system

Abstract

The system according to this embodiment aims to automatically propose the optimal IT solution for business challenges and requirements. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and an interface unit. The reception unit receives input of business issues and requirements from users. The analysis unit analyzes the information received by the reception unit. The generation unit proposes optimal IT solutions and system designs based on the information analyzed by the analysis unit. The interface unit shares the proposals and roadmaps generated by the generation unit with relevant parties.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, the proposal of an optimal IT solution for business issues and requirements depends on human resources, and there is room for efficiency improvement.

[0005] The system according to the embodiment aims to automatically propose an optimal IT solution for business issues and requirements.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and an interface unit. The reception unit receives input of business challenges and requirements from users. The analysis unit analyzes the information received by the reception unit. The generation unit proposes optimal IT solutions and system designs based on the information analyzed by the analysis unit. The interface unit shares the proposals and roadmaps generated by the generation unit with relevant parties. [Effects of the Invention]

[0007] The system according to this embodiment can automatically propose the optimal IT solution for business challenges and requirements. [Brief explanation of the drawing]

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

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0028] (Example of form 1) The IT solution proposal system according to an embodiment of the present invention is a system that automatically proposes optimal IT solutions and system designs when a client inputs their business challenges and requirements. This system analyzes the input information using natural language processing and generative AI, and proposes optimal IT solutions and system designs based on the user's input of business challenges and requirements. These proposals are automatically generated as professional proposal documents and roadmaps, which can be shared with stakeholders. Furthermore, the system interacts with the user through an interactive interface, gathering additional information and fine-tuning the proposals. For example, if a user inputs that they are a "non-IT company considering new IT implementation" and "want to promote DX but don't know where to start," the system analyzes this challenge and proposes the optimal IT solution. The proposal includes specific system designs, implementation plans, and cost estimates. This system uses natural language processing to understand user input and generate accurate proposals. It also utilizes knowledge graphs and machine learning to learn the latest IT solutions and technology trends, reflecting them in the proposals. Additionally, it uses generative AI to automatically create professional proposal documents and roadmaps, which can be shared with stakeholders. This will compensate for the shortage of human resources among IT consultants and shorten the lead time to proposals. It will also reduce and streamline consulting costs. For example, traditional consulting services often take several weeks from initial consultation to proposal creation, but with this system, proposals can be received within a few days. Furthermore, this system interacts with users through an interactive interface, gathering additional information and fine-tuning the proposal. This enables flexible proposals tailored to user needs. For example, if a user inputs additional requirements for the proposal, the system analyzes those requirements and updates the proposal. This system aims to democratize IT consulting and create an environment where all companies, regardless of size or industry, can receive high-quality IT consulting.This is expected to contribute to improving productivity in Japan and creating new markets and value. As a result, the IT solution proposal system can efficiently analyze clients' business challenges and requirements and propose optimal IT solutions and system designs.

[0029] The IT solution proposal system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and an interface unit. The reception unit receives input of business issues and requirements from the user. For example, the reception unit can receive business issues and requirements entered by the user in text format. The reception unit can also receive voice input. For example, if the user enters business issues or requirements by voice, the reception unit converts the voice into text and receives it. Furthermore, the reception unit can store the information entered by the user in a database. The analysis unit analyzes the information received by the reception unit. For example, the analysis unit uses natural language processing technology to analyze the business issues and requirements entered by the user. The analysis unit uses generation AI to analyze the information entered by the user and generates data for proposing the optimal IT solution and system design. The generation unit proposes the optimal IT solution and system design based on the information analyzed by the analysis unit. For example, the generation unit uses generation AI to propose the optimal IT solution for the user's business issues and requirements. The generation unit can use generation AI to automatically create professional proposals and roadmaps. The generation unit automatically generates proposals based on the user's business challenges and requirements, for example, using generation AI. The generation unit can also automatically generate roadmaps based on the user's business challenges and requirements, using generation AI. The interface unit shares the proposals and roadmaps generated by the generation unit with relevant parties. For example, the interface unit can send the generated proposals and roadmaps to relevant parties via email. The interface unit can also share the generated proposals and roadmaps with relevant parties through a web application. Furthermore, the interface unit can print the generated proposals and roadmaps and distribute them to relevant parties. As a result, the IT solution proposal system according to this embodiment can efficiently analyze the user's business challenges and requirements and propose optimal IT solutions and system designs.

[0030] The reception desk receives input of business issues and requirements from users. For example, the reception desk can receive business issues and requirements entered by users in text format. Specifically, when a user enters business issues or requirements into a web form, the reception desk receives the content in real time and saves it to the database. The reception desk can also accept voice input. For example, if a user enters business issues or requirements by voice, the reception desk converts the voice into text and receives it. Using speech recognition technology, it converts the user's voice into text with high accuracy and is equipped with a feedback function to minimize misrecognition. Furthermore, the reception desk can save the information entered by the user to the database. The saved data is centrally managed so that subsequent analysis and generation units can access it, and it can be updated or modified as needed. This allows the reception desk to handle various input formats from users and collect information accurately and efficiently. In addition, the reception desk is equipped with a function to automatically send confirmation messages to the user's input content, prompting them to check and correct the input content. This allows the user to reconfirm their input content and correct it as needed, improving the reliability and accuracy of the entire system.

[0031] The analysis unit analyzes the information received by the reception unit. For example, the analysis unit uses natural language processing technology to analyze the business challenges and requirements entered by the user. Specifically, it uses natural language processing technology to tokenize the user's input and perform grammatical and semantic analysis. This allows it to extract the intent and important keywords of the user's business challenges and requirements. The analysis unit uses generative AI to analyze the user's input and generate data to propose optimal IT solutions and system designs. The generative AI learns from past data and case studies to build a model for proposing the best solutions to the user's business challenges. For example, if the user's business challenge is "improvement of the customer management system," the analysis unit will refer to past case studies and solutions related to customer management and generate data to propose the best improvement measures. Furthermore, the analysis unit can also perform risk assessments and cost analyses for the user's business challenges and requirements. This allows the user to understand the feasibility and cost-effectiveness of the proposed solutions in advance. Based on the generated data, the analysis unit provides a foundation for proposing the best IT solutions to the user's business challenges.

[0032] The generation unit proposes optimal IT solutions and system designs based on the information analyzed by the analysis unit. For example, the generation unit uses generation AI to propose IT solutions best suited to the user's business challenges and requirements. The generation AI automatically generates specific solutions to the user's business challenges based on the data provided by the analysis unit. For example, if the user's business challenge is "improvement of the customer management system," the generation unit proposes the functions and technologies necessary for improving the customer management system and designs a system based on them. The generation unit can automatically create professional proposals and roadmaps using generation AI. For example, the generation unit can automatically generate proposals based on the user's business challenges and requirements using generation AI. The proposal includes an overview of the proposed IT solution, the benefits of implementation, the implementation schedule, and cost estimates. The generation unit can also automatically generate roadmaps based on the user's business challenges and requirements using generation AI. The roadmap shows the implementation steps and schedule of the proposed IT solution, as well as the specific work content and goals at each step. This allows the generation unit to propose concrete and feasible IT solutions to the user and provide clear guidance for implementation. Furthermore, the generation unit can flexibly modify the content of proposals and roadmaps based on user feedback. This allows for quick responses to user requests and changes, enabling the provision of optimal proposals.

[0033] The interface unit shares proposals and roadmaps generated by the generation unit with stakeholders. For example, the interface unit can send generated proposals and roadmaps to stakeholders via email. Specifically, it converts proposals and roadmaps created by the generation unit into file formats such as PDF or Word and sends them to the stakeholders' email addresses. The interface unit can also share generated proposals and roadmaps with stakeholders through a web application. The web application provides functions that allow stakeholders to view proposals and roadmaps and provide comments and feedback. Furthermore, the interface unit can print and distribute generated proposals and roadmaps to stakeholders. This allows stakeholders to refer to proposals and roadmaps as physical documents. The interface unit facilitates communication with stakeholders and supports a deeper understanding of the proposal content. For example, it collects feedback from stakeholders and reflects that feedback in the generation unit to improve the accuracy and effectiveness of the proposal content. The interface unit also supports the effective use of generated proposals and roadmaps during meetings and presentations with stakeholders. This allows the interface unit to share the proposals and roadmaps generated by the generation unit with stakeholders, facilitating understanding and consensus building on the proposed content.

[0034] The generation unit can automatically create professional proposals and roadmaps using generational AI. For example, the generation unit can automatically generate proposals based on the user's business challenges and requirements using generational AI. The generation unit can also automatically generate roadmaps based on the user's business challenges and requirements using generational AI. For example, when creating proposals based on the user's business challenges and requirements using generational AI, the generation unit can include content compliant with industry standards and detailed analysis results. When creating roadmaps based on the user's business challenges and requirements using generational AI, the generation unit can clearly show project progress and key milestones. This allows for the automatic creation of professional proposals and roadmaps using generational AI. The generation AI can, for example, use text generation AI (e.g., large-scale language models such as Transformer models). The generation AI takes the user's business challenges and requirements as input and outputs professional proposals and roadmaps. For example, the generation AI can receive a prompt such as "Propose the optimal IT solution for the user's business challenges" and generate a proposal or roadmap.

[0035] The interface unit can interact with the user through an interactive interface, gather additional information, and fine-tune the proposed content. For example, the interface unit can use a chatbot to interact with the user. Using a chatbot, the interface unit can gather additional information from the user and fine-tune the proposed content. The interface unit can also use a voice assistant to interact with the user. Using a voice assistant, the interface unit can gather additional information from the user and fine-tune the proposed content. For example, if the user inputs additional requirements for the proposed content, the interface unit can analyze those requirements and update the proposed content. For example, if the user inputs additional requirements via a chatbot, the interface unit analyzes those requirements and updates the proposed content. For example, if the user inputs additional requirements via a voice assistant, the interface unit analyzes those requirements and updates the proposed content. This allows for gathering additional information and fine-tuning of proposed content through interaction with the user via an interactive interface. The interactive interface can utilize, for example, a chatbot or a voice assistant. Interactive interfaces can adjust their suggestions based on user feedback. For example, if a user provides feedback on a suggestion via a chatbot, the interface analyzes that feedback and adjusts the suggestion accordingly. Interactive interfaces can provide flexible suggestions tailored to the user's needs.

[0036] The analysis unit can learn the latest IT solutions and technology trends using knowledge graphs and machine learning, and reflect them in its proposals. For example, the analysis unit learns the latest IT solutions and technology trends using a knowledge graph. The analysis unit uses a knowledge graph to collect information from relevant data sources, define nodes and edges, and construct the knowledge graph. The analysis unit learns the latest IT solutions and technology trends using machine learning algorithms. The analysis unit uses machine learning algorithms to analyze the collected data and reflect it in its proposals. The analysis unit learns the latest IT solutions and technology trends using deep learning. The analysis unit learns from large amounts of data using deep learning and reflects it in its proposals. The analysis unit learns the latest IT solutions and technology trends using reinforcement learning. The analysis unit uses reinforcement learning to optimize its proposals. This allows the analysis unit to learn the latest IT solutions and technology trends and reflect them in its proposals by utilizing knowledge graphs and machine learning. The knowledge graph includes, for example, data sources, node and edge definitions, etc. Machine learning can utilize algorithms such as deep learning and reinforcement learning. The latest IT solutions and technological trends include, for example, cloud computing, AI technology, and blockchain.

[0037] The generation unit can automate the following processes: current situation analysis / market research, solution research, planning / proposal, project plan formulation, requirements definition, design / development / testing, implementation, operation, and support. For example, the generation unit can automate current situation analysis / market research. The generation unit uses generational AI to conduct current situation analysis / market research and collect data based on the user's business challenges and requirements. For example, the generation unit can automate solution research. The generation unit uses generational AI to research the optimal solution for the user's business challenges and requirements. For example, the generation unit can automate planning / proposal. The generation unit uses generational AI to create plans and proposals based on the user's business challenges and requirements. For example, the generation unit can automate project plan formulation. The generation unit uses generational AI to formulate project plans based on the user's business challenges and requirements. For example, the generation unit can automate requirements definition. The generation unit uses generational AI to define requirements based on the user's business challenges and requirements. For example, the generation unit can automate design / development / testing. The generation unit uses generational AI to design, develop, and test based on the user's business challenges and requirements. For example, the generation unit automates implementation. The generation unit uses generational AI to implement based on the user's business challenges and requirements. For example, the generation unit automates operation. The generation unit uses generational AI to perform operations based on the user's business challenges and requirements. For example, the generation unit automates support. The generation unit uses generational AI to provide support based on the user's business challenges and requirements. By automating each process, it becomes possible to efficiently propose IT solutions and system designs. Each process includes, for example, current situation analysis and market research, solution research, planning and proposal, project planning, requirements definition, design, development and testing, implementation, operation, and support.

[0038] The interface unit can analyze additional requirements entered by the user and update the proposed content. For example, if the user enters additional requirements via a chatbot, the interface unit can analyze those requirements and update the proposed content. For example, if the user enters additional requirements via a voice assistant, the interface unit can analyze those requirements and update the proposed content. For example, if the user enters additional requirements via a web form, the interface unit can analyze those requirements and update the proposed content. Additional requirements include, for example, functional requirements and non-functional requirements. Some or all of the above processing in the interface unit may be performed using, for example, AI, or not using AI. For example, the interface unit can input the additional requirements entered by the user into a generating AI and have the generating AI perform the update of the proposed content.

[0039] The reception desk can analyze the user's past input history of business challenges and requirements and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk can predict and suggest input methods to be used during specific time periods based on the user's past input history. For example, the reception desk can provide input assistance functions by referring to content that the user has entered in the past. This allows for the selection of the optimal input method by analyzing past input history. Past input history of business challenges and requirements includes, for example, past project data and methods for storing historical data. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI select the optimal input method.

[0040] The reception unit can filter the input of business issues and requirements based on the user's current projects and areas of interest. For example, the reception unit prioritizes inputting information related to the project the user is currently working on. For example, the reception unit filters relevant business issues and requirements based on the user's areas of interest. For example, the reception unit inputs necessary information according to the progress of the user's project. This allows for the input of highly relevant information by filtering based on the current project and areas of interest. Current projects and areas of interest include, for example, data from project management tools and methods for identifying the user's areas of interest. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's current project data into a generating AI and have the generating AI perform the filtering.

[0041] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when they input business issues and requirements. For example, if the user is in a specific region, the reception desk will prioritize inputting business issues and requirements related to that region. For example, the reception desk will provide region-specific information based on the user's location. For example, if the user is on the move, the reception desk will prioritize inputting information related to their current location. This allows for the priority input of highly relevant information by considering geographical location. Geographical location information includes, for example, GPS data and location services. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI select highly relevant information.

[0042] The reception desk can analyze the user's social media activity and input relevant information when business issues and requirements are entered. For example, the reception desk can input relevant business issues and requirements based on information shared by the user on social media. For example, the reception desk can extract and input topics of interest from the user's social media activity. For example, the reception desk can input relevant business issues and requirements based on information from accounts the user follows on social media. This allows relevant information to be entered by analyzing social media activity. Social media activity includes, for example, analysis of post content and analysis of followers. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI extract relevant information.

[0043] The analysis unit can adjust the level of detail of its analysis based on the importance of business issues and requirements. For example, the analysis unit performs a detailed analysis on business issues with high importance. For example, the analysis unit performs a concise analysis on business issues with low importance. The analysis unit adjusts the depth of its analysis according to importance. By adjusting the level of detail of the analysis based on importance, it is possible to provide more appropriate analysis results. The importance of business issues and requirements includes, for example, business impact and risk assessment. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input importance data of business issues and requirements into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms during analysis depending on the category of business issues and requirements. For example, the analysis unit can apply a technical analysis algorithm to technical issues. For example, the analysis unit can apply a management analysis algorithm to management issues. For example, the analysis unit can apply a market research analysis algorithm to market research issues. By applying different analysis algorithms depending on the category, more appropriate analysis results can be provided. Categories of business issues and requirements include, for example, by industry, by function, etc. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input business issue and requirement category data into a generating AI and have the generating AI execute the application of analysis algorithms.

[0045] The analysis unit can determine the priority of analysis based on the submission timing of business issues and requirements during the analysis process. For example, the analysis unit may prioritize analyzing business issues with approaching deadlines, or postpone analyzing business issues with distant deadlines. The analysis unit may also adjust the priority of analysis according to the submission timing. This allows for analysis to be performed in a more appropriate order by determining the priority of analysis based on the submission timing. The submission timing of business issues and requirements includes, for example, submission deadlines and project progress. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input business issue and requirement submission timing data into a generating AI and have the generating AI determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of business issues and requirements during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant business issues. For example, the analysis unit may postpone the analysis of less relevant business issues. For example, the analysis unit adjusts the order of analysis according to relevance. This allows for analysis to be performed in a more appropriate order by adjusting the order of analysis based on relevance. The relevance of business issues and requirements includes, for example, correlations and dependencies. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input relevance data of business issues and requirements into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0047] The generation unit can adjust the level of detail of proposals based on the importance of business issues and requirements during proposal generation. For example, the generation unit can provide detailed proposals for high-importance business issues, and concise proposals for low-importance business issues. The generation unit can adjust the depth of proposals according to importance. This allows for the provision of more appropriate proposals by adjusting the level of detail based on importance. The importance of business issues and requirements includes, for example, business impact and risk assessment. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input importance data of business issues and requirements into a generation AI and have the generation AI perform the adjustment of the level of detail of proposals.

[0048] The generation unit can apply different proposal algorithms depending on the category of business challenges and requirements when generating proposals. For example, the generation unit can apply a technical proposal algorithm to technical challenges. For example, the generation unit can apply a management proposal algorithm to management challenges. For example, the generation unit can apply a market research proposal algorithm to market research challenges. By applying different proposal algorithms depending on the category, it is possible to provide more appropriate proposals. Categories of business challenges and requirements include, for example, by industry or by function. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input business challenge and requirement category data into a generation AI and have the generation AI execute the application of proposal algorithms.

[0049] The generation unit can determine the priority of proposals based on the submission timing of business issues and requirements when generating proposals. For example, the generation unit will prioritize proposals for business issues with approaching deadlines. For example, the generation unit will postpone proposals for business issues with distant deadlines. For example, the generation unit will adjust the priority of proposals according to the submission timing. This allows proposals to be presented in a more appropriate order by determining the priority of proposals based on the submission timing. The submission timing of business issues and requirements includes, for example, submission deadlines and project progress. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input data on the submission timing of business issues and requirements into a generation AI and have the generation AI perform the determination of proposal priorities.

[0050] The generation unit can adjust the order of proposals based on the relevance of business issues and requirements during proposal generation. For example, the generation unit may prioritize proposals for highly relevant business issues. For example, the generation unit may postpone proposals for less relevant business issues. For example, the generation unit adjusts the order of proposals according to their relevance. This allows for proposals to be presented in a more appropriate order by adjusting the order of proposals based on relevance. The relevance of business issues and requirements includes, for example, correlations and dependencies. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input relevance data of business issues and requirements into a generation AI and have the generation AI perform the adjustment of the proposal order.

[0051] The interface unit can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the interface unit can prioritize displaying interface designs that the user has frequently used in the past. For example, the interface unit can predict specific operation patterns from the user's past operation history and propose the optimal display method. For example, the interface unit can automatically apply interface settings that the user has used in the past. This allows the optimal display method to be selected by referring to the past operation history. Past operation history includes, for example, click history and operation logs. Some or all of the above processing in the interface unit may be performed using, for example, AI, or not using AI. For example, the interface unit can input the user's past operation history data into a generating AI and have the generating AI select the optimal display method.

[0052] The interface unit can select the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, the interface unit provides a display method that matches the screen size. For example, if the user is using a tablet, the interface unit provides a display method optimized for a large screen. For example, if the user is using a desktop, the interface unit displays detailed information. In this way, the optimal display method can be selected by taking device information into account. Device information includes, for example, the type of device, screen size, and OS. Some or all of the above processing in the interface unit may be performed using, for example, AI, or without AI. For example, the interface unit can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The reception desk can analyze user input in real time and provide appropriate feedback based on that input. For example, it can automatically generate and present relevant questions in response to the business challenges entered by the user. The reception desk can also provide relevant reference materials and past case studies based on the user's input. Furthermore, when the user modifies their input, the reception desk can re-analyze the changes and provide appropriate feedback. This allows users to correct their input more accurately and efficiently.

[0055] The analysis unit can evaluate the reliability of user input when analyzing it. For example, the analysis unit can verify the source of the information entered by the user and prioritize the analysis of highly reliable information. Furthermore, the analysis unit can check the consistency of the information entered by the user and display a warning if there are inconsistencies. In addition, the analysis unit can evaluate the accuracy of the information entered by the user and request additional information as needed. This allows the analysis unit to perform analysis based on highly reliable information.

[0056] The generation unit can generate multiple proposal options based on the user's business challenges and requirements. For example, it can propose different technical solutions and compare their respective advantages and disadvantages. It can also present different cost scenarios, allowing the user to make the best choice based on their budget. Furthermore, it can propose different implementation schedules, enabling the user to select the most suitable schedule based on the project's progress. This allows the user to choose the best proposal from multiple options.

[0057] The interface can provide interactive visual tools when users review proposals. For example, the interface can provide a dashboard that visually displays the proposals, allowing users to intuitively understand them. The interface can also simulate the proposals, allowing users to visually confirm their effectiveness. Furthermore, the interface can provide interactive forms for users to provide feedback on the proposals. This allows users to gain a deeper understanding of the proposals and provide appropriate feedback.

[0058] The reception desk can consider the context of user input when analyzing it. For example, the reception desk can collect background information on the business issue entered by the user and understand the context of the input. Furthermore, the reception desk can evaluate the relevance of the information entered by the user and prioritize the analysis of relevant information. In addition, the reception desk can evaluate the importance of the information entered by the user and prioritize the analysis of important information. This allows the reception desk to perform more appropriate analysis by considering the context of the input.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The reception desk receives input of business issues and requirements from users. The reception desk can accept business issues and requirements entered by users in text format, and can also accept voice input. In the case of voice input, the reception desk converts the voice to text for reception. Furthermore, the reception desk can save the information entered by users to a database. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit uses natural language processing technology to analyze the business challenges and requirements entered by the user. The analysis unit uses generative AI to analyze the information entered by the user and generate data to propose the optimal IT solutions and system designs. Step 3: The generation unit proposes the optimal IT solution and system design based on the information analyzed by the analysis unit. Using generation AI, the generation unit proposes the optimal IT solution for the user's business challenges and requirements, and automatically creates professional proposals and roadmaps. Step 4: The interface unit shares the proposals and roadmaps generated by the generation unit with stakeholders. The interface unit can send the generated proposals and roadmaps to stakeholders via email, or share them with stakeholders through a web application. Furthermore, the interface unit can print the generated proposals and roadmaps and distribute them to stakeholders.

[0061] (Example of form 2) The IT solution proposal system according to an embodiment of the present invention is a system that automatically proposes optimal IT solutions and system designs when a client inputs their business challenges and requirements. This system analyzes the input information using natural language processing and generative AI, and proposes optimal IT solutions and system designs based on the user's input of business challenges and requirements. These proposals are automatically generated as professional proposal documents and roadmaps, which can be shared with stakeholders. Furthermore, the system interacts with the user through an interactive interface, gathering additional information and fine-tuning the proposals. For example, if a user inputs that they are a "non-IT company considering new IT implementation" and "want to promote DX but don't know where to start," the system analyzes this challenge and proposes the optimal IT solution. The proposal includes specific system designs, implementation plans, and cost estimates. This system uses natural language processing to understand user input and generate accurate proposals. It also utilizes knowledge graphs and machine learning to learn the latest IT solutions and technology trends, reflecting them in the proposals. Additionally, it uses generative AI to automatically create professional proposal documents and roadmaps, which can be shared with stakeholders. This will compensate for the shortage of human resources among IT consultants and shorten the lead time to proposals. It will also reduce and streamline consulting costs. For example, traditional consulting services often take several weeks from initial consultation to proposal creation, but with this system, proposals can be received within a few days. Furthermore, this system interacts with users through an interactive interface, gathering additional information and fine-tuning the proposal. This enables flexible proposals tailored to user needs. For example, if a user inputs additional requirements for the proposal, the system analyzes those requirements and updates the proposal. This system aims to democratize IT consulting and create an environment where all companies, regardless of size or industry, can receive high-quality IT consulting.This is expected to contribute to improving productivity in Japan and creating new markets and value. As a result, the IT solution proposal system can efficiently analyze clients' business challenges and requirements and propose optimal IT solutions and system designs.

[0062] The IT solution proposal system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and an interface unit. The reception unit receives input of business issues and requirements from the user. For example, the reception unit can receive business issues and requirements entered by the user in text format. The reception unit can also receive voice input. For example, if the user enters business issues or requirements by voice, the reception unit converts the voice into text and receives it. Furthermore, the reception unit can store the information entered by the user in a database. The analysis unit analyzes the information received by the reception unit. For example, the analysis unit uses natural language processing technology to analyze the business issues and requirements entered by the user. The analysis unit uses generation AI to analyze the information entered by the user and generates data for proposing the optimal IT solution and system design. The generation unit proposes the optimal IT solution and system design based on the information analyzed by the analysis unit. For example, the generation unit uses generation AI to propose the optimal IT solution for the user's business issues and requirements. The generation unit can use generation AI to automatically create professional proposals and roadmaps. The generation unit automatically generates proposals based on the user's business challenges and requirements, for example, using generation AI. The generation unit can also automatically generate roadmaps based on the user's business challenges and requirements, using generation AI. The interface unit shares the proposals and roadmaps generated by the generation unit with relevant parties. For example, the interface unit can send the generated proposals and roadmaps to relevant parties via email. The interface unit can also share the generated proposals and roadmaps with relevant parties through a web application. Furthermore, the interface unit can print the generated proposals and roadmaps and distribute them to relevant parties. As a result, the IT solution proposal system according to this embodiment can efficiently analyze the user's business challenges and requirements and propose optimal IT solutions and system designs.

[0063] The reception desk receives input of business issues and requirements from users. For example, the reception desk can receive business issues and requirements entered by users in text format. Specifically, when a user enters business issues or requirements into a web form, the reception desk receives the content in real time and saves it to the database. The reception desk can also accept voice input. For example, if a user enters business issues or requirements by voice, the reception desk converts the voice into text and receives it. Using speech recognition technology, it converts the user's voice into text with high accuracy and is equipped with a feedback function to minimize misrecognition. Furthermore, the reception desk can save the information entered by the user to the database. The saved data is centrally managed so that subsequent analysis and generation units can access it, and it can be updated or modified as needed. This allows the reception desk to handle various input formats from users and collect information accurately and efficiently. In addition, the reception desk is equipped with a function to automatically send confirmation messages to the user's input content, prompting them to check and correct the input content. This allows the user to reconfirm their input content and correct it as needed, improving the reliability and accuracy of the entire system.

[0064] The analysis unit analyzes the information received by the reception unit. For example, the analysis unit uses natural language processing technology to analyze the business challenges and requirements entered by the user. Specifically, it uses natural language processing technology to tokenize the user's input and perform grammatical and semantic analysis. This allows it to extract the intent and important keywords of the user's business challenges and requirements. The analysis unit uses generative AI to analyze the user's input and generate data to propose optimal IT solutions and system designs. The generative AI learns from past data and case studies to build a model for proposing the best solutions to the user's business challenges. For example, if the user's business challenge is "improvement of the customer management system," the analysis unit will refer to past case studies and solutions related to customer management and generate data to propose the best improvement measures. Furthermore, the analysis unit can also perform risk assessments and cost analyses for the user's business challenges and requirements. This allows the user to understand the feasibility and cost-effectiveness of the proposed solutions in advance. Based on the generated data, the analysis unit provides a foundation for proposing the best IT solutions to the user's business challenges.

[0065] The generation unit proposes optimal IT solutions and system designs based on the information analyzed by the analysis unit. For example, the generation unit uses generation AI to propose IT solutions best suited to the user's business challenges and requirements. The generation AI automatically generates specific solutions to the user's business challenges based on the data provided by the analysis unit. For example, if the user's business challenge is "improvement of the customer management system," the generation unit proposes the functions and technologies necessary for improving the customer management system and designs a system based on them. The generation unit can automatically create professional proposals and roadmaps using generation AI. For example, the generation unit can automatically generate proposals based on the user's business challenges and requirements using generation AI. The proposal includes an overview of the proposed IT solution, the benefits of implementation, the implementation schedule, and cost estimates. The generation unit can also automatically generate roadmaps based on the user's business challenges and requirements using generation AI. The roadmap shows the implementation steps and schedule of the proposed IT solution, as well as the specific work content and goals at each step. This allows the generation unit to propose concrete and feasible IT solutions to the user and provide clear guidance for implementation. Furthermore, the generation unit can flexibly modify the content of proposals and roadmaps based on user feedback. This allows for quick responses to user requests and changes, enabling the provision of optimal proposals.

[0066] The interface unit shares proposals and roadmaps generated by the generation unit with stakeholders. For example, the interface unit can send generated proposals and roadmaps to stakeholders via email. Specifically, it converts proposals and roadmaps created by the generation unit into file formats such as PDF or Word and sends them to the stakeholders' email addresses. The interface unit can also share generated proposals and roadmaps with stakeholders through a web application. The web application provides functions that allow stakeholders to view proposals and roadmaps and provide comments and feedback. Furthermore, the interface unit can print and distribute generated proposals and roadmaps to stakeholders. This allows stakeholders to refer to proposals and roadmaps as physical documents. The interface unit facilitates communication with stakeholders and supports a deeper understanding of the proposal content. For example, it collects feedback from stakeholders and reflects that feedback in the generation unit to improve the accuracy and effectiveness of the proposal content. The interface unit also supports the effective use of generated proposals and roadmaps during meetings and presentations with stakeholders. This allows the interface unit to share the proposals and roadmaps generated by the generation unit with stakeholders, facilitating understanding and consensus building on the proposed content.

[0067] The generation unit can automatically create professional proposals and roadmaps using generational AI. For example, the generation unit can automatically generate proposals based on the user's business challenges and requirements using generational AI. The generation unit can also automatically generate roadmaps based on the user's business challenges and requirements using generational AI. For example, when creating proposals based on the user's business challenges and requirements using generational AI, the generation unit can include content compliant with industry standards and detailed analysis results. When creating roadmaps based on the user's business challenges and requirements using generational AI, the generation unit can clearly show project progress and key milestones. This allows for the automatic creation of professional proposals and roadmaps using generational AI. The generation AI can, for example, use text generation AI (e.g., large-scale language models such as Transformer models). The generation AI takes the user's business challenges and requirements as input and outputs professional proposals and roadmaps. For example, the generation AI can receive a prompt such as "Propose the optimal IT solution for the user's business challenges" and generate a proposal or roadmap.

[0068] The interface unit can interact with the user through an interactive interface, gather additional information, and fine-tune the proposed content. For example, the interface unit can use a chatbot to interact with the user. Using a chatbot, the interface unit can gather additional information from the user and fine-tune the proposed content. The interface unit can also use a voice assistant to interact with the user. Using a voice assistant, the interface unit can gather additional information from the user and fine-tune the proposed content. For example, if the user inputs additional requirements for the proposed content, the interface unit can analyze those requirements and update the proposed content. For example, if the user inputs additional requirements via a chatbot, the interface unit analyzes those requirements and updates the proposed content. For example, if the user inputs additional requirements via a voice assistant, the interface unit analyzes those requirements and updates the proposed content. This allows for gathering additional information and fine-tuning of proposed content through interaction with the user via an interactive interface. The interactive interface can utilize, for example, a chatbot or a voice assistant. Interactive interfaces can adjust their suggestions based on user feedback. For example, if a user provides feedback on a suggestion via a chatbot, the interface analyzes that feedback and adjusts the suggestion accordingly. Interactive interfaces can provide flexible suggestions tailored to the user's needs.

[0069] The analysis unit can learn the latest IT solutions and technology trends using knowledge graphs and machine learning, and reflect them in its proposals. For example, the analysis unit learns the latest IT solutions and technology trends using a knowledge graph. The analysis unit uses a knowledge graph to collect information from relevant data sources, define nodes and edges, and construct the knowledge graph. The analysis unit learns the latest IT solutions and technology trends using machine learning algorithms. The analysis unit uses machine learning algorithms to analyze the collected data and reflect it in its proposals. The analysis unit learns the latest IT solutions and technology trends using deep learning. The analysis unit learns from large amounts of data using deep learning and reflects it in its proposals. The analysis unit learns the latest IT solutions and technology trends using reinforcement learning. The analysis unit uses reinforcement learning to optimize its proposals. This allows the analysis unit to learn the latest IT solutions and technology trends and reflect them in its proposals by utilizing knowledge graphs and machine learning. The knowledge graph includes, for example, data sources, node and edge definitions, etc. Machine learning can utilize algorithms such as deep learning and reinforcement learning. The latest IT solutions and technological trends include, for example, cloud computing, AI technology, and blockchain.

[0070] The generation unit can automate the following processes: current situation analysis / market research, solution research, planning / proposal, project plan formulation, requirements definition, design / development / testing, implementation, operation, and support. For example, the generation unit can automate current situation analysis / market research. The generation unit uses generational AI to conduct current situation analysis / market research and collect data based on the user's business challenges and requirements. For example, the generation unit can automate solution research. The generation unit uses generational AI to research the optimal solution for the user's business challenges and requirements. For example, the generation unit can automate planning / proposal. The generation unit uses generational AI to create plans and proposals based on the user's business challenges and requirements. For example, the generation unit can automate project plan formulation. The generation unit uses generational AI to formulate project plans based on the user's business challenges and requirements. For example, the generation unit can automate requirements definition. The generation unit uses generational AI to define requirements based on the user's business challenges and requirements. For example, the generation unit can automate design / development / testing. The generation unit uses generational AI to design, develop, and test based on the user's business challenges and requirements. For example, the generation unit automates implementation. The generation unit uses generational AI to implement based on the user's business challenges and requirements. For example, the generation unit automates operation. The generation unit uses generational AI to perform operations based on the user's business challenges and requirements. For example, the generation unit automates support. The generation unit uses generational AI to provide support based on the user's business challenges and requirements. By automating each process, it becomes possible to efficiently propose IT solutions and system designs. Each process includes, for example, current situation analysis and market research, solution research, planning and proposal, project planning, requirements definition, design, development and testing, implementation, operation, and support.

[0071] The interface unit can analyze additional requirements entered by the user and update the proposed content. For example, if the user enters additional requirements via a chatbot, the interface unit can analyze those requirements and update the proposed content. For example, if the user enters additional requirements via a voice assistant, the interface unit can analyze those requirements and update the proposed content. For example, if the user enters additional requirements via a web form, the interface unit can analyze those requirements and update the proposed content. Additional requirements include, for example, functional requirements and non-functional requirements. Some or all of the above processing in the interface unit may be performed using, for example, AI, or not using AI. For example, the interface unit can input the additional requirements entered by the user into a generating AI and have the generating AI perform the update of the proposed content.

[0072] The reception desk can estimate the user's emotions and adjust the timing of inputting business issues and requirements based on the estimated emotions. For example, if the user is stressed, the reception desk can delay the input timing to provide time for relaxation. For example, if the user is in a hurry, the reception desk can speed up the input timing to quickly collect information. For example, if the user is focused, the reception desk can adjust the input timing to allow information to be entered at the optimal time. In this way, by adjusting the input timing based on the user's emotions, information can be entered at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, 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 processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0073] The reception desk can analyze the user's past input history of business challenges and requirements and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk can predict and suggest input methods to be used during specific time periods based on the user's past input history. For example, the reception desk can provide input assistance functions by referring to content that the user has entered in the past. This allows for the selection of the optimal input method by analyzing past input history. Past input history of business challenges and requirements includes, for example, past project data and methods for storing historical data. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI select the optimal input method.

[0074] The reception unit can filter the input of business issues and requirements based on the user's current projects and areas of interest. For example, the reception unit prioritizes inputting information related to the project the user is currently working on. For example, the reception unit filters relevant business issues and requirements based on the user's areas of interest. For example, the reception unit inputs necessary information according to the progress of the user's project. This allows for the input of highly relevant information by filtering based on the current project and areas of interest. Current projects and areas of interest include, for example, data from project management tools and methods for identifying the user's areas of interest. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's current project data into a generating AI and have the generating AI perform the filtering.

[0075] The reception desk can estimate the user's emotions and, based on the estimated emotions, determine the priority of business issues and requirements to be entered. For example, if the user is stressed, the reception desk will postpone less important issues. For example, if the user is relaxed, the reception desk will prioritize the entry of high-priority issues. For example, if the user is focused, the reception desk will prioritize the entry of complex issues. This allows information to be entered in a more appropriate order by prioritizing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, 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 processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0076] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when they input business issues and requirements. For example, if the user is in a specific region, the reception desk will prioritize inputting business issues and requirements related to that region. For example, the reception desk will provide region-specific information based on the user's location. For example, if the user is on the move, the reception desk will prioritize inputting information related to their current location. This allows for the priority input of highly relevant information by considering geographical location. Geographical location information includes, for example, GPS data and location services. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI select highly relevant information.

[0077] The reception desk can analyze the user's social media activity and input relevant information when business issues and requirements are entered. For example, the reception desk can input relevant business issues and requirements based on information shared by the user on social media. For example, the reception desk can extract and input topics of interest from the user's social media activity. For example, the reception desk can input relevant business issues and requirements based on information from accounts the user follows on social media. This allows relevant information to be entered by analyzing social media activity. Social media activity includes, for example, analysis of post content and analysis of followers. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI extract relevant information.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is in a hurry, the analysis unit provides concise analysis results that get straight to the point. For example, if the user is excited, the analysis unit provides visually stimulating analysis results. By adjusting the presentation of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, for example, 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 processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0079] The analysis unit can adjust the level of detail of its analysis based on the importance of business issues and requirements. For example, the analysis unit performs a detailed analysis on business issues with high importance. For example, the analysis unit performs a concise analysis on business issues with low importance. The analysis unit adjusts the depth of its analysis according to importance. By adjusting the level of detail of the analysis based on importance, it is possible to provide more appropriate analysis results. The importance of business issues and requirements includes, for example, business impact and risk assessment. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input importance data of business issues and requirements into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms during analysis depending on the category of business issues and requirements. For example, the analysis unit can apply a technical analysis algorithm to technical issues. For example, the analysis unit can apply a management analysis algorithm to management issues. For example, the analysis unit can apply a market research analysis algorithm to market research issues. By applying different analysis algorithms depending on the category, more appropriate analysis results can be provided. Categories of business issues and requirements include, for example, by industry, by function, etc. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input business issue and requirement category data into a generating AI and have the generating AI execute the application of analysis algorithms.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. For example, if the user is excited, the analysis unit provides a visually stimulating analysis result. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, for example, 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 processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0082] The analysis unit can determine the priority of analysis based on the submission timing of business issues and requirements during the analysis process. For example, the analysis unit may prioritize analyzing business issues with approaching deadlines, or postpone analyzing business issues with distant deadlines. The analysis unit may also adjust the priority of analysis according to the submission timing. This allows for analysis to be performed in a more appropriate order by determining the priority of analysis based on the submission timing. The submission timing of business issues and requirements includes, for example, submission deadlines and project progress. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input business issue and requirement submission timing data into a generating AI and have the generating AI determine the priority of analysis.

[0083] The analysis unit can adjust the order of analysis based on the relevance of business issues and requirements during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant business issues. For example, the analysis unit may postpone the analysis of less relevant business issues. For example, the analysis unit adjusts the order of analysis according to relevance. This allows for analysis to be performed in a more appropriate order by adjusting the order of analysis based on relevance. The relevance of business issues and requirements includes, for example, correlations and dependencies. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input relevance data of business issues and requirements into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0084] The generation unit can estimate the user's emotions and adjust the way suggestions are presented based on the estimated emotions. For example, if the user is relaxed, the generation unit provides detailed suggestions. If the user is in a hurry, the generation unit provides concise suggestions that get straight to the point. If the user is excited, the generation unit provides visually stimulating suggestions. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, for example, 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 processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input user emotion data into the generative AI and have the generative AI adjust the way suggestions are presented.

[0085] The generation unit can adjust the level of detail of proposals based on the importance of business issues and requirements during proposal generation. For example, the generation unit can provide detailed proposals for high-importance business issues, and concise proposals for low-importance business issues. The generation unit can adjust the depth of proposals according to importance. This allows for the provision of more appropriate proposals by adjusting the level of detail based on importance. The importance of business issues and requirements includes, for example, business impact and risk assessment. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input importance data of business issues and requirements into a generation AI and have the generation AI perform the adjustment of the level of detail of proposals.

[0086] The generation unit can apply different proposal algorithms depending on the category of business challenges and requirements when generating proposals. For example, the generation unit can apply a technical proposal algorithm to technical challenges. For example, the generation unit can apply a management proposal algorithm to management challenges. For example, the generation unit can apply a market research proposal algorithm to market research challenges. By applying different proposal algorithms depending on the category, it is possible to provide more appropriate proposals. Categories of business challenges and requirements include, for example, by industry or by function. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input business challenge and requirement category data into a generation AI and have the generation AI execute the application of proposal algorithms.

[0087] The generation unit can estimate the user's emotions and adjust the length of suggestions based on the estimated emotions. For example, if the user is in a hurry, the generation unit will provide short, concise suggestions. For example, if the user is relaxed, the generation unit will provide detailed suggestions. For example, if the user is excited, the generation unit will provide visually stimulating suggestions. By adjusting the length of suggestions based on the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, for example, 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 processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of suggestions.

[0088] The generation unit can determine the priority of proposals based on the submission timing of business issues and requirements when generating proposals. For example, the generation unit will prioritize proposals for business issues with approaching deadlines. For example, the generation unit will postpone proposals for business issues with distant deadlines. For example, the generation unit will adjust the priority of proposals according to the submission timing. This allows proposals to be presented in a more appropriate order by determining the priority of proposals based on the submission timing. The submission timing of business issues and requirements includes, for example, submission deadlines and project progress. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input data on the submission timing of business issues and requirements into a generation AI and have the generation AI perform the determination of proposal priorities.

[0089] The generation unit can adjust the order of proposals based on the relevance of business issues and requirements during proposal generation. For example, the generation unit may prioritize proposals for highly relevant business issues. For example, the generation unit may postpone proposals for less relevant business issues. For example, the generation unit adjusts the order of proposals according to their relevance. This allows for proposals to be presented in a more appropriate order by adjusting the order of proposals based on relevance. The relevance of business issues and requirements includes, for example, correlations and dependencies. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input relevance data of business issues and requirements into a generation AI and have the generation AI perform the adjustment of the proposal order.

[0090] The interface unit can estimate the user's emotions and adjust the interface display method based on the estimated user emotions. For example, if the user is tense, the interface unit provides an interface with calm colors. For example, if the user is enjoying themselves, the interface unit provides an interface with bright colors. For example, if the user is tired, the interface unit provides a simple and highly visible interface. This allows for a more appropriate display by adjusting the interface display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, 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 processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input user emotion data into the generative AI and have the generative AI adjust the interface display method.

[0091] The interface unit can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the interface unit can prioritize displaying interface designs that the user has frequently used in the past. For example, the interface unit can predict specific operation patterns from the user's past operation history and propose the optimal display method. For example, the interface unit can automatically apply interface settings that the user has used in the past. This allows the optimal display method to be selected by referring to the past operation history. Past operation history includes, for example, click history and operation logs. Some or all of the above processing in the interface unit may be performed using, for example, AI, or not using AI. For example, the interface unit can input the user's past operation history data into a generating AI and have the generating AI select the optimal display method.

[0092] The interface unit can estimate the user's emotions and adjust the interface's operating procedures based on the estimated user emotions. For example, if the user is tense, the interface unit may simplify the operating procedures to reduce stress. For example, if the user is relaxed, the interface unit may provide detailed operating procedures. For example, if the user is in a hurry, the interface unit may provide procedures that allow for quick operation. In this way, by adjusting the operating procedures based on the user's emotions, a more appropriate operation can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, 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 processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the operating procedures.

[0093] The interface unit can select the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, the interface unit provides a display method that matches the screen size. For example, if the user is using a tablet, the interface unit provides a display method optimized for a large screen. For example, if the user is using a desktop, the interface unit displays detailed information. In this way, the optimal display method can be selected by taking device information into account. Device information includes, for example, the type of device, screen size, and OS. Some or all of the above processing in the interface unit may be performed using, for example, AI, or without AI. For example, the interface unit can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The reception desk can analyze user input in real time and provide appropriate feedback based on that input. For example, it can automatically generate and present relevant questions in response to the business challenges entered by the user. The reception desk can also provide relevant reference materials and past case studies based on the user's input. Furthermore, when the user modifies their input, the reception desk can re-analyze the changes and provide appropriate feedback. This allows users to correct their input more accurately and efficiently.

[0096] The analysis unit can evaluate the reliability of user input when analyzing it. For example, the analysis unit can verify the source of the information entered by the user and prioritize the analysis of highly reliable information. Furthermore, the analysis unit can check the consistency of the information entered by the user and display a warning if there are inconsistencies. In addition, the analysis unit can evaluate the accuracy of the information entered by the user and request additional information as needed. This allows the analysis unit to perform analysis based on highly reliable information.

[0097] The generation unit can generate multiple proposal options based on the user's business challenges and requirements. For example, it can propose different technical solutions and compare their respective advantages and disadvantages. It can also present different cost scenarios, allowing the user to make the best choice based on their budget. Furthermore, it can propose different implementation schedules, enabling the user to select the most suitable schedule based on the project's progress. This allows the user to choose the best proposal from multiple options.

[0098] The interface can provide interactive visual tools when users review proposals. For example, the interface can provide a dashboard that visually displays the proposals, allowing users to intuitively understand them. The interface can also simulate the proposals, allowing users to visually confirm their effectiveness. Furthermore, the interface can provide interactive forms for users to provide feedback on the proposals. This allows users to gain a deeper understanding of the proposals and provide appropriate feedback.

[0099] The reception desk can consider the context of user input when analyzing it. For example, the reception desk can collect background information on the business issue entered by the user and understand the context of the input. Furthermore, the reception desk can evaluate the relevance of the information entered by the user and prioritize the analysis of relevant information. In addition, the reception desk can evaluate the importance of the information entered by the user and prioritize the analysis of important information. This allows the reception desk to perform more appropriate analysis by considering the context of the input.

[0100] The reception desk can estimate the user's emotions and adjust the input verification process based on those emotions. For example, if the user is feeling anxious, the reception desk can carefully verify the input to reassure the user. If the user is excited, the reception desk can quickly verify the input to maintain the user's excitement. Furthermore, if the user is tired, the reception desk can simplify the input verification process to reduce the user's burden. In this way, by adjusting the input verification process based on the user's emotions, more appropriate verification can be performed.

[0101] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results to allow the user to understand them deeply. If the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point, allowing the user to understand them quickly. Furthermore, if the user is excited, the analysis unit can provide visually stimulating analysis results to maintain the user's excitement. In this way, by adjusting the presentation method of the analysis results based on the user's emotions, more appropriate analysis results can be provided.

[0102] The generation unit can estimate the user's emotions and adjust the level of detail in the suggestions based on those emotions. For example, if the user is relaxed, the generation unit can provide detailed suggestions to help the user understand them more deeply. If the user is in a hurry, the generation unit can provide concise suggestions that get straight to the point, allowing the user to understand them quickly. Furthermore, if the user is excited, the generation unit can provide visually stimulating suggestions to maintain the user's excitement. By adjusting the level of detail in suggestions based on the user's emotions, more appropriate suggestions can be provided.

[0103] The interface unit can estimate the user's emotions and adjust the interface operation method based on the estimated emotions. For example, if the user is tense, the interface unit can simplify the operation method to reduce stress. If the user is relaxed, the interface unit can provide detailed instructions to allow the user to enjoy the operation. Furthermore, if the user is in a hurry, the interface unit can provide a quick operation method to save the user time. In this way, by adjusting the interface operation method based on the user's emotions, a more appropriate operation can be provided.

[0104] The interface can estimate the user's emotions and adjust the interface's display content based on those emotions. For example, if the user is tense, the interface can provide calming colored content to help the user relax. If the user is enjoying themselves, the interface can provide bright colored content to maintain that enjoyment. Furthermore, if the user is tired, the interface can provide simple and highly visible content to reduce the user's burden. In this way, by adjusting the interface's display content based on the user's emotions, a more appropriate display can be provided.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The reception desk receives input of business issues and requirements from users. The reception desk can accept business issues and requirements entered by users in text format, and can also accept voice input. In the case of voice input, the reception desk converts the voice to text for reception. Furthermore, the reception desk can save the information entered by users to a database. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit uses natural language processing technology to analyze the business challenges and requirements entered by the user. The analysis unit uses generative AI to analyze the information entered by the user and generate data to propose the optimal IT solutions and system designs. Step 3: The generation unit proposes the optimal IT solution and system design based on the information analyzed by the analysis unit. Using generation AI, the generation unit proposes the optimal IT solution for the user's business challenges and requirements, and automatically creates professional proposals and roadmaps. Step 4: The interface unit shares the proposals and roadmaps generated by the generation unit with stakeholders. The interface unit can send the generated proposals and roadmaps to stakeholders via email, or share them with stakeholders through a web application. Furthermore, the interface unit can print the generated proposals and roadmaps and distribute them to stakeholders.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0109] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0110] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and interface unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives input of business issues and requirements from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the business issues and requirements entered by the user using natural language processing technology. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes optimal IT solutions and system designs using generated AI. The interface unit is implemented by the control unit 46A of the smart device 14 and shares the generated proposals and roadmaps with stakeholders. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0114] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0115] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0117] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0118] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0119] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0120] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0121] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0125] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and interface unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives input of business issues and requirements from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the business issues and requirements entered by the user using natural language processing technology. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes optimal IT solutions and system designs using generated AI. The interface unit is implemented by the control unit 46A of the smart glasses 214 and shares the generated proposals and roadmaps with stakeholders. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and interface unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives input of business issues and requirements from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the business issues and requirements entered by the user using natural language processing technology. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes optimal IT solutions and system designs using generated AI. The interface unit is implemented by the control unit 46A of the headset terminal 314 and shares the generated proposals and roadmaps with relevant parties. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0151] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and interface unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives input of business issues and requirements from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the business issues and requirements entered by the user using natural language processing technology. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes optimal IT solutions and system designs using generated AI. The interface unit is implemented by the control unit 46A of the robot 414 and shares the generated proposals and roadmaps with stakeholders. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0160] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0161] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0162] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0163] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0164] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0165] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0167] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0168] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0169] 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.

[0170] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0171] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0172] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0173] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0174] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0175] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0176] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0177] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0178] (Note 1) A reception desk that receives input of business challenges and requirements from users, An analysis unit that analyzes the information received by the reception unit, A generation unit proposes optimal IT solutions and system designs based on the information analyzed by the aforementioned analysis unit, The system includes an interface unit for sharing proposals and roadmaps generated by the generation unit with relevant parties. A system characterized by the following features. (Note 2) The generating unit is Use generative AI to automatically create professional proposals and roadmaps. The system described in Appendix 1, characterized by the features described herein. (Note 3) The interface unit is Interact with users through an interactive interface to gather additional information and fine-tune proposals. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, We utilize knowledge graphs and machine learning to learn about the latest IT solutions and technology trends, and reflect them in our proposals. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Automate the processes of current situation analysis / market research, solution research, planning / proposal, project planning, requirements definition, design / development / testing, implementation, operation, and support. The system described in Appendix 1, characterized by the features described herein. (Note 6) The interface unit is If a user enters additional requirements for a proposal, those requirements will be analyzed and the proposal updated. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of inputting business issues and requirements based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past business challenges and requirements input history to select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users input business challenges and requirements, filtering is performed based on their current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates user emotions and prioritizes the business issues and requirements to be entered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users input business challenges and requirements, the system prioritizes inputting highly relevant information by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering business challenges and requirements, the system analyzes users' social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During the analysis, adjust the level of detail based on the importance of the business issues and requirements. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of business challenges and requirements. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, prioritize the analysis based on the timing of business challenges and requirements submissions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of business issues and requirements. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating proposals, adjust the level of detail based on the importance of the business challenges and requirements. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating proposals, different proposal algorithms are applied depending on the category of business challenges and requirements. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating proposals, prioritize them based on the timing of business challenges and requirements submissions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating proposals, adjust the order of proposals based on the relevance of business challenges and requirements. The system described in Appendix 1, characterized by the features described herein. (Note 25) The interface unit is It estimates the user's emotions and adjusts the interface display based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The interface unit is When displaying the interface, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The interface unit is It estimates the user's emotions and adjusts the interface operation procedures based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The interface unit is When displaying the interface, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives input of business challenges and requirements from users, An analysis unit that analyzes the information received by the reception unit, A generation unit proposes optimal IT solutions and system designs based on the information analyzed by the aforementioned analysis unit, The system includes an interface unit for sharing proposals and roadmaps generated by the generation unit with relevant parties. A system characterized by the following features.

2. The generating unit is Use AI to automatically create professional proposals and roadmaps. The system according to feature 1.

3. The interface unit is Interact with users through an interactive interface to gather additional information and fine-tune proposals. The system according to feature 1.

4. The aforementioned analysis unit, We utilize knowledge graphs and machine learning to learn about the latest IT solutions and technology trends, and reflect them in our proposals. The system according to feature 1.

5. The generating unit is Automate the processes of current situation analysis and market research, solution research, planning and proposal, project plan formulation, requirements definition, design, development and testing, implementation, operation, and support. The system according to feature 1.

6. The interface unit is If a user enters additional requirements for a proposal, those requirements will be analyzed and the proposal updated. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of inputting business issues and requirements based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past business challenges and requirements input history to select the optimal input method. The system according to feature 1.

Images