system

Abstract

The system according to this embodiment aims to efficiently implement the most suitable cloud tools. [Solution] The system according to the embodiment comprises a learning unit, an interviewing unit, a proposal unit, and a support unit. The learning unit learns internal company information. The interviewing unit conducts interviews with the person in charge based on the information learned by the learning unit. The proposal unit proposes a cloud tool based on the requirements obtained by the interviewing unit. The support unit supports the implementation process of the cloud tool proposed by the proposal unit.

Description

Technical Field

[0004] ,

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[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, there was a problem that the introduction process of new tools was complicated and the workload of the person in charge was large.

[0005] The system according to the embodiment aims to efficiently introduce optimal cloud tools.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a learning unit, an interviewing unit, a proposal unit, and a support unit. The learning unit learns internal company information. The interviewing unit conducts interviews with relevant personnel based on the information learned by the learning unit. The proposal unit proposes cloud tools based on the requirements obtained by the interviewing unit. The support unit supports the implementation process of the cloud tools proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently implement the most suitable cloud tools. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The cloud tool implementation support system according to an embodiment of the present invention is a system that supports the cloud tool implementation process by having AI learn internal company information and conduct interviews with the person in charge. In this cloud tool implementation support system, the AI ​​learns the current internal company information (business processes, approval processes for implementation, security / legal rules, current system configuration) and conducts interviews with the person in charge. Based on the necessary requirements, it proposes multiple methods for solving problems using cloud tools after comparison. The AI ​​then proposes and supports the entire process up to implementation. This enables the implementation of the optimal cloud tool and reduces the workload of the person in charge during the implementation consideration process. For example, in the cloud tool implementation support system, the AI ​​first learns the current internal company information. At this time, it analyzes information such as business processes, approval processes for implementation, security / legal rules, and current system configuration in detail. Next, the cloud tool implementation support system has the AI ​​conduct interviews with the person in charge to understand the necessary requirements. For example, it interviews the person in charge about what kind of problems they are facing and what kind of functions they need. Based on this, the cloud tool implementation support system has the AI ​​propose multiple methods for solving problems using cloud tools. The proposed cloud tools are compared based on factors such as feasibility of achieving the desired outcome, legal / security considerations, and cost. For example, the system presents a comparison of tool A and tool B, indicating which is more suitable. Finally, the cloud tool implementation support system uses AI to propose and support the implementation process. For instance, it suggests the necessary procedures and approval processes, supporting the person in charge to ensure a smooth implementation. This allows for the adoption of the optimal cloud tool and reduces the workload on the person in charge during the implementation evaluation process. In this way, the cloud tool implementation support system reduces the workload on the person in charge and enables efficient cloud tool implementation.

[0029] The cloud tool implementation support system according to this embodiment comprises a learning unit, an interviewing unit, a proposal unit, and a support unit. The learning unit learns internal company information. For example, the learning unit learns internal company information such as business processes, approval processes up to implementation, security / legal rules, and the current system configuration. The learning unit can analyze and learn internal company information in detail using AI. For example, the learning unit analyzes and learns internal company information using machine learning algorithms. The interviewing unit conducts interviews with the person in charge based on the information learned by the learning unit. For example, the interviewing unit asks questions to the person in charge to understand the necessary requirements. The interviewing unit can analyze the person in charge's answers using AI and extract the necessary requirements. For example, the interviewing unit analyzes the person in charge's answers using natural language processing technology and extracts the requirements. The proposal unit proposes cloud tools based on the requirements obtained by the interviewing unit. The proposal unit compares cloud tools from perspectives such as feasibility of what they want to do, legal / security aspects, and cost aspects. The proposal department can use AI to compare cloud tools and propose the most suitable one. For example, the proposal department can compare the functions and costs of cloud tools and propose the optimal tool. The support department supports the implementation process of the cloud tools proposed by the proposal department. For example, the support department proposes the necessary procedures and approval processes for implementation and supports the person in charge so that the implementation can proceed smoothly. The support department can use AI to support the implementation process. For example, the support department monitors the progress of the implementation procedures and provides necessary support. As a result, the cloud tool implementation support system according to this embodiment can reduce the workload of the person in charge and realize efficient cloud tool implementation.

[0030] The Learning Department learns internal company information. For example, it learns internal information such as business processes, approval processes for implementation, security / legal rules, and the current system configuration. Specifically, the Learning Department collects documents and databases provided by various departments within the company, and integrates and analyzes this information. By using AI, it can efficiently process vast amounts of data and extract important information. For example, it uses machine learning algorithms to analyze business process flowcharts and approval process procedures, gaining a detailed understanding of each step. Regarding security and legal rules, it analyzes relevant documents and regulations to clarify points that must be complied with. Furthermore, regarding the current system configuration, it analyzes network diagrams and system architecture diagrams to understand the interrelationships and dependencies of each system. This allows the Learning Department to gain a detailed understanding of the current situation within the company and prepare foundational information for the introduction of cloud tools. The Learning Department centrally manages this information and can collaborate with other departments and systems as needed. For example, the information collected by the Learning Department is stored in a cloud-based database, making it accessible to the Hearing Department and the Proposal Department. Furthermore, the learning department can consistently provide accurate information by regularly updating information and maintaining the latest internal company data. This allows the learning department to efficiently and effectively learn internal company information and improve the overall performance of the cloud tool implementation support system.

[0031] The Hearing Department conducts interviews with the person in charge based on information learned by the Learning Department. Specifically, the Hearing Department asks detailed questions to the person in charge to understand the requirements necessary for introducing cloud tools. For example, the Hearing Department asks about the person in charge's work content, current challenges, and the functions and performance they require from the cloud tool. By using AI, the person in charge's answers can be quickly and accurately analyzed to extract the necessary requirements. For example, natural language processing technology is used to analyze the person in charge's answers as text and extract important keywords and phrases. This allows the Hearing Department to accurately understand the person in charge's needs and clarify the specific requirements for introducing cloud tools. Furthermore, the Hearing Department can utilize conversational AI to facilitate smooth communication with the person in charge. For example, conversational AI can be used to automate conversations with the person in charge and conduct interviews efficiently. The Hearing Department also organizes the information obtained from the person in charge and provides it to the Proposal Department. This allows the Proposal Department to propose the most suitable cloud tool based on the person in charge's requirements. The Hearing Department conducts interviews with the person in charge regularly to respond to changes in requirements and new needs. This allows the hearing department to continuously understand the needs of the person in charge and improve the overall flexibility and adaptability of the cloud tool implementation support system.

[0032] The proposal department proposes cloud tools based on the requirements obtained by the hearing department. Specifically, the proposal department compares cloud tools from the perspectives of feasibility of what they want to achieve, legal / security considerations, and cost. By using AI, they can quickly and accurately compare the functions and costs of cloud tools and propose the most suitable tool. For example, the proposal department analyzes cloud tool catalogs and specifications to evaluate the functions and performance of each tool. They also analyze relevant regulations and guidelines regarding legal and security requirements to evaluate the suitability of each tool. Furthermore, regarding costs, they compare the implementation costs and operating costs of each tool to evaluate cost performance. This allows the proposal department to propose the cloud tool that best suits the requirements of the person in charge. The proposal department explains the proposal to the person in charge, clarifying the advantages and disadvantages of implementation. For example, the proposal department explains how the introduction of cloud tools will improve work efficiency and enhance security. They also accurately explain the costs and risks associated with implementation to the person in charge, supporting their decision-making. The proposal department can collect feedback from the person in charge and improve the proposal. This allows the proposal department to suggest the most suitable cloud tool for the needs of the person in charge, maximizing the overall effectiveness of the cloud tool implementation support system.

[0033] The Support Department assists with the implementation process of cloud tools proposed by the Proposal Department. Specifically, the Support Department proposes the necessary procedures and approval processes for implementation, and supports the person in charge to ensure a smooth implementation. By using AI, the Support Department can monitor the progress of the implementation process in real time and provide necessary support. For example, the Support Department can automate each step of the implementation procedure and notify the person in charge of the progress. Regarding the approval process, it can also automatically collect relevant documents and data and submit them to approvers. This allows the Support Department to streamline the implementation process and reduce the workload of the person in charge. Furthermore, the Support Department also provides post-implementation support. For example, it provides support to the person in charge regarding the use of cloud tools and troubleshooting. By using AI, it can respond quickly and accurately to inquiries from the person in charge and resolve problems. For example, it can use a chatbot to automatically answer inquiries from the person in charge. The Support Department also monitors the operational status of the cloud tools after implementation and makes improvement suggestions as needed. This allows the Support Department to provide consistent support from cloud tool implementation to operation, maximizing the effectiveness of the entire cloud tool implementation support system.

[0034] The learning unit can learn internal company information such as business processes, approval processes leading up to implementation, security / legal rules, and the current system configuration. For example, the learning unit can learn the detailed flow of business processes and the specific content of each task. The learning unit can also learn the flow of approval processes and the roles of approvers. The learning unit can also learn the content of security / legal rules and legal regulations. The learning unit can also learn the details of the hardware and software of the current system configuration. As a result, by learning internal company information in detail, the learning unit can improve the accuracy of its cloud tool proposals and implementation processes. Some or all of the above processes in the learning unit may be performed using AI or not. For example, the learning unit can input internal company information into AI, and the AI ​​can analyze and learn from the information.

[0035] The interviewing department can conduct interviews with the person in charge to understand the necessary requirements. For example, the interviewing department can ask questions to the person in charge to understand what challenges they are facing and what functions they need. The interviewing department can use AI to analyze the person in charge's answers and extract the necessary requirements. For example, the interviewing department can use natural language processing technology to analyze the person in charge's answers and extract the requirements. In this way, the interviewing department can accurately understand the requirements necessary for proposing cloud tools by conducting interviews with the person in charge. Some or all of the above processes in the interviewing department may be performed using AI or not. For example, the interviewing department can input the person in charge's answers into AI, and the AI ​​can analyze the answers and extract the requirements.

[0036] The proposal department can compare cloud tools based on feasibility of the desired outcome, legal / security considerations, and cost considerations. For example, the proposal department can compare the functions and costs of cloud tools and propose the optimal tool. The proposal department can use AI to compare cloud tools and propose the optimal tool. For example, the proposal department can compare the functions and costs of cloud tools and propose the optimal tool. In this way, the proposal department can propose the optimal cloud tool by comparing cloud tools from multiple perspectives. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input information about cloud tools into AI, and the AI ​​can analyze the information and propose the optimal tool.

[0037] The support department can propose the necessary procedures and approval processes for implementation and support the person in charge so that the implementation can proceed smoothly. For example, the support department can propose the necessary procedures and approval processes for implementation and support the person in charge so that the implementation can proceed smoothly. The support department can use AI to support the implementation process. For example, the support department can monitor the progress of the implementation procedures and provide necessary support. In this way, the support department can support the implementation process so that the person in charge can smoothly implement the cloud tool. Some or all of the above processes in the support department may be performed using AI or not. For example, the support department can input the progress of the implementation procedures into AI, and the AI ​​can analyze the progress and provide necessary support.

[0038] The learning unit can optimize its learning algorithm by referencing past project data within the company during the learning process. For example, the learning unit can extract success stories from past project data and incorporate them into the learning algorithm. The learning unit can also extract failure stories from past project data and incorporate them into the learning algorithm. The learning unit can also analyze past project data and derive the optimal learning pattern. In this way, the learning unit can improve the accuracy of its learning algorithm by referring to past project data. Some or all of the above processes in the learning unit may be performed using AI or not. For example, the learning unit can input past project data into AI, and the AI ​​can analyze the data to optimize the learning algorithm.

[0039] The learning department can adjust its learning content by incorporating feedback from different departments within the company during the learning process. For example, the learning department can collect feedback from different departments and reflect it in the learning content. The learning department can also integrate opinions from different departments and adjust the learning algorithm. The learning department can also optimize the learning content by considering the needs of different departments. In this way, the learning department can improve the accuracy of the learning content by incorporating feedback from different departments. Some or all of the above processes in the learning department may be performed using AI or not. For example, the learning department can input feedback from different departments into AI, which can then analyze the feedback and adjust the learning content.

[0040] The learning unit can collect information while considering the geographical distribution within the company. For example, the learning unit can collect information while considering the business processes of each location. The learning unit can also collect information while considering the security / legal rules of each location. The learning unit can also collect information while considering the system configuration of each location. This allows the learning unit to perform more accurate learning by collecting information while considering the geographical distribution within the company. Some or all of the above processes in the learning unit may be performed using AI or not. For example, the learning unit can input information from each location into the AI, and the AI ​​can analyze the information and learn.

[0041] The learning unit can analyze internal social media activities and learn relevant information during the learning process. For example, the learning unit can extract trends from internal social media activities and reflect them in its learning content. The learning unit can also extract important information from internal social media activities and reflect it in its learning content. The learning unit can also analyze internal social media activities and optimize its learning algorithm. This allows the learning unit to efficiently learn relevant information by analyzing internal social media activities. Some or all of the above processes in the learning unit may be performed using AI or not. For example, the learning unit can input internal social media activity data into an AI, which can then analyze the data and learn relevant information.

[0042] The interviewing unit can select the most appropriate questions during an interview by referring to the interviewer's past statements. For example, the interviewing unit can select relevant questions from the interviewer's past statements. The interviewing unit can also analyze the interviewer's past statements and generate the most appropriate questions. The interviewing unit can also optimize the order of questions based on the interviewer's past statements. In this way, the interviewing unit can select the most appropriate questions by referring to the interviewer's past statements. Some or all of the above processes in the interviewing unit may be performed using AI or not. For example, the interviewing unit can input the interviewer's past statements into an AI, which can then analyze the history and select the most appropriate questions.

[0043] The interviewing unit can apply different questioning algorithms during interviews depending on the interviewer's position and job responsibilities. For example, the interviewing unit can select appropriate questions based on the interviewer's position. The interviewing unit can also select relevant questions based on the interviewer's job responsibilities. The interviewing unit can also apply the optimal questioning algorithm considering the interviewer's position and job responsibilities. This allows the interviewing unit to conduct efficient interviews by applying questioning algorithms according to the interviewer's position and job responsibilities. Some or all of the above processes in the interviewing unit may be performed using AI or not. For example, the interviewing unit can input the interviewer's position and job responsibilities into an AI, which can then analyze the information and apply the optimal questioning algorithm.

[0044] The interviewing unit can select the optimal interviewing method by considering the geographical location information of the person in charge during the interview. For example, the interviewing unit can select an appropriate interviewing method by considering the geographical location information of the person in charge. The interviewing unit can also select the optimal interviewing timing based on the geographical location information of the person in charge. The interviewing unit can also analyze the geographical location information of the person in charge and optimize the interviewing method. In this way, the interviewing unit can select the optimal interviewing method by considering the geographical location information of the person in charge. Some or all of the above processing in the interviewing unit may be performed using AI or not. For example, the interviewing unit can input the geographical location information of the person in charge into AI, and the AI ​​can analyze the information and select the optimal interviewing method.

[0045] The interviewing department can analyze the social media activity of the person in charge during the interview and ask relevant questions. For example, the interviewing department can select relevant questions from the person in charge's social media activity. The interviewing department can also analyze the person in charge's social media activity and generate the most appropriate questions. The interviewing department can also optimize the order of questions based on the person in charge's social media activity. In this way, the interviewing department can efficiently ask relevant questions by analyzing the person in charge's social media activity. Some or all of the above processes in the interviewing department may be performed using AI or not. For example, the interviewing department can input the person in charge's social media activity data into AI, and the AI ​​can analyze the data and generate relevant questions.

[0046] The proposal department can adjust the level of detail in its proposals based on the importance of the cloud tools. For example, the proposal department can provide detailed proposals for highly important cloud tools, while providing concise proposals for less important cloud tools. The proposal department can also adjust the level of detail in its proposals considering the importance of the cloud tools. This allows the proposal department to make efficient proposals by adjusting the level of detail according to the importance of the cloud tools. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input cloud tool importance data into an AI, which can then analyze the data and adjust the level of detail in its proposals.

[0047] The proposal unit can apply different proposal algorithms depending on the category of the cloud tool when making a proposal. For example, the proposal unit can apply an appropriate proposal algorithm depending on the category of the cloud tool. The proposal unit can also make the optimal proposal considering the category of the cloud tool. The proposal unit can also optimize the order of proposals based on the category of the cloud tool. In this way, the proposal unit can make efficient proposals by applying a proposal algorithm according to the category of the cloud tool. Some or all of the above processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input cloud tool category data into an AI, which can analyze the data and apply the optimal proposal algorithm.

[0048] The proposal department can prioritize proposals based on the timing of cloud tool implementation. For example, the proposal department can prioritize proposals for cloud tools whose implementation date is approaching. The proposal department can also postpone proposals for cloud tools whose implementation date is far off. The proposal department can also prioritize proposals considering the timing of cloud tool implementation. This allows the proposal department to make efficient proposals by prioritizing proposals according to the timing of cloud tool implementation. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input cloud tool implementation date data into AI, and the AI ​​can analyze the data to determine the priority of proposals.

[0049] The proposal department can adjust the order of proposals based on the relevance of the cloud tools during the proposal process. For example, the proposal department can prioritize proposals for highly relevant cloud tools. The proposal department can also postpone proposals for less relevant cloud tools. The proposal department can also adjust the order of proposals considering the relevance of the cloud tools. This allows the proposal department to make efficient proposals by adjusting the order of proposals according to the relevance of the cloud tools. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input cloud tool relevance data into an AI, which can then analyze the data and adjust the order of proposals.

[0050] The support unit can select the optimal support method by referring to past support history during support. For example, the support unit can select the optimal support method from past support history. The support unit can also analyze past support history and apply the optimal support algorithm. The support unit can also optimize the order of support based on past support history. In this way, the support unit can select the optimal support method by referring to past support history. Some or all of the above processes in the support unit may be performed using AI or not. For example, the support unit can input past support history data into AI, and the AI ​​can analyze the data and select the optimal support method.

[0051] The support department can apply different support algorithms depending on the position and job duties of the person providing support. For example, the support department can select an appropriate support method based on the person's position. The support department can also select relevant support methods based on the person's job duties. The support department can also apply the optimal support algorithm considering the person's position and job duties. This allows the support department to provide efficient support by applying support algorithms according to the person's position and job duties. Some or all of the above processes in the support department may be performed using AI, or not. For example, the support department can input data on the person's position and job duties into an AI, which can then analyze the data and apply the optimal support algorithm.

[0052] The support department can select the optimal support method by considering the geographical location information of the person providing support. For example, the support department can select an appropriate support method by considering the geographical location information of the person providing support. The support department can also select the optimal support timing based on the geographical location information of the person providing support. The support department can also analyze the geographical location information of the person providing support and optimize the support method. In this way, the support department can select the optimal support method by considering the geographical location information of the person providing support. Some or all of the above processes in the support department may be performed using AI or not. For example, the support department can input the geographical location information of the person providing support into AI, and the AI ​​can analyze the information and select the optimal support method.

[0053] The support department can analyze the social media activity of the person providing support and provide relevant support. For example, the support department can select relevant support based on the person's social media activity. The support department can also analyze the person's social media activity and provide the most appropriate support. The support department can also optimize the order of support based on the person's social media activity. In this way, the support department can efficiently provide relevant support by analyzing the person's social media activity. Some or all of the above processes in the support department may be performed using AI or not. For example, the support department can input the person's social media activity data into AI, and the AI ​​can analyze the data and provide relevant support.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The cloud tool implementation support system can also include a feedback collection unit. This unit can collect feedback from users after the cloud tool has been implemented, and use it to improve the system. For example, the feedback collection unit can gather user feedback and problems from users after implementation, and identify areas for system improvement. The feedback collection unit can also analyze the collected feedback and incorporate it into future cloud tool implementations. This allows the cloud tool implementation support system to be continuously improved, providing more effective implementation support.

[0056] The cloud tool implementation support system can also include a training department. This training department can provide training to personnel after the cloud tool has been implemented. For example, the training department can teach personnel the basic and advanced uses of the cloud tool. The training department can also support the skill development of personnel through online courses and workshops. In this way, the cloud tool implementation support system can help improve the skills of personnel after implementation and promote the effective use of the cloud tool.

[0057] The cloud tool implementation support system can also include a risk assessment department. This department can evaluate the risks associated with cloud tool implementation and propose risk management strategies to the responsible personnel. For example, it can assess security and legal risks and propose risk mitigation measures. It can also evaluate operational risks after cloud tool implementation and communicate operational considerations to the responsible personnel. This allows the cloud tool implementation support system to strengthen risk management and support the safe and effective implementation of cloud tools.

[0058] The cloud tool implementation support system can also include a customization section. This customization section can customize the cloud tool to meet the specific needs of the user during implementation. For example, the customization section can adjust the cloud tool's functions to suit the user's work. The customization section can also modify the cloud tool's interface according to the user's requests. This allows the cloud tool implementation support system to respond flexibly to the user's needs and effectively facilitate the implementation of cloud tools.

[0059] The cloud tool implementation support system can also include a performance monitoring unit. This unit can monitor the performance of the cloud tool after its implementation and provide improvement suggestions to the responsible personnel. For example, the performance monitoring unit can monitor the usage of the cloud tool in real time and detect performance degradation. It can also analyze the causes of performance degradation and propose improvement measures. This allows the cloud tool implementation support system to optimize post-implementation performance and support the effective operation of the cloud tool.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The learning unit learns internal company information. Specifically, it learns internal company information such as business processes, approval processes for implementation, security / legal rules, and the current system configuration. The learning unit uses AI and machine learning algorithms to analyze and learn internal company information in detail. Step 2: The interviewing department conducts interviews with the person in charge based on the information learned by the learning department. Specifically, they ask questions to the person in charge to understand the necessary requirements. The interviewing department uses AI and natural language processing technology to analyze the person in charge's answers and extract the necessary requirements. Step 3: The proposal team proposes cloud tools based on the requirements obtained by the hearing team. Specifically, they compare cloud tools based on feasibility of what they want to achieve, legal / security considerations, and cost considerations. The proposal team uses AI to compare the functions and costs of cloud tools and proposes the most suitable one. Step 4: The support department assists with the implementation process of the cloud tools proposed by the proposal department. Specifically, they propose the necessary procedures and approval processes for implementation and support the person in charge to ensure a smooth implementation. The support department uses AI to monitor the progress of the implementation procedures and provide necessary support.

[0062] (Example of form 2) The cloud tool implementation support system according to an embodiment of the present invention is a system that supports the cloud tool implementation process by having AI learn internal company information and conduct interviews with the person in charge. In this cloud tool implementation support system, the AI ​​learns the current internal company information (business processes, approval processes for implementation, security / legal rules, current system configuration) and conducts interviews with the person in charge. Based on the necessary requirements, it proposes multiple methods for solving problems using cloud tools after comparison. The AI ​​then proposes and supports the entire process up to implementation. This enables the implementation of the optimal cloud tool and reduces the workload of the person in charge during the implementation consideration process. For example, in the cloud tool implementation support system, the AI ​​first learns the current internal company information. At this time, it analyzes information such as business processes, approval processes for implementation, security / legal rules, and current system configuration in detail. Next, the cloud tool implementation support system has the AI ​​conduct interviews with the person in charge to understand the necessary requirements. For example, it interviews the person in charge about what kind of problems they are facing and what kind of functions they need. Based on this, the cloud tool implementation support system has the AI ​​propose multiple methods for solving problems using cloud tools. The proposed cloud tools are compared based on factors such as feasibility of achieving the desired outcome, legal / security considerations, and cost. For example, the system presents a comparison of tool A and tool B, indicating which is more suitable. Finally, the cloud tool implementation support system uses AI to propose and support the implementation process. For instance, it suggests the necessary procedures and approval processes, supporting the person in charge to ensure a smooth implementation. This allows for the adoption of the optimal cloud tool and reduces the workload on the person in charge during the implementation evaluation process. In this way, the cloud tool implementation support system reduces the workload on the person in charge and enables efficient cloud tool implementation.

[0063] The cloud tool implementation support system according to this embodiment comprises a learning unit, an interviewing unit, a proposal unit, and a support unit. The learning unit learns internal company information. For example, the learning unit learns internal company information such as business processes, approval processes up to implementation, security / legal rules, and the current system configuration. The learning unit can analyze and learn internal company information in detail using AI. For example, the learning unit analyzes and learns internal company information using machine learning algorithms. The interviewing unit conducts interviews with the person in charge based on the information learned by the learning unit. For example, the interviewing unit asks questions to the person in charge to understand the necessary requirements. The interviewing unit can analyze the person in charge's answers using AI and extract the necessary requirements. For example, the interviewing unit analyzes the person in charge's answers using natural language processing technology and extracts the requirements. The proposal unit proposes cloud tools based on the requirements obtained by the interviewing unit. The proposal unit compares cloud tools from perspectives such as feasibility of what they want to do, legal / security aspects, and cost aspects. The proposal department can use AI to compare cloud tools and propose the most suitable one. For example, the proposal department can compare the functions and costs of cloud tools and propose the optimal tool. The support department supports the implementation process of the cloud tools proposed by the proposal department. For example, the support department proposes the necessary procedures and approval processes for implementation and supports the person in charge so that the implementation can proceed smoothly. The support department can use AI to support the implementation process. For example, the support department monitors the progress of the implementation procedures and provides necessary support. As a result, the cloud tool implementation support system according to this embodiment can reduce the workload of the person in charge and realize efficient cloud tool implementation.

[0064] The Learning Department learns internal company information. For example, it learns internal information such as business processes, approval processes for implementation, security / legal rules, and the current system configuration. Specifically, the Learning Department collects documents and databases provided by various departments within the company, and integrates and analyzes this information. By using AI, it can efficiently process vast amounts of data and extract important information. For example, it uses machine learning algorithms to analyze business process flowcharts and approval process procedures, gaining a detailed understanding of each step. Regarding security and legal rules, it analyzes relevant documents and regulations to clarify points that must be complied with. Furthermore, regarding the current system configuration, it analyzes network diagrams and system architecture diagrams to understand the interrelationships and dependencies of each system. This allows the Learning Department to gain a detailed understanding of the current situation within the company and prepare foundational information for the introduction of cloud tools. The Learning Department centrally manages this information and can collaborate with other departments and systems as needed. For example, the information collected by the Learning Department is stored in a cloud-based database, making it accessible to the Hearing Department and the Proposal Department. Furthermore, the learning department can consistently provide accurate information by regularly updating information and maintaining the latest internal company data. This allows the learning department to efficiently and effectively learn internal company information and improve the overall performance of the cloud tool implementation support system.

[0065] The Hearing Department conducts interviews with the person in charge based on information learned by the Learning Department. Specifically, the Hearing Department asks detailed questions to the person in charge to understand the requirements necessary for introducing cloud tools. For example, the Hearing Department asks about the person in charge's work content, current challenges, and the functions and performance they require from the cloud tool. By using AI, the person in charge's answers can be quickly and accurately analyzed to extract the necessary requirements. For example, natural language processing technology is used to analyze the person in charge's answers as text and extract important keywords and phrases. This allows the Hearing Department to accurately understand the person in charge's needs and clarify the specific requirements for introducing cloud tools. Furthermore, the Hearing Department can utilize conversational AI to facilitate smooth communication with the person in charge. For example, conversational AI can be used to automate conversations with the person in charge and conduct interviews efficiently. The Hearing Department also organizes the information obtained from the person in charge and provides it to the Proposal Department. This allows the Proposal Department to propose the most suitable cloud tool based on the person in charge's requirements. The Hearing Department conducts interviews with the person in charge regularly to respond to changes in requirements and new needs. This allows the hearing department to continuously understand the needs of the person in charge and improve the overall flexibility and adaptability of the cloud tool implementation support system.

[0066] The proposal department proposes cloud tools based on the requirements obtained by the hearing department. Specifically, the proposal department compares cloud tools from the perspectives of feasibility of what they want to achieve, legal / security considerations, and cost. By using AI, they can quickly and accurately compare the functions and costs of cloud tools and propose the most suitable tool. For example, the proposal department analyzes cloud tool catalogs and specifications to evaluate the functions and performance of each tool. They also analyze relevant regulations and guidelines regarding legal and security requirements to evaluate the suitability of each tool. Furthermore, regarding costs, they compare the implementation costs and operating costs of each tool to evaluate cost performance. This allows the proposal department to propose the cloud tool that best suits the requirements of the person in charge. The proposal department explains the proposal to the person in charge, clarifying the advantages and disadvantages of implementation. For example, the proposal department explains how the introduction of cloud tools will improve work efficiency and enhance security. They also accurately explain the costs and risks associated with implementation to the person in charge, supporting their decision-making. The proposal department can collect feedback from the person in charge and improve the proposal. This allows the proposal department to suggest the most suitable cloud tool for the needs of the person in charge, maximizing the overall effectiveness of the cloud tool implementation support system.

[0067] The Support Department assists with the implementation process of cloud tools proposed by the Proposal Department. Specifically, the Support Department proposes the necessary procedures and approval processes for implementation, and supports the person in charge to ensure a smooth implementation. By using AI, the Support Department can monitor the progress of the implementation process in real time and provide necessary support. For example, the Support Department can automate each step of the implementation procedure and notify the person in charge of the progress. Regarding the approval process, it can also automatically collect relevant documents and data and submit them to approvers. This allows the Support Department to streamline the implementation process and reduce the workload of the person in charge. Furthermore, the Support Department also provides post-implementation support. For example, it provides support to the person in charge regarding the use of cloud tools and troubleshooting. By using AI, it can respond quickly and accurately to inquiries from the person in charge and resolve problems. For example, it can use a chatbot to automatically answer inquiries from the person in charge. The Support Department also monitors the operational status of the cloud tools after implementation and makes improvement suggestions as needed. This allows the Support Department to provide consistent support from cloud tool implementation to operation, maximizing the effectiveness of the entire cloud tool implementation support system.

[0068] The learning unit can learn internal company information such as business processes, approval processes leading up to implementation, security / legal rules, and the current system configuration. For example, the learning unit can learn the detailed flow of business processes and the specific content of each task. The learning unit can also learn the flow of approval processes and the roles of approvers. The learning unit can also learn the content of security / legal rules and legal regulations. The learning unit can also learn the details of the hardware and software of the current system configuration. As a result, by learning internal company information in detail, the learning unit can improve the accuracy of its cloud tool proposals and implementation processes. Some or all of the above processes in the learning unit may be performed using AI or not. For example, the learning unit can input internal company information into AI, and the AI ​​can analyze and learn from the information.

[0069] The interviewing department can conduct interviews with the person in charge to understand the necessary requirements. For example, the interviewing department can ask questions to the person in charge to understand what challenges they are facing and what functions they need. The interviewing department can use AI to analyze the person in charge's answers and extract the necessary requirements. For example, the interviewing department can use natural language processing technology to analyze the person in charge's answers and extract the requirements. In this way, the interviewing department can accurately understand the requirements necessary for proposing cloud tools by conducting interviews with the person in charge. Some or all of the above processes in the interviewing department may be performed using AI or not. For example, the interviewing department can input the person in charge's answers into AI, and the AI ​​can analyze the answers and extract the requirements.

[0070] The proposal department can compare cloud tools based on feasibility of the desired outcome, legal / security considerations, and cost considerations. For example, the proposal department can compare the functions and costs of cloud tools and propose the optimal tool. The proposal department can use AI to compare cloud tools and propose the optimal tool. For example, the proposal department can compare the functions and costs of cloud tools and propose the optimal tool. In this way, the proposal department can propose the optimal cloud tool by comparing cloud tools from multiple perspectives. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input information about cloud tools into AI, and the AI ​​can analyze the information and propose the optimal tool.

[0071] The support department can propose the necessary procedures and approval processes for implementation and support the person in charge so that the implementation can proceed smoothly. For example, the support department can propose the necessary procedures and approval processes for implementation and support the person in charge so that the implementation can proceed smoothly. The support department can use AI to support the implementation process. For example, the support department can monitor the progress of the implementation procedures and provide necessary support. In this way, the support department can support the implementation process so that the person in charge can smoothly implement the cloud tool. Some or all of the above processes in the support department may be performed using AI or not. For example, the support department can input the progress of the implementation procedures into AI, and the AI ​​can analyze the progress and provide necessary support.

[0072] The learning unit can estimate the emotions of the person in charge and determine the priority of information to learn based on the estimated emotions. For example, if the person in charge is stressed, the learning unit will prioritize learning information of high importance. If the person in charge is relaxed, the learning unit can also learn information that includes details. If the person in charge is in a hurry, the learning unit can also prioritize information that can be learned quickly. In this way, the learning unit can perform efficient learning by determining the priority of information to learn according to the emotions of the person in charge. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is 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 learning unit may be performed using AI or not using AI. For example, the learning unit can input the person in charge's emotion data into an AI, and the AI ​​can analyze the emotions and determine the priority of information to learn.

[0073] The learning unit can optimize its learning algorithm by referencing past project data within the company during the learning process. For example, the learning unit can extract success stories from past project data and incorporate them into the learning algorithm. The learning unit can also extract failure stories from past project data and incorporate them into the learning algorithm. The learning unit can also analyze past project data and derive the optimal learning pattern. In this way, the learning unit can improve the accuracy of its learning algorithm by referring to past project data. Some or all of the above processes in the learning unit may be performed using AI or not. For example, the learning unit can input past project data into AI, and the AI ​​can analyze the data to optimize the learning algorithm.

[0074] The learning department can adjust its learning content by incorporating feedback from different departments within the company during the learning process. For example, the learning department can collect feedback from different departments and reflect it in the learning content. The learning department can also integrate opinions from different departments and adjust the learning algorithm. The learning department can also optimize the learning content by considering the needs of different departments. In this way, the learning department can improve the accuracy of the learning content by incorporating feedback from different departments. Some or all of the above processes in the learning department may be performed using AI or not. For example, the learning department can input feedback from different departments into AI, which can then analyze the feedback and adjust the learning content.

[0075] The learning unit can estimate the emotions of the person in charge and adjust the level of detail of the information it learns based on the estimated emotions. For example, if the person in charge is stressed, the learning unit will provide concise information. If the person in charge is relaxed, the learning unit may also provide detailed information. If the person in charge is in a hurry, the learning unit may also provide concise information. This allows the learning unit to learn efficiently by adjusting the level of detail of the information it learns according to the emotions of the person in charge. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input the person in charge's emotion data into an AI, and the AI ​​can analyze the emotions and adjust the level of detail of the information it learns.

[0076] The learning unit can collect information while considering the geographical distribution within the company. For example, the learning unit can collect information while considering the business processes of each location. The learning unit can also collect information while considering the security / legal rules of each location. The learning unit can also collect information while considering the system configuration of each location. This allows the learning unit to perform more accurate learning by collecting information while considering the geographical distribution within the company. Some or all of the above processes in the learning unit may be performed using AI or not. For example, the learning unit can input information from each location into the AI, and the AI ​​can analyze the information and learn.

[0077] The learning unit can analyze internal social media activities and learn relevant information during the learning process. For example, the learning unit can extract trends from internal social media activities and reflect them in its learning content. The learning unit can also extract important information from internal social media activities and reflect it in its learning content. The learning unit can also analyze internal social media activities and optimize its learning algorithm. This allows the learning unit to efficiently learn relevant information by analyzing internal social media activities. Some or all of the above processes in the learning unit may be performed using AI or not. For example, the learning unit can input internal social media activity data into an AI, which can then analyze the data and learn relevant information.

[0078] The interview unit can estimate the emotions of the person in charge and adjust the timing of the interview based on the estimated emotions. For example, if the person in charge is feeling stressed, the interview unit will conduct the interview at a time when they are relaxed. If the person in charge is relaxed, the interview unit can also conduct a detailed interview. If the person in charge is in a hurry, the interview unit can conduct a rapid interview. In this way, the interview unit can conduct efficient interviews by adjusting the timing of the interview according to the emotions of the person in charge. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interview unit may be performed using AI or not. For example, the interview unit can input the person in charge's emotion data into AI, and the AI ​​can analyze the emotions and adjust the timing of the interview.

[0079] The interviewing unit can select the most appropriate questions during an interview by referring to the interviewer's past statements. For example, the interviewing unit can select relevant questions from the interviewer's past statements. The interviewing unit can also analyze the interviewer's past statements and generate the most appropriate questions. The interviewing unit can also optimize the order of questions based on the interviewer's past statements. In this way, the interviewing unit can select the most appropriate questions by referring to the interviewer's past statements. Some or all of the above processes in the interviewing unit may be performed using AI or not. For example, the interviewing unit can input the interviewer's past statements into an AI, which can then analyze the history and select the most appropriate questions.

[0080] The interviewing unit can apply different questioning algorithms during interviews depending on the interviewer's position and job responsibilities. For example, the interviewing unit can select appropriate questions based on the interviewer's position. The interviewing unit can also select relevant questions based on the interviewer's job responsibilities. The interviewing unit can also apply the optimal questioning algorithm considering the interviewer's position and job responsibilities. This allows the interviewing unit to conduct efficient interviews by applying questioning algorithms according to the interviewer's position and job responsibilities. Some or all of the above processes in the interviewing unit may be performed using AI or not. For example, the interviewing unit can input the interviewer's position and job responsibilities into an AI, which can then analyze the information and apply the optimal questioning algorithm.

[0081] The interviewing unit can estimate the emotions of the person conducting the interview and adjust the interview method based on the estimated emotions. For example, if the person conducting the interview is stressed, the interviewing unit can conduct the interview in a relaxed manner. If the person conducting the interview is relaxed, the interviewing unit can also conduct a detailed interview. If the person conducting the interview is in a hurry, the interviewing unit can conduct a rapid interview. In this way, the interviewing unit can conduct efficient interviews by adjusting the interview method according to the emotions of the person conducting the interview. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interviewing unit may be performed using AI or not. For example, the interviewing unit can input the person's emotion data into the AI, which can analyze the emotions and adjust the interview method.

[0082] The interviewing unit can select the optimal interviewing method by considering the geographical location information of the person in charge during the interview. For example, the interviewing unit can select an appropriate interviewing method by considering the geographical location information of the person in charge. The interviewing unit can also select the optimal interviewing timing based on the geographical location information of the person in charge. The interviewing unit can also analyze the geographical location information of the person in charge and optimize the interviewing method. In this way, the interviewing unit can select the optimal interviewing method by considering the geographical location information of the person in charge. Some or all of the above processing in the interviewing unit may be performed using AI or not. For example, the interviewing unit can input the geographical location information of the person in charge into AI, and the AI ​​can analyze the information and select the optimal interviewing method.

[0083] The interviewing department can analyze the social media activity of the person in charge during the interview and ask relevant questions. For example, the interviewing department can select relevant questions from the person in charge's social media activity. The interviewing department can also analyze the person in charge's social media activity and generate the most appropriate questions. The interviewing department can also optimize the order of questions based on the person in charge's social media activity. In this way, the interviewing department can efficiently ask relevant questions by analyzing the person in charge's social media activity. Some or all of the above processes in the interviewing department may be performed using AI or not. For example, the interviewing department can input the person in charge's social media activity data into AI, and the AI ​​can analyze the data and generate relevant questions.

[0084] The proposal unit can estimate the emotions of the person in charge and adjust the way the proposal is expressed based on the estimated emotions. For example, if the person in charge is stressed, the proposal unit will make a concise proposal. If the person in charge is relaxed, the proposal unit can also make a detailed proposal. If the person in charge is in a hurry, the proposal unit can also make a quick proposal. In this way, the proposal unit can make efficient proposals by adjusting the way the proposal is expressed according to the emotions of the person in charge. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the person in charge's emotion data into AI, and the AI ​​can analyze the emotions and adjust the way the proposal is expressed.

[0085] The proposal department can adjust the level of detail in its proposals based on the importance of the cloud tools. For example, the proposal department can provide detailed proposals for highly important cloud tools, while providing concise proposals for less important cloud tools. The proposal department can also adjust the level of detail in its proposals considering the importance of the cloud tools. This allows the proposal department to make efficient proposals by adjusting the level of detail according to the importance of the cloud tools. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input cloud tool importance data into an AI, which can then analyze the data and adjust the level of detail in its proposals.

[0086] The proposal unit can apply different proposal algorithms depending on the category of the cloud tool when making a proposal. For example, the proposal unit can apply an appropriate proposal algorithm depending on the category of the cloud tool. The proposal unit can also make the optimal proposal considering the category of the cloud tool. The proposal unit can also optimize the order of proposals based on the category of the cloud tool. In this way, the proposal unit can make efficient proposals by applying a proposal algorithm according to the category of the cloud tool. Some or all of the above processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input cloud tool category data into an AI, which can analyze the data and apply the optimal proposal algorithm.

[0087] The proposal unit can estimate the emotions of the person in charge and adjust the length of the proposal based on the estimated emotions. For example, if the person in charge is stressed, the proposal unit will make a concise proposal. If the person in charge is relaxed, the proposal unit can also make a detailed proposal. If the person in charge is in a hurry, the proposal unit can make a quick proposal. In this way, the proposal unit can make efficient proposals by adjusting the length of the proposal according to the emotions of the person in charge. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the person in charge's emotion data into an AI, which can analyze the emotions and adjust the length of the proposal.

[0088] The proposal department can prioritize proposals based on the timing of cloud tool implementation. For example, the proposal department can prioritize proposals for cloud tools whose implementation date is approaching. The proposal department can also postpone proposals for cloud tools whose implementation date is far off. The proposal department can also prioritize proposals considering the timing of cloud tool implementation. This allows the proposal department to make efficient proposals by prioritizing proposals according to the timing of cloud tool implementation. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input cloud tool implementation date data into AI, and the AI ​​can analyze the data to determine the priority of proposals.

[0089] The proposal department can adjust the order of proposals based on the relevance of the cloud tools during the proposal process. For example, the proposal department can prioritize proposals for highly relevant cloud tools. The proposal department can also postpone proposals for less relevant cloud tools. The proposal department can also adjust the order of proposals considering the relevance of the cloud tools. This allows the proposal department to make efficient proposals by adjusting the order of proposals according to the relevance of the cloud tools. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input cloud tool relevance data into an AI, which can then analyze the data and adjust the order of proposals.

[0090] The support unit can estimate the emotions of the staff member and adjust its support methods based on the estimated emotions. For example, if the staff member is stressed, the support unit will provide support in a relaxed manner. If the staff member is relaxed, the support unit can also provide detailed support. If the staff member is in a hurry, the support unit can provide support quickly. In this way, the support unit can provide efficient support by adjusting its support methods according to the emotions of the staff member. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI or not. For example, the support unit can input the staff member's emotion data into an AI, which can analyze the emotions and adjust the support method.

[0091] The support unit can select the optimal support method by referring to past support history during support. For example, the support unit can select the optimal support method from past support history. The support unit can also analyze past support history and apply the optimal support algorithm. The support unit can also optimize the order of support based on past support history. In this way, the support unit can select the optimal support method by referring to past support history. Some or all of the above processes in the support unit may be performed using AI or not. For example, the support unit can input past support history data into AI, and the AI ​​can analyze the data and select the optimal support method.

[0092] The support department can apply different support algorithms depending on the position and job duties of the person providing support. For example, the support department can select an appropriate support method based on the person's position. The support department can also select relevant support methods based on the person's job duties. The support department can also apply the optimal support algorithm considering the person's position and job duties. This allows the support department to provide efficient support by applying support algorithms according to the person's position and job duties. Some or all of the above processes in the support department may be performed using AI, or not. For example, the support department can input data on the person's position and job duties into an AI, which can then analyze the data and apply the optimal support algorithm.

[0093] The support department can estimate the emotions of the staff member and determine the priority of support based on the estimated emotions. For example, if the staff member is stressed, the support department will prioritize high-priority support. If the staff member is relaxed, the support department can also provide detailed support. If the staff member is in a hurry, the support department can also provide rapid support. In this way, the support department can provide efficient support by determining the priority of support according to the emotions of the staff member. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support department may be performed using AI or not. For example, the support department can input staff member emotion data into an AI, which can analyze the emotions and determine the priority of support.

[0094] The support department can select the optimal support method by considering the geographical location information of the person providing support. For example, the support department can select an appropriate support method by considering the geographical location information of the person providing support. The support department can also select the optimal support timing based on the geographical location information of the person providing support. The support department can also analyze the geographical location information of the person providing support and optimize the support method. In this way, the support department can select the optimal support method by considering the geographical location information of the person providing support. Some or all of the above processes in the support department may be performed using AI or not. For example, the support department can input the geographical location information of the person providing support into AI, and the AI ​​can analyze the information and select the optimal support method.

[0095] The support department can analyze the social media activity of the person providing support and provide relevant support. For example, the support department can select relevant support based on the person's social media activity. The support department can also analyze the person's social media activity and provide the most appropriate support. The support department can also optimize the order of support based on the person's social media activity. In this way, the support department can efficiently provide relevant support by analyzing the person's social media activity. Some or all of the above processes in the support department may be performed using AI or not. For example, the support department can input the person's social media activity data into AI, and the AI ​​can analyze the data and provide relevant support.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The cloud tool implementation support system can also include a feedback collection unit. This unit can collect feedback from users after the cloud tool has been implemented, and use it to improve the system. For example, the feedback collection unit can gather user feedback and problems from users after implementation, and identify areas for system improvement. The feedback collection unit can also analyze the collected feedback and incorporate it into future cloud tool implementations. This allows the cloud tool implementation support system to be continuously improved, providing more effective implementation support.

[0098] The cloud tool implementation support system can also include a training department. This training department can provide training to personnel after the cloud tool has been implemented. For example, the training department can teach personnel the basic and advanced uses of the cloud tool. The training department can also support the skill development of personnel through online courses and workshops. In this way, the cloud tool implementation support system can help improve the skills of personnel after implementation and promote the effective use of the cloud tool.

[0099] The cloud tool implementation support system can also include a risk assessment department. This department can evaluate the risks associated with cloud tool implementation and propose risk management strategies to the responsible personnel. For example, it can assess security and legal risks and propose risk mitigation measures. It can also evaluate operational risks after cloud tool implementation and communicate operational considerations to the responsible personnel. This allows the cloud tool implementation support system to strengthen risk management and support the safe and effective implementation of cloud tools.

[0100] The cloud tool implementation support system can also include a customization section. This customization section can customize the cloud tool to meet the specific needs of the user during implementation. For example, the customization section can adjust the cloud tool's functions to suit the user's work. The customization section can also modify the cloud tool's interface according to the user's requests. This allows the cloud tool implementation support system to respond flexibly to the user's needs and effectively facilitate the implementation of cloud tools.

[0101] The cloud tool implementation support system can also include a performance monitoring unit. This unit can monitor the performance of the cloud tool after its implementation and provide improvement suggestions to the responsible personnel. For example, the performance monitoring unit can monitor the usage of the cloud tool in real time and detect performance degradation. It can also analyze the causes of performance degradation and propose improvement measures. This allows the cloud tool implementation support system to optimize post-implementation performance and support the effective operation of the cloud tool.

[0102] The cloud tool implementation support system can also include an emotional feedback unit. This unit can regularly monitor the emotions of the person in charge after the cloud tool implementation and use this information to improve the system. For example, if the person in charge is experiencing stress, the emotional feedback unit can identify the cause and propose solutions. If the person in charge is satisfied, the emotional feedback unit can analyze the factors contributing to that satisfaction and share this information with other people in charge. This allows the cloud tool implementation support system to continuously improve based on the emotions of the people in charge, providing better implementation support.

[0103] The cloud tool implementation support system can also be equipped with an emotion analysis unit. This unit can analyze the emotions of the person in charge during the cloud tool implementation process in real time and provide appropriate support. For example, if the person in charge is feeling anxious, the emotion analysis unit can provide reassuring support. If the person in charge is excited, the emotion analysis unit can also leverage that energy to accelerate the implementation process. This allows the cloud tool implementation support system to provide support tailored to the person in charge's emotions, ensuring a smooth implementation process.

[0104] The cloud tool implementation support system can also be equipped with an emotion prediction unit. This unit can predict the emotions of the person in charge at each stage of the cloud tool implementation process and take proactive measures. For example, if the emotion prediction unit anticipates that the person in charge is likely to feel anxious in the initial stages of implementation, it can provide reassuring information in advance. If the emotion prediction unit anticipates that the person in charge is likely to feel fatigued in the middle stages of implementation, it can also suggest taking a break. In this way, the cloud tool implementation support system can predict the emotions of the person in charge and take appropriate action, thereby ensuring a smooth implementation process.

[0105] The cloud tool implementation support system can also include an emotion sharing section. This section allows the emotions of the person in charge during the cloud tool implementation process to be shared with other team members, enabling the entire team to provide support. For example, if the person in charge is feeling stressed, the emotion sharing section will share that information with other team members and request support. Similarly, if the person in charge is experiencing success, the emotion sharing section can share that information to boost the motivation of the entire team. This allows the cloud tool implementation support system to share the emotions of the person in charge with the entire team and to collaboratively advance the implementation process.

[0106] The cloud tool implementation support system can also include an emotional feedback loop. This loop continuously provides feedback on the emotions of the personnel involved in the cloud tool implementation process, helping to improve the system. For example, it can identify the causes of stress experienced by the personnel and implement corrective measures to prevent similar problems in subsequent implementation processes. It can also analyze the factors contributing to the personnel's satisfaction and reflect these findings in other implementation processes. As a result, the cloud tool implementation support system can continuously improve based on the emotions of the personnel involved, providing more effective implementation support.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The learning unit learns internal company information. Specifically, it learns internal company information such as business processes, approval processes for implementation, security / legal rules, and the current system configuration. The learning unit uses AI and machine learning algorithms to analyze and learn internal company information in detail. Step 2: The interviewing department conducts interviews with the person in charge based on the information learned by the learning department. Specifically, they ask questions to the person in charge to understand the necessary requirements. The interviewing department uses AI and natural language processing technology to analyze the person in charge's answers and extract the necessary requirements. Step 3: The proposal team proposes cloud tools based on the requirements obtained by the hearing team. Specifically, they compare cloud tools based on feasibility of what they want to achieve, legal / security considerations, and cost considerations. The proposal team uses AI to compare the functions and costs of cloud tools and proposes the most suitable one. Step 4: The support department assists with the implementation process of the cloud tools proposed by the proposal department. Specifically, they propose the necessary procedures and approval processes for implementation and support the person in charge to ensure a smooth implementation. The support department uses AI to monitor the progress of the implementation procedures and provide necessary support.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the learning unit, interviewing unit, proposal unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the learning unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes and learns company information in detail. The interviewing unit is implemented by the control unit 46A of the smart device 14, which asks questions to the person in charge and understands the necessary requirements. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which compares cloud tools and proposes the optimal cloud tool. The support unit is implemented by the control unit 46A of the smart device 14, which supports the implementation process. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the learning unit, interviewing unit, proposal unit, and support unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the learning unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes and learns company information in detail. The interviewing unit is implemented by the control unit 46A of the smart glasses 214, which asks questions to the person in charge and understands the necessary requirements. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which compares cloud tools and proposes the optimal cloud tool. The support unit is implemented by the control unit 46A of the smart glasses 214, which supports the implementation process. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the learning unit, interviewing unit, proposal unit, and support unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the learning unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes and learns company information in detail. The interviewing unit is implemented by the control unit 46A of the headset terminal 314, which asks questions to the person in charge and grasps the necessary requirements. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which compares cloud tools and proposes the optimal cloud tool. The support unit is implemented by the control unit 46A of the headset terminal 314, which supports the implementation process. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the learning unit, interviewing unit, proposal unit, and support unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the learning unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes and learns company information in detail. The interviewing unit is implemented by, for example, the control unit 46A of the robot 414, which asks questions to the person in charge and understands the necessary requirements. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which compares cloud tools and proposes the optimal cloud tool. The support unit is implemented by, for example, the control unit 46A of the robot 414, which supports the implementation process. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) The learning department, which is responsible for learning internal company information, Based on the information learned by the aforementioned learning unit, the interviewing unit conducts interviews with the person in charge, Based on the requirements obtained by the aforementioned hearing department, the proposal department proposes a cloud tool. The system includes a support unit that supports the implementation process of the cloud tools proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned learning unit, Learn internal company information such as business processes, approval processes for implementation, security / legal rules, and the current system configuration. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned hearing section is, We will interview the person in charge to understand the necessary requirements. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Compare cloud tools based on feasibility of achieving your goals, legal / security considerations, and cost. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned support unit is We propose the necessary procedures and approval processes for implementation and support the person in charge to ensure a smooth implementation process. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned learning unit, The system estimates the emotions of the person in charge and determines the priority of information to learn based on the estimated emotions of the person in charge. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned learning unit, During training, the learning algorithm is optimized by referencing past project data within the company. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned learning unit, During the learning process, we incorporate feedback from different departments within the company to adjust the learning content. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned learning unit, It estimates the emotions of the person in charge and adjusts the level of detail of the information learned based on the estimated emotions of the person in charge. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned learning unit, When learning, collect information while considering the geographical distribution within the company. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned learning unit, During the learning process, we analyze internal social media activity and learn relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned hearing section is, We estimate the emotions of the person in charge and adjust the timing of the interview based on the estimated emotions of the person in charge. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned hearing section is, During the interview, we will select the most appropriate questions by referring to the past statements made by the person in charge. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned hearing section is, During the interview, different question algorithms are applied depending on the position and job responsibilities of the person in charge. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned hearing section is, We estimate the emotions of the person in charge and adjust the interview method based on the estimated emotions of the person in charge. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned hearing section is, During the interview, the most suitable interview method will be selected, taking into account the geographical location of the person in charge. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned hearing section is, During the interview, we will analyze the social media activity of the person in charge and ask relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, We estimate the emotions of the person in charge and adjust the way the proposal is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the cloud tools. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When submitting a proposal, different proposal algorithms are applied depending on the category of the cloud tool. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, Estimate the emotions of the person in charge and adjust the length of the proposal based on the estimated emotions of the person in charge. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making proposals, prioritize them based on the timing of cloud tool implementation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting proposals, adjust the order of proposals based on the relevance of the cloud tools. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned support unit is The system estimates the emotions of the person in charge and adjusts the support method based on the estimated emotions of the person in charge. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned support unit is During support, we will refer to past support history to select the most appropriate support method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned support unit is When providing support, different support algorithms are applied depending on the support staff member's position and job responsibilities. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned support unit is The system estimates the emotions of the person in charge and determines the priority of support based on the estimated emotions of the person in charge. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned support unit is When providing support, the most suitable support method will be selected considering the geographical location of the support staff. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned support unit is When providing support, we analyze the social media activity of the person in charge to provide relevant support. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 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. The learning department, which is responsible for learning internal company information, Based on the information learned by the aforementioned learning unit, the interviewing unit conducts interviews with the person in charge, Based on the requirements obtained by the aforementioned hearing department, the proposal department proposes a cloud tool. The system includes a support unit that supports the implementation process of the cloud tools proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned learning unit, Learn internal company information such as business processes, approval processes for implementation, security / legal rules, and the current system configuration. The system according to feature 1.

3. The aforementioned hearing section is, We will interview the person in charge to understand the necessary requirements. The system according to feature 1.

4. The aforementioned proposal section is, Compare cloud tools based on feasibility of achieving your goals, legal / security considerations, and cost. The system according to feature 1.

5. The aforementioned support unit is We propose the necessary procedures and approval processes for implementation and support the person in charge to ensure a smooth implementation process. The system according to feature 1.

6. The aforementioned learning unit, The system estimates the emotions of the person in charge and determines the priority of information to learn based on the estimated emotions of the person in charge. The system according to feature 1.

7. The aforementioned learning unit, During training, the learning algorithm is optimized by referencing past project data within the company. The system according to feature 1.

8. The aforementioned learning unit, During the learning process, we incorporate feedback from different departments within the company to adjust the learning content. The system according to feature 1.

9. The aforementioned learning unit, It estimates the emotions of the person in charge and adjusts the level of detail of the information learned based on the estimated emotions of the person in charge. The system according to feature 1.

10. The aforementioned learning unit, When learning, collect information while considering the geographical distribution within the company. The system according to feature 1.

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