts the method for generating specifications based on feedback provided by the user in the past. The specification generation unit can also learn the optimal expression method from the user's past feedback. Furthermore, the specification generation unit can adjust the specification generation algorithm by reflecting the user's feedback. In this way, more appropriate specifications can be generated by reflecting the user's past feedback.

[0061] The suggestion unit can analyze the user's past suggestion history and select the optimal suggestion method. For example, the suggestion unit analyzes what kind of suggestions the user has accepted in the past and selects the most effective suggestion method. The suggestion unit can also analyze when the user has accepted suggestions in the past and select the optimal suggestion timing. Furthermore, the suggestion unit can analyze in what format the user has accepted suggestions in the past and select the optimal proposal format. This enables more effective suggestions by taking the user's past suggestion history into consideration.

[0062] The specification generation unit can adjust the way the specification is expressed in accordance with the user's level of expertise. For example, if the user has specialized knowledge, the specification generation unit generates a specification that uses a lot of technical terminology. Also, if the user does not have specialized knowledge, the specification generation unit can generate a specification that uses simple language. Furthermore, the specification generation unit can dynamically adjust the way the specification is expressed in accordance with the user's level of expertise. In this way, by adjusting the way the specification is expressed in accordance with the user's level of expertise, it is possible to generate a specification that is easier to understand.

[0063] The risk analysis unit can improve the accuracy of the risk analysis by taking into account the interrelationships between requirements. For example, the risk analysis unit performs risk analysis by taking into account the dependency relationships between requirements. The risk analysis unit can also perform risk analysis by taking into account the interactions between requirements. Furthermore, the risk analysis unit can improve the accuracy of the risk analysis based on the interrelationships between requirements. In this way, the accuracy of the risk analysis is improved by taking into account the interrelationships between requirements.

[0064] The proposal unit can weight proposals by taking into account the market value of the requirements. For example, the proposal unit can increase the weight of proposals for requirements with high market value. The proposal unit can also decrease the weight of proposals for requirements with low market value. Furthermore, the proposal unit can dynamically adjust the weight of proposals based on the market value. This allows the weight of proposals to be dynamically adjusted by taking into account the market value of requirements.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The requirements analysis unit collects system requirements. The requirements analysis unit collects requirements for new system construction or modifications entered by the user in natural language, analyzes the user's input using generative AI, and extracts the requirements. For example, if a user enters, "I want to add a new user management function," the unit analyzes the requirements and extracts them as specific requirements. Step 2: The specification generation unit creates system specifications based on the collected requirements. Using generative AI, the specification generation unit automatically generates system blueprints and functional specifications based on the requirements. For example, it generates specific specifications such as "database design for adding new user management functions" and "user interface specifications." Step 3: The risk analysis unit analyzes the contents of the system specifications and identifies risk factors. The risk analysis unit uses generative AI to analyze the contents of the specifications and identify risk factors for the system. For example, it identifies risk factors such as "database security risks" and "user interface usability issues." Step 4: The proposal department proposes the areas to be checked and the implementation method based on the identified risk factors. Using generative AI, the proposal department proposes which areas should be checked and how to check their operation in response to the identified risk factors. For example, it proposes specific operation check methods such as "database security testing" and "user interface usability testing."

[0067] (Example 2) A system according to an embodiment of the present invention uses a generation AI to automate the creation and updating of system specifications for new system construction and modifications. This system collects system requirements through interaction with the generation AI, creates system specifications based on the collected requirements, performs risk analysis based on the system specifications, and proposes operational check points and implementation methods. For example, a user inputs requirements for new system construction or modifications in natural language, and the generation AI analyzes the requirements and creates a system specification. The generation AI then analyzes the system specification and identifies risk factors. Furthermore, the generation AI proposes operational check points and implementation methods based on the identified risk factors. This significantly reduces the effort required to create and update system specifications and achieves high-quality system management. This system significantly reduces the effort required to create and update specifications and achieves high-quality system management. For example, a user can easily collect system requirements through interaction with the generation AI and automatically create and update specifications. Furthermore, risk analysis and operational check proposals can improve system quality.

[0068] A system according to an embodiment includes a requirements analysis unit, a specification generation unit, a risk analysis unit, and a proposal unit. The requirements analysis unit collects system requirements. For example, the requirements analysis unit collects requirements related to the new construction or modification of a system input by a user in natural language. The requirements analysis unit can analyze the user's input and extract requirements using a generation AI. For example, if a user inputs "I want to add a new user management function," the requirements analysis unit analyzes the requirement and extracts it as a specific requirement. The specification generation unit creates a system specification based on the collected requirements. The specification generation unit automatically generates a system design and functional specifications based on the requirements using a generation AI. For example, the specification generation unit generates specific specifications such as "database design for adding a new user management function" and "user interface specifications." The risk analysis unit analyzes the contents of the system specification and identifies risk factors. The risk analysis unit analyzes the contents of the specification using a generation AI and identifies risk factors for the system. For example, the risk analysis unit identifies risk factors such as "database security risks" and "user interface usability issues." The proposal unit proposes operation check points and implementation methods based on the identified risk factors. The proposal unit uses a generative AI to propose which parts should be checked for the identified risk factors and how. For example, the proposal unit proposes specific operation check methods such as "database security testing" and "user interface usability testing." As a result, the system according to the embodiment automates system requirements collection, specification creation, risk analysis, and operation check proposals, reducing man-hours and achieving high-quality system management.

[0069] The requirements analysis unit can collect requirements for new system construction or modification input by the user in natural language. The requirements analysis unit, for example, collects requirements for new system construction or modification input by the user in natural language. The requirements analysis unit can analyze the user's input and extract requirements using a generation AI. For example, if the user inputs "I want to add a new user management function," the requirements analysis unit analyzes the requirements and extracts them as specific requirements. This makes it easier to collect requirements by having the user input requirements in natural language. Some or all of the above-mentioned processing in the requirements analysis unit may be performed using or without the generation AI. For example, the requirements analysis unit can input the user's natural language input into a generation AI and have the generation AI extract requirements.

[0070] The specification generation unit can automatically generate system design drawings and functional specifications based on the collected requirements. The specification generation unit, for example, automatically generates system design drawings and functional specifications based on the collected requirements. The specification generation unit can automatically generate system design drawings and functional specifications based on the requirements using a generation AI. For example, the specification generation unit generates specific specifications such as "database design for adding a new user management function" and "user interface specifications." This improves the efficiency of specification creation by automatically generating design drawings and functional specifications based on the collected requirements. Some or all of the above-mentioned processing in the specification generation unit may be performed using or without the generation AI. For example, the specification generation unit can input the collected requirements into the generation AI and cause the generation AI to generate system design drawings and functional specifications.

[0071] The risk analysis unit can analyze the contents of the system specifications and identify risk factors for the system. The risk analysis unit can, for example, analyze the contents of the system specifications and identify risk factors for the system. The risk analysis unit can use a generation AI to analyze the contents of the specifications and identify risk factors for the system. For example, the risk analysis unit can identify risk factors such as "database security risks" and "user interface usability issues." This facilitates risk management by analyzing the contents of the system specifications and identifying risk factors. Some or all of the above-mentioned processing in the risk analysis unit may be performed using or without the generation AI. For example, the risk analysis unit can input the system specifications into the generation AI and have the generation AI identify risk factors.

[0072] The suggestion unit can propose which parts should be checked for operation and how for the identified risk factors. For example, the suggestion unit can propose which parts should be checked for operation and how for the identified risk factors. The suggestion unit can use the generation AI to propose which parts should be checked for operation and how for the identified risk factors. For example, the suggestion unit can propose specific operation check methods such as "database security testing" or "user interface usability testing." This improves the quality of the system by proposing an operation check method based on the identified risk factors. Some or all of the above-mentioned processing in the suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input the identified risk factors into the generation AI and have the generation AI execute a proposed operation check method.

[0073] The requirements analysis unit can estimate a user's emotions and adjust the timing of requirements collection based on the estimated user emotions. For example, the requirements analysis unit can estimate a user's emotions and adjust the timing of requirements collection based on the estimated user emotions. The requirements analysis unit can estimate a user's emotions using a generation AI and adjust the timing of requirements collection based on the estimated user emotions. For example, if the user is feeling stressed, the requirements analysis unit can delay the timing of requirements collection and collect requirements in a relaxed state. Furthermore, if the user is concentrating, the requirements analysis unit can advance the timing of requirements collection to collect requirements more efficiently. Furthermore, if the user is tired, the requirements analysis unit can adjust the timing of requirements collection and collect requirements after a break. By adjusting the timing of requirements collection according to the user's emotions, requirements can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can 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 processing in the requirements analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the requirements analysis unit may input user emotion data into the generation AI and have the generation AI adjust the timing of requirements collection.

[0074] The requirements analysis unit can analyze the user's past requirements submission history and select the optimal collection method. For example, the requirements analysis unit analyzes the user's past requirements submission history and selects the optimal collection method. The requirements analysis unit can analyze the user's past requirements submission history and select the optimal collection method using a generation AI. For example, if the user has preferred text input in the past, the requirements analysis unit can preferentially suggest text input. Also, if the user has frequently used voice input in the past, the requirements analysis unit can recommend voice input. Also, if the user has previously submitted requirements using images, the requirements analysis unit can support image input. In this way, the optimal collection method can be selected by analyzing the user's past requirements submission history. Some or all of the above-mentioned processing in the requirements analysis unit may be performed using or without the generation AI. For example, the requirements analysis unit can input the user's past requirements submission history into the generation AI and have the generation AI select the optimal collection method.

[0075] The requirements analysis unit can perform filtering based on the user's current project or areas of interest during requirements collection. For example, the requirements analysis unit can perform filtering based on the user's current project or areas of interest during requirements collection. The requirements analysis unit can use the generation AI to perform filtering based on the user's current project or areas of interest during requirements collection. For example, the requirements analysis unit prioritizes collecting only requirements related to the user's ongoing project. The requirements analysis unit can also filter and collect related requirements based on the user's areas of interest. The requirements analysis unit can also prioritize collecting requirements related to areas in which the user has previously shown interest. This allows highly relevant requirements to be collected by filtering requirements based on the user's current project or areas of interest. Some or all of the above-described processing in the requirements analysis unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the requirements analysis unit can input data on the user's project information and areas of interest into the generation AI and have the generation AI perform the filtering.

[0076] The requirements analysis unit can select the optimal collection means depending on the user's input method when collecting requirements. For example, the requirements analysis unit selects the optimal collection means depending on the user's input method (voice, text, image, etc.) when collecting requirements. The requirements analysis unit can use the generation AI to select the optimal collection means depending on the user's input method when collecting requirements. For example, if the user selects voice input, the requirements analysis unit collects requirements using voice recognition technology. Also, if the user selects text input, the requirements analysis unit can collect requirements using text analysis technology. Also, if the user selects image input, the requirements analysis unit can collect requirements using image analysis technology. This allows for efficient collection of requirements by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the requirements analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the requirements analysis unit can input the user's input data into the generation AI and have the generation AI select the optimal collection means.

[0077] The requirements analysis unit can estimate the user's emotions and determine the priority of requirements to be collected based on the estimated user emotions. The requirements analysis unit can, for example, estimate the user's emotions and determine the priority of requirements to be collected based on the estimated user emotions. The requirements analysis unit can estimate the user's emotions using a generation AI and determine the priority of requirements to be collected based on the estimated user emotions. For example, if the user is nervous, the requirements analysis unit can prioritize collecting requirements of high importance. Also, if the user is relaxed, the requirements analysis unit can also collect requirements of low importance. Also, if the user is in a hurry, the requirements analysis unit can prioritize collecting only the most important requirements. Thus, by determining the priority of requirements according to the user's emotions, important requirements can be prioritized and collected. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 processing in the requirements analysis unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the requirements analysis unit can input user emotional data into the generation AI and have the generation AI determine the priority of the requirements.

[0078] The requirements analysis unit can prioritize collecting highly relevant requirements by taking into account the user's geographical location information when collecting requirements. For example, the requirements analysis unit can prioritize collecting highly relevant requirements by taking into account the user's geographical location information when collecting requirements. The requirements analysis unit can prioritize collecting highly relevant requirements by using the generation AI by taking into account the user's geographical location information when collecting requirements. For example, if the user is in a specific area, the requirements analysis unit can prioritize collecting requirements related to that area. Furthermore, if the user is traveling, the requirements analysis unit can also collect relevant requirements based on the user's current location. Furthermore, if the user is in a specific facility, the requirements analysis unit can prioritize collecting requirements related to that facility. In this way, by taking into account the user's geographical location information, highly relevant requirements can be prioritized. Some or all of the above-described processing in the requirements analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the requirements analysis unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant requirements.

[0079] The requirements analysis unit can analyze the user's social media activities and collect related requirements during requirements collection. For example, the requirements analysis unit can analyze the user's social media activities and collect related requirements during requirements collection. The requirements analysis unit can use the generation AI to analyze the user's social media activities and collect related requirements during requirements collection. For example, the requirements analysis unit prioritizes collecting requirements mentioned by the user on social media. The requirements analysis unit can also analyze the user's social media activities and collect related requirements. The requirements analysis unit can also collect related requirements by referring to the activities of the user's friends on social media. In this way, related requirements can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the requirements analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the requirements analysis unit can input the user's social media data into the generation AI and cause the generation AI to collect related requirements.

[0080] The requirements analysis unit can customize the collection method by reflecting the user's past feedback when collecting requirements. For example, the requirements analysis unit customizes the collection method by reflecting the user's past feedback when collecting requirements. The requirements analysis unit can use the generation AI to customize the collection method by reflecting the user's past feedback when collecting requirements. For example, the requirements analysis unit adjusts the collection method based on feedback provided by the user in the past. The requirements analysis unit can also select the optimal collection means from the user's past feedback. The requirements analysis unit can also customize the collection method by reflecting the user's feedback. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the requirements analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the requirements analysis unit can input the user's past feedback data into the generation AI and have the generation AI customize the collection method.

[0081] The specification generation unit can estimate the user's emotion and adjust the expression of the specification based on the estimated user's emotion. For example, the specification generation unit can estimate the user's emotion and adjust the expression of the specification based on the estimated user's emotion. The specification generation unit can estimate the user's emotion using a generation AI and adjust the expression of the specification based on the estimated user's emotion. For example, if the user is nervous, the specification generation unit can use a simple and clear expression. If the user is relaxed, the specification generation unit can use an expression that includes detailed explanations. If the user is in a hurry, the specification generation unit can use a concise expression that focuses on the main points. In this way, by adjusting the expression of the specification according to the user's emotion, it is possible to generate a specification that is easier to understand. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can 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 processing in the specification generation unit may be performed using or without the generation AI. For example, the specification generation unit may input user emotion data into the generation AI and have the generation AI adjust the expression method of the specification.

[0082] The specification generation unit can adjust the level of detail of the specification based on the importance of the requirements when generating the specification. For example, the specification generation unit adjusts the level of detail of the specification based on the importance of the requirements when generating the specification. The specification generation unit can adjust the level of detail of the specification based on the importance of the requirements when generating the specification using a generation AI. For example, the specification generation unit generates a detailed specification for a requirement with high importance. The specification generation unit can also generate a concise specification for a requirement with low importance. The specification generation unit can also dynamically adjust the level of detail of the specification according to the importance of the requirements. This enables efficient creation of specifications by adjusting the level of detail of the specification based on the importance of the requirements. Some or all of the above-mentioned processing in the specification generation unit may be performed using or without the generation AI. For example, the specification generation unit can input requirement importance data to the generation AI and cause the generation AI to adjust the level of detail of the specification.

[0083] The specification generation unit can apply different generation algorithms depending on the category of requirements when generating specifications. For example, the specification generation unit applies different generation algorithms depending on the category of requirements when generating specifications. The specification generation unit can use a generation AI to apply different generation algorithms depending on the category of requirements when generating specifications. For example, the specification generation unit applies a generation algorithm specialized for database design to database-related requirements. The specification generation unit can also apply a generation algorithm specialized for UI design to user interface-related requirements. The specification generation unit can also apply a generation algorithm specialized for security design to security-related requirements. In this way, by applying a generation algorithm depending on the category of requirements, more appropriate specifications can be generated. Some or all of the above-mentioned processing in the specification generation unit may be performed using or without the generation AI. For example, the specification generation unit can input requirement category data to the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0084] The specification generation unit can improve the accuracy of generation when generating specifications by referring to the user's past specification results. For example, the specification generation unit can improve the accuracy of generation when generating specifications by referring to the user's past specification results. The specification generation unit can improve the accuracy of generation when generating specifications by using a generation AI by referring to the user's past specification results. For example, the specification generation unit can improve the accuracy of generation by referring to specifications created by the user in the past. The specification generation unit can also learn the optimal expression method from the user's past specification results. The specification generation unit can also adjust the generation algorithm based on the user's past specification results. In this way, the accuracy of generation is improved by referring to the user's past specification results. Some or all of the above-mentioned processing in the specification generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the specification generation unit can input the user's past specification data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0085] The specification generation unit can estimate the user's emotion and adjust the length of the specification based on the estimated user emotion. For example, the specification generation unit can estimate the user's emotion and adjust the length of the specification based on the estimated user emotion. The specification generation unit can estimate the user's emotion using a generation AI and adjust the length of the specification based on the estimated user emotion. For example, if the user is in a hurry, the specification generation unit can generate a short and to-the-point specification. Alternatively, if the user is relaxed, the specification generation unit can generate a longer specification with detailed explanations. Alternatively, if the user is excited, the specification generation unit can generate a specification with visually stimulating effects. This allows the length of the specification to be adjusted according to the user's emotion, thereby generating a more appropriate specification. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can 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 processing in the specification generation unit may be performed using or without the generation AI. For example, the specification generation unit may input user emotion data into the generation AI and have the generation AI adjust the length of the specification.

[0086] The specification generation unit can determine the priority of specifications based on the submission dates of requirements when generating specifications. For example, the specification generation unit determines the priority of specifications based on the submission dates of requirements when generating specifications. The specification generation unit can use a generation AI to determine the priority of specifications based on the submission dates of requirements when generating specifications. For example, the specification generation unit prioritizes generating specifications for recently submitted requirements. The specification generation unit can also postpone generating specifications for older submitted requirements. The specification generation unit can also dynamically adjust the order in which specifications are generated based on the submission dates. This enables efficient specification creation by determining the priority of specifications based on the submission dates of requirements. Some or all of the above-described processing in the specification generation unit may be performed using or without the generation AI. For example, the specification generation unit can input requirement submission date data into the generation AI and have the generation AI determine the priority of specifications.

[0087] The specification generation unit can adjust the order of the specifications based on the relevance of the requirements when generating the specifications. For example, the specification generation unit adjusts the order of the specifications based on the relevance of the requirements when generating the specifications. The specification generation unit can adjust the order of the specifications based on the relevance of the requirements when generating the specifications using a generation AI. For example, the specification generation unit prioritizes highly relevant requirements when describing them in the specifications. The specification generation unit can also postpone the description of less relevant requirements when describing them in the specifications. The specification generation unit can also dynamically adjust the order of the specifications based on the relevance of the requirements. In this way, by adjusting the order of the specifications based on the relevance of the requirements, it is possible to generate specifications that are easier to understand. Some or all of the above-mentioned processing in the specification generation unit may be performed using or without the generation AI. For example, the specification generation unit can input requirement relevance data into the generation AI and cause the generation AI to adjust the order of the specifications.

[0088] The specification generation unit can adjust the use of technical terms in the specification according to the user's level of expertise when generating the specification. For example, the specification generation unit can adjust the use of technical terms in the specification according to the user's level of expertise when generating the specification. The specification generation unit can use a generation AI to adjust the use of technical terms in the specification according to the user's level of expertise when generating the specification. For example, if the user has technical expertise, the specification generation unit can generate a specification that uses a lot of technical terms. Also, if the user does not have technical expertise, the specification generation unit can generate a specification that uses simple language. The specification generation unit can also dynamically adjust the use of technical terms in the specification according to the user's level of expertise. This makes it possible to generate a more appropriate specification by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-mentioned processing in the specification generation unit may be performed using or without the generation AI. For example, the specification generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0089] The risk analysis unit can estimate the user's emotions and adjust the risk analysis criteria based on the estimated user emotions. For example, the risk analysis unit can estimate the user's emotions and adjust the risk analysis criteria based on the estimated user emotions. The risk analysis unit can estimate the user's emotions using a generation AI and adjust the risk analysis criteria based on the estimated user emotions. For example, the risk analysis unit can set stricter risk analysis criteria when the user is nervous. The risk analysis unit can also set lenient risk analysis criteria when the user is relaxed. The risk analysis unit can also simplify the risk analysis criteria when the user is in a hurry. This enables more appropriate risk analysis by adjusting the risk analysis criteria according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the risk analysis unit may be performed using a generation AI or without a generation AI. For example, the risk analysis unit can input the user's emotional data into the generation AI and have the generation AI adjust the criteria for risk analysis.

[0090] The risk analysis unit can improve the accuracy of the risk analysis by taking into account the interrelationships between requirements during risk analysis. For example, the risk analysis unit can improve the accuracy of the risk analysis by taking into account the interrelationships between requirements during risk analysis. The risk analysis unit can improve the accuracy of the risk analysis by using a generative AI to take into account the interrelationships between requirements during risk analysis. For example, the risk analysis unit performs risk analysis by taking into account dependencies between requirements. The risk analysis unit can also perform risk analysis by taking into account interactions between requirements. The risk analysis unit can also improve the accuracy of the risk analysis based on the interrelationships between requirements. In this way, the accuracy of the risk analysis is improved by taking into account the interrelationships between requirements. Some or all of the above-mentioned processing in the risk analysis unit may be performed using or without the generative AI. For example, the risk analysis unit can input requirement interrelationship data into the generative AI and cause the generative AI to improve the accuracy of the risk analysis.

[0091] The risk analysis unit can perform risk analysis by taking into account attribute information of the requirement submitter during risk analysis. For example, the risk analysis unit can perform risk analysis by taking into account attribute information of the requirement submitter during risk analysis. The risk analysis unit can perform risk analysis by using a generation AI and taking into account attribute information of the requirement submitter during risk analysis. For example, if the requirement submitter is an expert, the risk analysis unit can evaluate the risk of the requirement as low. Also, if the requirement submitter is a novice, the risk analysis unit can evaluate the risk of the requirement as high. Also, the risk analysis unit can improve the accuracy of the risk analysis based on the attribute information of the requirement submitter. In this way, the accuracy of the risk analysis is improved by taking into account the attribute information of the requirement submitter. Some or all of the above-mentioned processing in the risk analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the risk analysis unit can input attribute information of the requirement submitter into the generation AI and have the generation AI perform the risk analysis.

[0092] The risk analysis unit can weight the risk analysis based on the frequency of requirement submission during risk analysis. For example, the risk analysis unit weights the risk analysis based on the frequency of requirement submission during risk analysis. The risk analysis unit can weight the risk analysis based on the frequency of requirement submission during risk analysis using a generation AI. For example, the risk analysis unit may evaluate the risk higher for requirements that are submitted frequently. The risk analysis unit can also evaluate the risk lower for requirements that are submitted infrequently. The risk analysis unit can also dynamically adjust the weighting of the risk analysis based on the submission frequency. This enables more appropriate risk assessment by weighting the risk analysis based on the frequency of requirement submission. Some or all of the above-described processing in the risk analysis unit may be performed using or without the generation AI. For example, the risk analysis unit may input requirement submission frequency data into the generation AI and have the generation AI perform the weighting of the risk analysis.

[0093] The risk analysis unit can estimate the user's emotions and adjust the order in which the risk analysis results are displayed based on the estimated user emotions. For example, the risk analysis unit can estimate the user's emotions and adjust the order in which the risk analysis results are displayed based on the estimated user emotions. The risk analysis unit can use a generation AI to estimate the user's emotions and adjust the order in which the risk analysis results are displayed based on the estimated user emotions. For example, if the user is nervous, the risk analysis unit can display the most important risks first. If the user is relaxed, the risk analysis unit can also display all risks in detail. If the user is in a hurry, the risk analysis unit can also display risks that are summarized first. This allows for adjusting the order in which the risk analysis results are displayed based on the user's emotions, thereby providing risk analysis results that are easier to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can 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 processing in the risk analysis unit may be performed using or without the generation AI. For example, the risk analysis unit can input the user's emotional data into the generation AI and have the generation AI adjust the display order of the risk analysis results.

[0094] The risk analysis unit can perform risk analysis taking into account the geographical distribution of requirements during risk analysis. For example, the risk analysis unit can perform risk analysis taking into account the geographical distribution of requirements during risk analysis. The risk analysis unit can perform risk analysis taking into account the geographical distribution of requirements using a generation AI during risk analysis. For example, the risk analysis unit can evaluate the risk higher for requirements that are geographically concentrated. The risk analysis unit can also evaluate the risk lower for requirements that are geographically dispersed. The risk analysis unit can also improve the accuracy of the risk analysis based on the geographical distribution. In this way, the accuracy of the risk analysis is improved by considering the geographical distribution of requirements. Some or all of the above-mentioned processing in the risk analysis unit may be performed using or without the generation AI. For example, the risk analysis unit can input geographical distribution data of requirements into the generation AI and have the generation AI perform the risk analysis.

[0095] The risk analysis unit can improve the accuracy of the risk analysis by referring to literature related to the requirements during risk analysis. For example, the risk analysis unit can improve the accuracy of the risk analysis by referring to literature related to the requirements during risk analysis. The risk analysis unit can improve the accuracy of the risk analysis by using the generation AI during risk analysis. For example, the risk analysis unit can evaluate the risk of the requirements by referring to literature related to the requirements. The risk analysis unit can also improve the accuracy of the risk analysis based on information in the literature related to the requirements. The risk analysis unit can also adjust the criteria for the risk analysis by referring to literature related to the requirements. In this way, the accuracy of the risk analysis is improved by referring to literature related to the requirements. Some or all of the above-mentioned processing in the risk analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the risk analysis unit can input literature data related to the requirements into the generation AI and have the generation AI perform the risk analysis.

[0096] The risk analysis unit can perform risk analysis taking into account the market value of requirements during risk analysis. For example, the risk analysis unit can perform risk analysis taking into account the market value of requirements during risk analysis. The risk analysis unit can perform risk analysis taking into account the market value of requirements using a generation AI during risk analysis. For example, the risk analysis unit can evaluate the risk higher for requirements with high market value. The risk analysis unit can also evaluate the risk lower for requirements with low market value. The risk analysis unit can also dynamically adjust the weighting of the risk analysis based on the market value. In this way, the weighting of the risk analysis can be dynamically adjusted by taking into account the market value of the requirements. Some or all of the above-mentioned processing in the risk analysis unit may be performed using or without the generation AI. For example, the risk analysis unit can input market value data of requirements to the generation AI and have the generation AI perform the risk analysis.

[0097] The suggestion unit can estimate the user's emotions and prioritize suggestions based on the estimated user emotions. For example, the suggestion unit can estimate the user's emotions and prioritize suggestions based on the estimated user emotions. The suggestion unit can estimate the user's emotions using a generation AI and prioritize suggestions based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can make the most important suggestions first. If the user is relaxed, the suggestion unit can make all suggestions in detail. If the user is in a hurry, the suggestion unit can make suggestions that focus on the main points first. This enables more appropriate suggestions by prioritizing suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input the user's emotional data into the generation AI and have the generation AI determine the priority of the suggestions.

[0098] The proposal unit can improve the accuracy of the proposal by taking into account the interrelationships between requirements when making a proposal. For example, the proposal unit can improve the accuracy of the proposal by taking into account the interrelationships between requirements when making a proposal. The proposal unit can improve the accuracy of the proposal by using the generation AI to take into account the interrelationships between requirements when making a proposal. For example, the proposal unit makes a proposal by taking into account the dependency relationships between requirements. The proposal unit can also make a proposal by taking into account the interactions between requirements. The proposal unit can also improve the accuracy of the proposal based on the interrelationships between requirements. In this way, the accuracy of the proposal is improved by taking into account the interrelationships between requirements. Some or all of the above-mentioned processing in the proposal unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the proposal unit can input requirement interrelationship data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0099] The proposal unit can make a proposal by taking into account attribute information of the requirement submitter when making a proposal. For example, the proposal unit can make a proposal by taking into account attribute information of the requirement submitter when making a proposal. The proposal unit can use the generation AI to make a proposal by taking into account attribute information of the requirement submitter when making a proposal. For example, if the requirement submitter is an expert, the proposal unit can make an advanced proposal for the requirement. Also, if the requirement submitter is a novice, the proposal unit can make a basic proposal for the requirement. Also, the proposal unit can improve the accuracy of the proposal based on the attribute information of the requirement submitter. In this way, the accuracy of the proposal is improved by taking into account the attribute information of the requirement submitter. Some or all of the above-mentioned processing in the proposal unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the proposal unit can input attribute information of the requirement submitter to the generation AI and have the generation AI execute the proposal.

[0100] The proposal unit can weight the proposal based on the submission frequency of the requirements at the time of proposal. For example, the proposal unit weights the proposal based on the submission frequency of the requirements at the time of proposal. The proposal unit can weight the proposal based on the submission frequency of the requirements at the time of proposal using the generation AI. For example, the proposal unit increases the weight of the proposal for a requirement that is submitted frequently. The proposal unit can also decrease the weight of the proposal for a requirement that is submitted infrequently. The proposal unit can also dynamically adjust the weight of the proposal based on the submission frequency. In this way, weighting the proposal based on the submission frequency of the requirements enables more appropriate proposals. Some or all of the above-described processing in the proposal unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the proposal unit can input requirement submission frequency data to the generation AI and cause the generation AI to weight the proposals.

[0101] The suggestion unit can estimate the user's emotions and adjust the display method of suggestions based on the estimated user emotions. For example, the suggestion unit can estimate the user's emotions and adjust the display method of suggestions based on the estimated user emotions. The suggestion unit can estimate the user's emotions using a generation AI and adjust the display method of suggestions based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can provide a simple, highly visible display method. If the user is relaxed, the suggestion unit can provide a display method including detailed information. If the user is in a hurry, the suggestion unit can provide a display method that focuses on the main points. This allows the suggestion display method to be adjusted according to the user's emotions, making it easier to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the way the suggestions are displayed.

[0102] The proposal unit can make a proposal taking into account the geographical distribution of requirements when making a proposal. For example, the proposal unit can make a proposal taking into account the geographical distribution of requirements when making a proposal. The proposal unit can make a proposal taking into account the geographical distribution of requirements using the generation AI when making a proposal. For example, the proposal unit can assign a higher weight to requirements that are geographically concentrated. The proposal unit can also assign a lower weight to requirements that are geographically dispersed. The proposal unit can also improve the accuracy of the proposal based on the geographical distribution. In this way, the accuracy of the proposal is improved by taking into account the geographical distribution of requirements. Some or all of the above-mentioned processing in the proposal unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the proposal unit can input geographical distribution data of requirements to the generation AI and have the generation AI execute the proposal.

[0103] The proposal unit can improve the accuracy of the proposal by referring to literature related to the requirements when making a proposal. For example, the proposal unit can improve the accuracy of the proposal by referring to literature related to the requirements when making a proposal. The proposal unit can improve the accuracy of the proposal by using the generation AI when making a proposal. For example, the proposal unit makes a proposal for a requirement by referring to literature related to the requirements. The proposal unit can also improve the accuracy of the proposal based on information in the literature related to the requirements. The proposal unit can also adjust the criteria for the proposal by referring to literature related to the requirements. In this way, the accuracy of the proposal is improved by referring to literature related to the requirements. Some or all of the above-mentioned processing in the proposal unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the proposal unit can input literature data related to the requirements into the generation AI and have the generation AI execute the proposal.

[0104] The proposal unit can make a proposal taking into account the market value of the requirements when making a proposal. For example, the proposal unit can make a proposal taking into account the market value of the requirements when making a proposal. The proposal unit can make a proposal taking into account the market value of the requirements when making a proposal using the generation AI. For example, the proposal unit can increase the weighting of the proposal for a requirement with a high market value. The proposal unit can also decrease the weighting of the proposal for a requirement with a low market value. The proposal unit can also dynamically adjust the weighting of the proposal based on the market value. In this way, the weighting of the proposal can be dynamically adjusted by taking into account the market value of the requirements. Some or all of the above-mentioned processing in the proposal unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the proposal unit can input market value data of the requirements to the generation AI and have the generation AI execute the proposal. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned requirements analysis unit, specification generation unit, risk analysis unit, and proposal unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the requirements analysis unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the specification generation unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the risk analysis unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the proposal unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned requirements analysis unit, specification generation unit, risk analysis unit, and proposal unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the requirements analysis unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the specification generation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the risk analysis unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the proposal unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned requirements analysis unit, specification generation unit, risk analysis unit, and proposal unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the requirements analysis unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the specification generation unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the risk analysis unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the proposal unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned requirements analysis unit, specification generation unit, risk analysis unit, and proposal unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the requirements analysis unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the specification generation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the risk analysis unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the proposal unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The requirements analysis unit can analyze the user's past behavioral patterns and propose the optimal requirements collection method. For example, the requirements analysis unit can analyze how the user has submitted requirements in the past and propose the most efficient collection method. The requirements analysis unit can also analyze when the user has submitted requirements in the past and propose the optimal collection timing. Furthermore, the requirements analysis unit can analyze in what format the user has submitted requirements in the past and propose the optimal collection format. This enables more efficient requirements collection by taking into account the user's past behavioral patterns.

[0107] The requirements analysis unit can estimate the user's emotions and adjust the requirements collection method based on the estimated user emotions. For example, if the user is feeling stressed, the requirements analysis unit can collect requirements in the form of simple questions. If the user is relaxed, the requirements analysis unit can also collect requirements in the form of detailed questions. Furthermore, if the user is concentrating, the requirements analysis unit can collect multiple requirements at once. This allows for more appropriate requirements collection by adjusting the requirements collection method according to the user's emotions.

[0108] The specification generation unit can customize the method for generating specifications by reflecting past user feedback. For example, the specification generation unit adjusts the method for generating specifications based on feedback provided by the user in the past. The specification generation unit can also learn the optimal expression method from the user's past feedback. Furthermore, the specification generation unit can adjust the specification generation algorithm by reflecting the user's feedback. In this way, more appropriate specifications can be generated by reflecting the user's past feedback.

[0109] The risk analysis unit can estimate the user's emotions and adjust the way in which the risk analysis results are displayed based on the estimated user's emotions. For example, if the user is nervous, the risk analysis unit can display the most important risks first. If the user is relaxed, the risk analysis unit can also display all risks in detail. Furthermore, if the user is in a hurry, the risk analysis unit can also display the most important risks first. In this way, by adjusting the way in which the risk analysis results are displayed according to the user's emotions, it is possible to provide risk analysis results that are easier to understand.

[0110] The suggestion unit can analyze the user's past suggestion history and select the optimal suggestion method. For example, the suggestion unit analyzes what kind of suggestions the user has accepted in the past and selects the most effective suggestion method. The suggestion unit can also analyze when the user has accepted suggestions in the past and select the optimal suggestion timing. Furthermore, the suggestion unit can analyze in what format the user has accepted suggestions in the past and select the optimal proposal format. This enables more effective suggestions by taking the user's past suggestion history into consideration.

[0111] The requirements analysis unit can estimate the user's emotions and determine the priority of requirements collection based on the estimated user's emotions. For example, if the user is nervous, the requirements analysis unit will prioritize collecting requirements of high importance. Also, if the user is relaxed, the requirements analysis unit can collect requirements including requirements of low importance. Furthermore, if the user is in a hurry, the requirements analysis unit can prioritize collecting only the most important requirements. In this way, by determining the priority of requirements according to the user's emotions, important requirements can be collected with priority.

[0112] The specification generation unit can adjust the way the specification is expressed in accordance with the user's level of expertise. For example, if the user has specialized knowledge, the specification generation unit generates a specification that uses a lot of technical terminology. Also, if the user does not have specialized knowledge, the specification generation unit can generate a specification that uses simple language. Furthermore, the specification generation unit can dynamically adjust the way the specification is expressed in accordance with the user's level of expertise. In this way, by adjusting the way the specification is expressed in accordance with the user's level of expertise, it is possible to generate a specification that is easier to understand.

[0113] The risk analysis unit can improve the accuracy of the risk analysis by taking into account the interrelationships between requirements. For example, the risk analysis unit performs risk analysis by taking into account the dependency relationships between requirements. The risk analysis unit can also perform risk analysis by taking into account the interactions between requirements. Furthermore, the risk analysis unit can improve the accuracy of the risk analysis based on the interrelationships between requirements. In this way, the accuracy of the risk analysis is improved by taking into account the interrelationships between requirements.

[0114] The suggestion unit can estimate the user's emotions and adjust the display method of suggestions based on the estimated user's emotions. For example, if the user is nervous, the suggestion unit can provide a simple, highly visible display method. If the user is relaxed, the suggestion unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the suggestion unit can also provide a display method that focuses on the main points. In this way, by adjusting the display method of suggestions according to the user's emotions, it becomes possible to provide suggestions that are easier to understand.

[0115] The proposal unit can weight proposals by taking into account the market value of the requirements. For example, the proposal unit can increase the weight of proposals for requirements with high market value. The proposal unit can also decrease the weight of proposals for requirements with low market value. Furthermore, the proposal unit can dynamically adjust the weight of proposals based on the market value. This allows the weight of proposals to be dynamically adjusted by taking into account the market value of requirements.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The requirements analysis unit collects system requirements. The requirements analysis unit collects requirements for new system construction or modifications entered by the user in natural language, analyzes the user's input using generative AI, and extracts the requirements. For example, if a user enters, "I want to add a new user management function," the unit analyzes the requirements and extracts them as specific requirements. Step 2: The specification generation unit creates system specifications based on the collected requirements. Using generative AI, the specification generation unit automatically generates system blueprints and functional specifications based on the requirements. For example, it generates specific specifications such as "database design for adding new user management functions" and "user interface specifications." Step 3: The risk analysis unit analyzes the contents of the system specifications and identifies risk factors. The risk analysis unit uses generative AI to analyze the contents of the specifications and identify risk factors for the system. For example, it identifies risk factors such as "database security risks" and "user interface usability issues." Step 4: The proposal department proposes the areas to be checked and the implementation method based on the identified risk factors. Using generative AI, the proposal department proposes which areas should be checked and how to check their operation in response to the identified risk factors. For example, it proposes specific operation check methods such as "database security testing" and "user interface usability testing."

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0124] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0140] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0156] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0162] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0168] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0172] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0173] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0174] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0175] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0176] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0177] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0178] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0179] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0180] 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.

[0181] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0182] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0183] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0184] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0185] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0186] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0187] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0188] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0189] [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a requirements analysis unit that collects system requirements; a specification generation unit that generates a system specification based on the requirements collected by the requirement analysis unit; a risk analysis unit that performs risk analysis based on the system specifications created by the specification creation unit; a proposal unit that proposes operation check points and implementation methods based on the risk factors identified by the risk analysis unit; Equipped with A system characterized by:

2. The requirement analysis unit Collect requirements for new system development or modifications entered by users in natural language 2. The system of claim 1.

3. The specification generation unit Automatically generate system blueprints and functional specifications based on collected requirements 2. The system of claim 1.

4. The risk analysis unit Analyze the contents of the system specifications and identify the risk factors of the system 2. The system of claim 1.

5. The proposal unit Propose which parts and how to check the operation for the identified risk factors 2. The system of claim 1.

6. The requirement analysis unit Infer user emotions and adjust the timing of requirements gathering based on the inferred user emotions 2. The system of claim 1.

7. The requirement analysis unit Analyze the user's past requirements submission history and select the optimal collection method 2. The system of claim 1.

8. The requirement analysis unit Filter requirements gathering based on your current projects and areas of interest 2. The system of claim 1.

9. The requirement analysis unit When gathering requirements, select the most appropriate collection method depending on the user's input method.

2. The system of claim 1.

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