system

Abstract

The system according to this embodiment aims to efficiently solve the individual challenges of sales technical members. [Solution] The system according to the embodiment comprises a generation unit, a support unit, and a solution unit. The generation unit provides a generation AI chat service. The support unit provides one-on-one support based on the solutions provided by the generation unit. The solution unit solves the problems of the sales technical members supported by the support unit.

Description

Technical Field

[0004] ,

[0006] , , ,

[0005] , , , ,

[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 the chatbot's 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 conventional technology, sufficient support for efficiently solving the individual problems of sales technology members has not been provided, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently solve the individual problems of sales technology members.

Means for Solving the Problems

[0006] The system according to the embodiment includes a generation unit, a support unit, and a solution unit. The generation unit provides a generation AI chat service. The support unit provides one-on-one support based on the solutions provided by the generation unit. The solution unit solves the problems of sales technology members supported by the support unit.

Effects of the Invention

[0007] The system according to this embodiment can efficiently solve the individual challenges of sales technical members. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 enterprise management department shared service according to an embodiment of the present invention is a system that combines a generative AI chat service with the resources of management department staff to solve the challenges of each individual sales technical member on a one-on-one basis. This system realizes a transformation of the role of the management department in anticipation of the singularity and provides on-the-ground support by making maximum use of enterprise AI chatbots. First, a proof-of-concept (POC) project will be released within the SoftBank Group, and participants will be loaned smart glasses, smartwatches, or tablets equipped with AI specialized for SoftBank internal access information. This will allow even those with insufficient literacy to solve problems immediately through process support by applications that implement generative AI functionality. This mechanism is expected to contribute to maximizing business speed and contribute to organizational revitalization and create further value by including not only the provision of intelligence but also action support through collaboration. In addition, as a measure to monetize the resources of indirect departments, the aim is to transform indirect departments into throughput centers. Specifically, it consists of the following steps: First, sales technical members input their problems into the generative AI chat service. Next, the generative AI analyzes the problem and proposes the optimal solution. Furthermore, administrative staff will provide one-on-one support based on the proposed solutions. This process will enable the rapid resolution of challenges faced by sales technical members. The generated AI also has functions such as sentiment analysis, real-time risk management, schedule management, automatic task assignment, data analysis and suggestions for process optimization, provision of personalized learning content, and automatic updating and delivery of the knowledge base. This will improve employee engagement and operational efficiency. After proof-of-concept verification within the SoftBank Group, this initiative will be rolled out to all group companies and eventually offered as a service to domestic companies outside the SoftBank Group. The goal is to increase the productivity of the entire group by 30% annually. As a result, the shared administrative services for enterprise companies can quickly resolve challenges faced by sales technical members, revitalize organizations, and improve operational efficiency.

[0029] The enterprise management department shared service according to this embodiment comprises a generation unit, a support unit, and a resolution unit. The generation unit provides a generation AI chat service. The generation unit has functions such as text generation, dialogue generation, and problem solving. The generation unit uses generation AI to analyze issues entered by sales technical members and proposes optimal solutions. The generation unit uses, for example, text generation AI (e.g., LLM) to analyze the issues of sales technical members and generate solutions. The generation unit can also use multimodal generation AI to analyze the content of issues and propose solutions. The generation unit uses generation AI to have functions such as sentiment analysis, real-time risk management, schedule management, automatic task assignment, data analysis and proposals for process optimization, provision of personalized learning content, and automatic updating and provision of a knowledge base. The support unit provides one-on-one support based on the solutions provided by the generation unit. The support unit provides support through methods such as individual meetings, online chat, and telephone support. The support unit provides specific support to sales technical members based on the solutions proposed by the generation unit. The support department, for example, directly interacts with sales technical members and supports the implementation of solutions. The support department can also provide real-time support through online chat. The support department can also provide detailed explanations to sales technical members through telephone support. The solution department resolves the issues of sales technical members supported by the support department. The solution department implements the most suitable solution depending on the type of problem. The solution department resolves the issues of sales technical members based on the solutions proposed by the generation department. The solution department provides specific solutions to technical problems, for example. The solution department can also implement effective solutions to issues in sales activities. The solution department quickly resolves the issues of sales technical members and improves operational efficiency. As a result, the shared management service for enterprise companies according to this embodiment can quickly resolve the issues of sales technical members and achieve organizational revitalization and operational efficiency.

[0030] The generation unit provides a generation AI chat service. The generation unit has functions such as text generation, dialogue generation, and problem solving. Specifically, the generation unit uses text generation AI (e.g., LLM) to analyze issues entered by sales technical members and propose optimal solutions. LLM has learned from a large amount of text data and utilizes natural language processing technology to understand the entered issues and generate appropriate solutions. For example, if a sales technical member enters "I need to know how to conduct market research for a new product," the generation unit will use LLM to propose market research methods and specific steps. Furthermore, the generation unit can use multimodal generation AI to analyze different data formats, such as images and audio, in addition to text, and propose more comprehensive solutions. For example, if an issue regarding product design is entered, the generation unit will use image analysis technology to suggest design improvements. In addition, the generation unit performs sentiment analysis to understand the emotional state of sales technical members and provide more appropriate support. It also performs real-time risk management, schedule management, automatic task assignment, and data analysis and suggestions for process optimization, improving the work efficiency of sales technical members. For example, it monitors project progress in real time and immediately proposes countermeasures if risks arise. It also provides personalized learning content and automatically updates and delivers a knowledge base to support the skill development of sales technical members. This allows the generation department to respond to the diverse needs of sales technical members and support improved work efficiency and problem-solving.

[0031] The support department provides one-on-one support based on the solutions provided by the development department. Specifically, support is provided through methods such as individual meetings, online chat, and telephone support. The support department provides concrete support to sales technical members based on the solutions proposed by the development department. For example, when a sales technical member is implementing a new market strategy, the support department will formulate a specific action plan through individual meetings and support its implementation. Support can also be provided in real time through online chat. For example, if a sales technical member asks a question about a problem that arises during work, the support department will answer immediately and provide a solution. Furthermore, detailed explanations can be provided to sales technical members through telephone support. For example, if there is a question about how to use a new system, the support department will provide a detailed explanation by phone to help the sales technical member effectively utilize the system. Through direct dialogue with sales technical members, the support department supports the implementation of solutions and enables rapid problem resolution. In addition, the support department collects feedback from sales technical members and provides it to the development department to continuously improve the accuracy and effectiveness of the solutions. In this way, the support department can support sales technical members in solving their challenges and achieve operational efficiency and organizational revitalization.

[0032] The Solutions Department resolves the challenges faced by sales technical members supported by the Support Department. Specifically, it implements the most appropriate solution depending on the type of problem. Based on the solutions proposed by the Generation Department, the Solutions Department resolves the challenges faced by sales technical members. For example, it provides specific solutions to technical problems. If a sales technical member is having trouble implementing new software, the Solutions Department will configure the software, troubleshoot, and resolve the problem. It can also implement effective solutions to challenges in sales activities. For example, in the implementation of a new market strategy, the Solutions Department will develop a specific marketing plan and support its implementation. The Solutions Department utilizes its expertise and experience to quickly resolve the challenges faced by sales technical members and improve operational efficiency. Furthermore, the Solutions Department follows up after the implementation of the solution and takes measures to prevent the problem from recurring. For example, it evaluates the effectiveness of the solution and provides additional support as needed. The Solutions Department also documents the solution implementation process and adds it to the knowledge base so that other sales technical members can refer to it when they face similar problems. In this way, the Solutions Department can quickly and effectively resolve the challenges faced by sales technical members, achieving operational efficiency and organizational revitalization.

[0033] The Risk Management Department performs real-time risk management. The Risk Management Department manages risks by methods such as risk type, monitoring method, and countermeasures. The Risk Management Department manages risks in real time using generative AI. The Risk Management Department analyzes risk data and manages risks using, for example, text generation AI (e.g., LLM). The Risk Management Department can also analyze risk data and manage risks in real time using speech recognition technology. The Risk Management Department can also analyze risk data and manage risks in real time using facial recognition technology. The Risk Management Department is implemented using real-time risk management functions using an emotion engine or generative AI. This allows the Risk Management Department to grasp risks in detail in real time and take rapid action. Some or all of the above processes in the Risk Management Department may be performed using, for example, AI, or not using AI. For example, the Risk Management Department can input risk data into a generative AI and have the generative AI perform real-time risk management.

[0034] The Schedule Management Unit manages schedules. The Schedule Management Unit manages schedules using methods such as task prioritization, reminder functions, and progress management. The Schedule Management Unit manages schedules using generative AI. The Schedule Management Unit analyzes schedule data and manages schedules using, for example, text generation AI (e.g., LLM). The Schedule Management Unit can also analyze schedule data and manage schedules using speech recognition technology. The Schedule Management Unit can also analyze schedule data and manage schedules using facial expression recognition technology. The Schedule Management Unit is implemented using schedule management functions with an emotion engine or generative AI. This allows the Schedule Management Unit to grasp schedules in detail and perform tasks efficiently. Some or all of the above-described processes in the Schedule Management Unit may be performed using, for example, AI, or without AI. For example, the Schedule Management Unit can input schedule data into a generative AI and have the generative AI perform schedule management.

[0035] The task assignment unit performs automatic task assignment. The task assignment unit automatically assigns tasks using methods such as task type, assignment algorithm, and evaluation criteria. The task assignment unit automatically assigns tasks using generative AI. The task assignment unit analyzes task data and automatically assigns tasks using, for example, text generation AI (e.g., LLM). The task assignment unit can also analyze task data and automatically assign tasks using speech recognition technology. The task assignment unit can also analyze task data and automatically assign tasks using facial recognition technology. The task assignment unit is implemented using an automatic task assignment function with an emotion engine or generative AI. This allows the task assignment unit to understand tasks in detail and perform work efficiently. Some or all of the above-described processes in the task assignment unit may be performed using, for example, AI, or not using AI. For example, the task assignment unit can input task data into a generative AI and have the generative AI perform automatic task assignment.

[0036] The Data Analysis Department performs data analysis and makes recommendations for process optimization. The Data Analysis Department optimizes processes through methods such as data analysis techniques, proposed solutions, and evaluation criteria. The Data Analysis Department uses generative AI to analyze data and make recommendations for process optimization. For example, the Data Analysis Department uses text generation AI (e.g., LLM) to analyze data and make recommendations for process optimization. The Data Analysis Department can also use speech recognition technology to analyze data and make recommendations for process optimization. The Data Analysis Department can also use facial expression recognition technology to analyze data and make recommendations for process optimization. The Data Analysis Department is implemented using data analysis functions for process optimization, such as an emotion engine or generative AI. This allows the Data Analysis Department to gain a detailed understanding of the data and optimize business processes. Some or all of the above-described processes in the Data Analysis Department may be performed using AI, or not. For example, the Data Analysis Department can input data into a generative AI and have the generative AI perform data analysis for process optimization.

[0037] The learning delivery unit provides personalized learning content. The learning delivery unit provides learning content based, for example, on the learner's level, interests, and learning goals. The learning delivery unit uses generative AI to provide learning content best suited to the learner. The learning delivery unit uses, for example, text generation AI (e.g., LLM) to analyze learner data and provide personalized learning content. The learning delivery unit can also use speech recognition technology to analyze learner data and provide personalized learning content. The learning delivery unit can also use facial recognition technology to analyze learner data and provide personalized learning content. The learning delivery unit is implemented using a personalized learning content delivery function that utilizes an emotion engine or generative AI. This allows the learning delivery unit to gain a detailed understanding of learner data and provide individually optimized learning content. Some or all of the above-described processes in the learning delivery unit may be performed using, for example, AI, or not using AI. For example, the learning delivery unit can input learner data into a generative AI and have the generative AI deliver personalized learning content.

[0038] The Knowledge Provision Department automatically updates and provides the knowledge base. The Knowledge Provision Department automatically updates the knowledge base based on criteria such as update frequency, information reliability, and data sources. The Knowledge Provision Department uses generative AI to automatically update the knowledge base and provide the latest information. The Knowledge Provision Department uses text generation AI (e.g., LLM) to analyze and automatically update the data in the knowledge base. The Knowledge Provision Department can also use speech recognition technology to analyze and automatically update the data in the knowledge base. The Knowledge Provision Department can also use facial recognition technology to analyze and automatically update the data in the knowledge base. The Knowledge Provision Department's automatic knowledge base update function is implemented using an emotion engine or generative AI. This allows the Knowledge Provision Department to have a detailed understanding of the knowledge base and provide the latest information. Some or all of the above-described processes in the Knowledge Provision Department may be performed using AI, or not. For example, the knowledge provision department can input data from the knowledge base into a generating AI and have the generating AI perform automatic updates.

[0039] The lending unit lends smart glasses, smartwatches, and tablets. The lending unit lends devices based on, for example, device specifications, usage scenarios, and delivery methods. The lending unit uses generative AI to select and lend the optimal device. The lending unit uses, for example, text generation AI (e.g., LLM) to analyze user data and select the optimal device. The lending unit can also use speech recognition technology to analyze user data and select the optimal device. The lending unit can also use facial recognition technology to analyze user data and select the optimal device. The lending unit implements a device selection function using an emotion engine or generative AI. This allows the lending unit to understand user data in detail and lend the optimal device. Some or all of the above processing in the lending unit may be performed using, for example, AI, or not using AI. For example, the lending unit can input user data into a generative AI and have the generative AI perform device selection.

[0040] The generation unit generates the optimal solution by referring to the user's past solution usage history during generation. The generation unit generates solutions for similar problems based on solutions the user has used in the past, for example. The generation unit proposes the most effective solution based on the user's past solution usage history, for example. The generation unit analyzes the user's past solution usage history and generates the optimal solution, for example. In this way, the generation unit can provide the optimal solution based on past usage history. Some or all of the above processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's past solution usage history into a generation AI and have the generation AI perform the generation of the optimal solution.

[0041] The generation unit adjusts the priority of solutions based on the user's current work situation during generation. For example, the generation unit analyzes the user's current work situation and generates solutions prioritizing the most urgent issues. For example, the generation unit considers the user's workload and generates solutions that alleviate the burden. For example, the generation unit provides solutions at the optimal time based on the user's work progress. This allows the generation unit to provide solutions with priorities that correspond to the current work situation. Some or all of the above processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's current work situation into the generation AI and have the generation AI adjust the priority of solutions.

[0042] The generation unit generates highly relevant solutions while considering the user's geographical location information. For example, the generation unit proposes the optimal solution based on the user's current location. For example, the generation unit generates solutions for region-specific issues while considering the user's geographical location information. For example, the generation unit proposes solutions that utilize nearby resources based on the user's location information. In this way, the generation unit can provide solutions that take geographical location information into account. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's geographical location information into a generation AI and have the generation AI perform the generation of highly relevant solutions.

[0043] The generation unit analyzes the user's social media activity during generation and generates relevant solutions. For example, the generation unit analyzes the user's social media activity and proposes relevant solutions. For example, the generation unit generates the optimal solution based on the user's social media posts. For example, the generation unit proposes solutions aligned with trends, taking into account the user's social media activity. In this way, the generation unit can provide solutions based on social media activity. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's social media activity into a generation AI and have the generation AI perform the generation of relevant solutions.

[0044] The support unit selects the optimal support method by referring to the user's past support history when providing support. For example, the support unit proposes the optimal support method based on the user's past support history. For example, the support unit selects the most effective support method from the user's past support history. For example, the support unit analyzes the user's past support history and provides the optimal support method. In this way, the support unit can provide the optimal support method based on past support history. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's past support history into a generating AI and have the generating AI select the optimal support method.

[0045] The support department adjusts the priority of support based on the user's current work situation when providing assistance. For example, the support department analyzes the user's current work situation and prioritizes providing the most urgent support. For example, the support department considers the user's workload and provides support methods that reduce the burden. For example, the support department provides support at the optimal timing based on the user's work progress. This allows the support department to provide support with priorities according to the current work situation. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input the user's current work situation into a generating AI and have the generating AI perform the adjustment of support priorities.

[0046] The support unit provides highly relevant support by considering the user's geographical location information during the support process. For example, the support unit proposes the optimal support method based on the user's current location. For example, the support unit provides support for region-specific issues by considering the user's geographical location information. For example, the support unit proposes support methods that utilize nearby resources based on the user's location information. In this way, the support unit can provide support that takes geographical location information into consideration. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing highly relevant support.

[0047] The support department analyzes the user's social media activity and provides relevant support during the support process. For example, the support department analyzes the user's social media activity and proposes relevant support methods. For example, the support department provides the optimal support method based on the user's social media posts. For example, the support department proposes support methods that align with trends, taking into account the user's social media activity. This allows the support department to provide support based on social media activity. Some or all of the above processes in the support department may be performed using AI, for example, or without AI. For example, the support department can input the user's social media activity into a generating AI and have the generating AI perform the provision of relevant support.

[0048] The resolution unit, upon resolving a problem, selects the optimal solution by referring to the user's past resolution history. The resolution unit, for example, proposes solutions for similar problems based on solutions the user has used in the past. The resolution unit, for example, selects the most effective solution from the user's past resolution history. The resolution unit, for example, analyzes the user's past resolution history and provides the optimal solution. In this way, the resolution unit can provide the optimal solution based on past resolution history. Some or all of the above processes in the resolution unit may be performed using AI, for example, or without AI. For example, the resolution unit can input the user's past resolution history into a generating AI and have the generating AI select the optimal solution.

[0049] The resolution unit adjusts the priority of solutions based on the user's current work situation when resolving a problem. For example, the resolution unit analyzes the user's current work situation and prioritizes providing solutions for the most urgent issues. For example, the resolution unit considers the user's workload and provides solutions that reduce the burden. For example, the resolution unit provides solutions at the optimal timing based on the user's work progress. This allows the resolution unit to provide solutions with priorities according to the current work situation. Some or all of the above processes in the resolution unit may be performed using AI, for example, or without AI. For example, the resolution unit can input the user's current work situation into a generating AI and have the generating AI perform the adjustment of the resolution priorities.

[0050] The solution unit provides highly relevant solutions while considering the user's geographical location information. For example, the solution unit proposes the optimal solution based on the user's current location. For example, the solution unit provides solutions for region-specific issues while considering the user's geographical location information. For example, the solution unit proposes solutions that utilize nearby resources based on the user's location information. In this way, the solution unit can provide solutions that take geographical location information into consideration. Some or all of the above processing in the solution unit may be performed using AI, for example, or without AI. For example, the solution unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing highly relevant solutions.

[0051] The solution unit analyzes the user's social media activity and provides relevant solutions during the solution process. For example, the solution unit analyzes the user's social media activity and proposes relevant solutions. For example, the solution unit provides the optimal solution based on the user's social media posts. For example, the solution unit proposes solutions aligned with trends, taking into account the user's social media activity. In this way, the solution unit can provide solutions based on social media activity. Some or all of the above-described processes in the solution unit may be performed using AI, for example, or without AI. For example, the solution unit can input the user's social media activity into a generating AI and have the generating AI perform the task of providing relevant solutions.

[0052] The sentiment analysis unit selects the optimal analysis method by referring to the user's past emotional data during sentiment analysis. For example, the sentiment analysis unit proposes the optimal sentiment analysis method based on the user's past emotional data. For example, the sentiment analysis unit selects the most effective sentiment analysis method from the user's past emotional data. For example, the sentiment analysis unit analyzes the user's past emotional data and provides the optimal sentiment analysis method. In this way, the sentiment analysis unit can provide the optimal sentiment analysis method based on past emotional data. Some or all of the above processes in the sentiment analysis unit may be performed using AI, for example, or without AI. For example, the sentiment analysis unit can input the user's past emotional data into a generating AI and have the generating AI select the optimal sentiment analysis method.

[0053] The sentiment analysis unit analyzes highly relevant sentiment data while considering the user's geographical location information during sentiment analysis. For example, the sentiment analysis unit proposes the optimal sentiment analysis method based on the user's current location. For example, the sentiment analysis unit analyzes region-specific sentiment data while considering the user's geographical location information. For example, the sentiment analysis unit proposes a sentiment analysis method that utilizes nearby resources based on the user's location information. In this way, the sentiment analysis unit can provide sentiment data that takes geographical location information into account. Some or all of the above processing in the sentiment analysis unit may be performed using AI, for example, or without AI. For example, the sentiment analysis unit can input the user's geographical location information into a generating AI and have the generating AI perform the analysis of highly relevant sentiment data.

[0054] The risk management department selects the optimal risk management method by referring to the user's past risk data during risk management. For example, the risk management department proposes the optimal risk management method based on the user's past risk data. For example, the risk management department selects the most effective risk management method from the user's past risk data. For example, the risk management department analyzes the user's past risk data and provides the optimal risk management method. In this way, the risk management department can provide the optimal risk management method based on past risk data. Some or all of the above processes in the risk management department may be performed using AI, for example, or without AI. For example, the risk management department can input the user's past risk data into a generating AI and have the generating AI select the optimal risk management method.

[0055] The Risk Management Department manages highly relevant risk data while considering the user's geographical location information during risk management. For example, the Risk Management Department proposes the optimal risk management method based on the user's current location. For example, the Risk Management Department manages region-specific risk data while considering the user's geographical location information. For example, the Risk Management Department proposes a risk management method that utilizes nearby resources based on the user's location information. In this way, the Risk Management Department can provide risk data that takes geographical location information into account. Some or all of the above processes in the Risk Management Department may be performed using AI, for example, or without AI. For example, the Risk Management Department can input the user's geographical location information into a generating AI and have the generating AI perform the management of highly relevant risk data.

[0056] The schedule management unit selects the optimal management method by referring to the user's past schedule data when managing schedules. For example, the schedule management unit proposes the optimal schedule management method based on the user's past schedule data. For example, the schedule management unit selects the most effective schedule management method from the user's past schedule data. For example, the schedule management unit analyzes the user's past schedule data and provides the optimal schedule management method. In this way, the schedule management unit can provide the optimal schedule management method based on past schedule data. Some or all of the above processes in the schedule management unit may be performed using AI, for example, or without AI. For example, the schedule management unit can input the user's past schedule data into a generating AI and have the generating AI select the optimal schedule management method.

[0057] The schedule management unit manages highly relevant schedule data while considering the user's geographical location information. For example, the schedule management unit proposes the optimal schedule management method based on the user's current location. For example, the schedule management unit manages region-specific schedule data while considering the user's geographical location information. For example, the schedule management unit proposes a schedule management method that utilizes nearby resources based on the user's location information. In this way, the schedule management unit can provide schedule data that takes geographical location information into account. Some or all of the above processing in the schedule management unit may be performed using AI, for example, or without AI. For example, the schedule management unit can input the user's geographical location information into a generating AI and have the generating AI perform the management of highly relevant schedule data.

[0058] The task assignment unit selects the optimal assignment method by referring to the user's past task data when assigning tasks. The task assignment unit proposes the optimal task assignment method based on the user's past task data, for example. The task assignment unit selects the most effective task assignment method from the user's past task data, for example. The task assignment unit analyzes the user's past task data and provides the optimal task assignment method, for example. In this way, the task assignment unit can provide the optimal task assignment method based on past task data. Some or all of the above processes in the task assignment unit may be performed using AI, for example, or without AI. For example, the task assignment unit can input the user's past task data into a generating AI and have the generating AI select the optimal task assignment method.

[0059] The task assignment unit assigns highly relevant task data to users while considering their geographical location information. The task assignment unit proposes an optimal task assignment method based on the user's current location, for example. The task assignment unit assigns region-specific task data while considering the user's geographical location information, for example. The task assignment unit proposes a task assignment method that utilizes nearby resources based on the user's location information, for example. This allows the task assignment unit to provide task data that takes geographical location information into account. Some or all of the above-described processes in the task assignment unit may be performed using AI, for example, or without AI. For example, the task assignment unit can input the user's geographical location information into a generating AI and have the generating AI perform the assignment of highly relevant task data.

[0060] The data analysis department selects the optimal analysis method by referring to the user's past data during data analysis. For example, the data analysis department proposes the optimal data analysis method based on the user's past data. For example, the data analysis department selects the most effective data analysis method from the user's past data. For example, the data analysis department analyzes the user's past data and provides the optimal data analysis method. In this way, the data analysis department can provide the optimal data analysis method based on past data. Some or all of the above processes in the data analysis department may be performed using AI, for example, or without AI. For example, the data analysis department can input the user's past data into a generating AI and have the generating AI perform the selection of the optimal data analysis method.

[0061] The data analysis department analyzes highly relevant data while considering the user's geographical location information. For example, the data analysis department proposes the optimal data analysis method based on the user's current location. For example, the data analysis department analyzes region-specific data while considering the user's geographical location information. For example, the data analysis department proposes a data analysis method that utilizes nearby resources based on the user's location information. In this way, the data analysis department can provide data that takes geographical location information into account. Some or all of the above processes in the data analysis department may be performed using AI, for example, or without AI. For example, the data analysis department can input the user's geographical location information into a generating AI and have the generating AI perform the analysis of highly relevant data.

[0062] The learning delivery unit provides optimal content by referring to the user's past learning data when providing learning. For example, the learning delivery unit proposes optimal learning content based on the user's past learning data. For example, the learning delivery unit selects the most effective learning content from the user's past learning data. For example, the learning delivery unit analyzes the user's past learning data and provides optimal learning content. In this way, the learning delivery unit can provide optimal learning content based on past learning data. Some or all of the above processes in the learning delivery unit may be performed using AI, for example, or without AI. For example, the learning delivery unit can input the user's past learning data into a generating AI and have the generating AI perform the task of providing optimal learning content.

[0063] The learning delivery unit provides highly relevant content while considering the user's geographical location information. For example, the learning delivery unit suggests optimal learning content based on the user's current location. For example, the learning delivery unit provides region-specific learning content while considering the user's geographical location information. For example, the learning delivery unit suggests learning content that utilizes nearby resources based on the user's location information. In this way, the learning delivery unit can provide learning content that takes geographical location information into consideration. Some or all of the above processing in the learning delivery unit may be performed using AI, for example, or without AI. For example, the learning delivery unit can input the user's geographical location information into a generating AI and have the generating AI execute the provision of highly relevant learning content.

[0064] The knowledge provision unit selects the optimal provision method by referring to the user's past knowledge data when providing knowledge. For example, the knowledge provision unit proposes the optimal knowledge provision method based on the user's past knowledge data. For example, the knowledge provision unit selects the most effective knowledge provision method from the user's past knowledge data. For example, the knowledge provision unit analyzes the user's past knowledge data and provides the optimal knowledge provision method. In this way, the knowledge provision unit can provide the optimal knowledge provision method based on past knowledge data. Some or all of the above processes in the knowledge provision unit may be performed using AI, for example, or without AI. For example, the knowledge provision unit can input the user's past knowledge data into a generating AI and have the generating AI select the optimal knowledge provision method.

[0065] The knowledge provision unit provides highly relevant knowledge data while considering the user's geographical location information. For example, the knowledge provision unit proposes the optimal knowledge provision method based on the user's current location. For example, the knowledge provision unit provides region-specific knowledge data while considering the user's geographical location information. For example, the knowledge provision unit proposes a knowledge provision method that utilizes nearby resources based on the user's location information. In this way, the knowledge provision unit can provide knowledge data that takes geographical location information into consideration. Some or all of the above processing in the knowledge provision unit may be performed using AI, for example, or without AI. For example, the knowledge provision unit can input the user's geographical location information into a generating AI and have the generating AI perform the provision of highly relevant knowledge data.

[0066] The lending unit selects the optimal device at the time of lending by referring to the user's past device usage history. The lending unit proposes the optimal device based on the user's past device usage history. The lending unit selects the most effective device from the user's past device usage history. The lending unit analyzes the user's past device usage history and provides the optimal device. In this way, the lending unit can provide the optimal device based on past device usage history. Some or all of the above processes in the lending unit may be performed using AI, for example, or without AI. For example, the lending unit can input the user's past device usage history into a generating AI and have the generating AI perform the selection of the optimal device.

[0067] The lending unit lends highly relevant devices, taking into account the user's geographical location information at the time of lending. For example, the lending unit suggests the optimal device based on the user's current location. For example, the lending unit lends region-specific devices, taking into account the user's geographical location information. For example, the lending unit suggests devices that utilize nearby resources based on the user's location information. In this way, the lending unit can provide devices that take geographical location information into account. Some or all of the above processing in the lending unit may be performed using AI, for example, or without AI. For example, the lending unit can input the user's geographical location information into a generating AI and have the generating AI perform the lending of highly relevant devices.

[0068] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0069] The generation unit can also generate the optimal solution by referring to the user's past solution usage history. For example, it can generate solutions for similar problems based on solutions the user has used in the past. It can also suggest the most effective solution based on the user's past solution usage history. Furthermore, it can analyze the user's past solution usage history and generate the optimal solution. In this way, the generation unit can provide the optimal solution based on past usage history.

[0070] The solution unit can also adjust the priority of solutions based on the user's current work situation. For example, it can analyze the user's current work situation and prioritize generating solutions for the most urgent issues. It can also consider the user's workload and generate solutions that reduce the burden. Furthermore, it can provide solutions at the optimal time based on the user's work progress. In this way, the solution unit can provide solutions with priorities that match the current work situation.

[0071] The risk management department can also select the optimal risk management method by referring to the user's past risk data. For example, it can propose the optimal risk management method based on the user's past risk data. It can also select the most effective risk management method from the user's past risk data. Furthermore, it can analyze the user's past risk data and provide the optimal risk management method. In this way, the risk management department can provide the optimal risk management method based on past risk data.

[0072] The task assignment unit can also select the optimal assignment method by referring to the user's past task data. For example, it can propose the optimal task assignment method based on the user's past task data. It can also select the most effective task assignment method from the user's past task data. Furthermore, it can analyze the user's past task data and provide the optimal task assignment method. In this way, the task assignment unit can provide the optimal task assignment method based on past task data.

[0073] The learning delivery unit can also provide optimal content by referring to the user's past learning data. For example, it can suggest the most suitable learning content based on the user's past learning data. It can also select the most effective learning content from the user's past learning data. Furthermore, it can analyze the user's past learning data and provide the most suitable learning content. In this way, the learning delivery unit can provide optimal learning content based on past learning data.

[0074] The following briefly describes the processing flow for example form 1.

[0075] Step 1: The generation unit provides a generation AI chat service. The generation unit has functions such as text generation, dialogue generation, and problem solving, and analyzes issues entered by sales technical members and proposes the optimal solution. For example, it uses text generation AI (LLM) and multimodal generation AI to analyze the content of the issue and propose a solution. It also has functions such as sentiment analysis, real-time risk management, schedule management, automatic task assignment, data analysis and proposals for process optimization, provision of personalized learning content, and automatic updating and provision of the knowledge base. Step 2: The support team provides one-on-one support based on the solutions provided by the generation team. The support team provides specific assistance to sales technical members through methods such as individual meetings, online chat, and telephone support. For example, they directly interact with sales technical members and support the implementation of solutions. They can also provide real-time support through online chat and detailed explanations through telephone support. Step 3: The Solution Department resolves the challenges faced by sales technical members, supported by the Support Department. The Solution Department implements the most suitable solution according to the type of problem, resolving the challenges faced by sales technical members based on the solutions proposed by the Generation Department. For example, it provides specific solutions to technical problems and implements effective solutions to challenges in sales activities. This allows for the rapid resolution of challenges faced by sales technical members and improves operational efficiency.

[0076] (Example of form 2) The enterprise management department shared service according to an embodiment of the present invention is a system that combines a generative AI chat service with the resources of management department staff to solve the challenges of each individual sales technical member on a one-on-one basis. This system realizes a transformation of the role of the management department in anticipation of the singularity and provides on-the-ground support by making maximum use of enterprise AI chatbots. First, a proof-of-concept (POC) project will be released within the SoftBank Group, and participants will be loaned smart glasses, smartwatches, or tablets equipped with AI specialized for SoftBank internal access information. This will allow even those with insufficient literacy to solve problems immediately through process support by applications that implement generative AI functionality. This mechanism is expected to contribute to maximizing business speed and contribute to organizational revitalization and create further value by including not only the provision of intelligence but also action support through collaboration. In addition, as a measure to monetize the resources of indirect departments, the aim is to transform indirect departments into throughput centers. Specifically, it consists of the following steps: First, sales technical members input their problems into the generative AI chat service. Next, the generative AI analyzes the problem and proposes the optimal solution. Furthermore, administrative staff will provide one-on-one support based on the proposed solutions. This process will enable the rapid resolution of challenges faced by sales technical members. The generated AI also has functions such as sentiment analysis, real-time risk management, schedule management, automatic task assignment, data analysis and suggestions for process optimization, provision of personalized learning content, and automatic updating and delivery of the knowledge base. This will improve employee engagement and operational efficiency. After proof-of-concept verification within the SoftBank Group, this initiative will be rolled out to all group companies and eventually offered as a service to domestic companies outside the SoftBank Group. The goal is to increase the productivity of the entire group by 30% annually. As a result, the shared administrative services for enterprise companies can quickly resolve challenges faced by sales technical members, revitalize organizations, and improve operational efficiency.

[0077] The enterprise management department shared service according to this embodiment comprises a generation unit, a support unit, and a resolution unit. The generation unit provides a generation AI chat service. The generation unit has functions such as text generation, dialogue generation, and problem solving. The generation unit uses generation AI to analyze issues entered by sales technical members and proposes optimal solutions. The generation unit uses, for example, text generation AI (e.g., LLM) to analyze the issues of sales technical members and generate solutions. The generation unit can also use multimodal generation AI to analyze the content of issues and propose solutions. The generation unit uses generation AI to have functions such as sentiment analysis, real-time risk management, schedule management, automatic task assignment, data analysis and proposals for process optimization, provision of personalized learning content, and automatic updating and provision of a knowledge base. The support unit provides one-on-one support based on the solutions provided by the generation unit. The support unit provides support through methods such as individual meetings, online chat, and telephone support. The support unit provides specific support to sales technical members based on the solutions proposed by the generation unit. The support department, for example, directly interacts with sales technical members and supports the implementation of solutions. The support department can also provide real-time support through online chat. The support department can also provide detailed explanations to sales technical members through telephone support. The solution department resolves the issues of sales technical members supported by the support department. The solution department implements the most suitable solution depending on the type of problem. The solution department resolves the issues of sales technical members based on the solutions proposed by the generation department. The solution department provides specific solutions to technical problems, for example. The solution department can also implement effective solutions to issues in sales activities. The solution department quickly resolves the issues of sales technical members and improves operational efficiency. As a result, the shared management service for enterprise companies according to this embodiment can quickly resolve the issues of sales technical members and achieve organizational revitalization and operational efficiency.

[0078] The generation unit provides a generation AI chat service. The generation unit has functions such as text generation, dialogue generation, and problem solving. Specifically, the generation unit uses text generation AI (e.g., LLM) to analyze issues entered by sales technical members and propose optimal solutions. LLM has learned from a large amount of text data and utilizes natural language processing technology to understand the entered issues and generate appropriate solutions. For example, if a sales technical member enters "I need to know how to conduct market research for a new product," the generation unit will use LLM to propose market research methods and specific steps. Furthermore, the generation unit can use multimodal generation AI to analyze different data formats, such as images and audio, in addition to text, and propose more comprehensive solutions. For example, if an issue regarding product design is entered, the generation unit will use image analysis technology to suggest design improvements. In addition, the generation unit performs sentiment analysis to understand the emotional state of sales technical members and provide more appropriate support. It also performs real-time risk management, schedule management, automatic task assignment, and data analysis and suggestions for process optimization, improving the work efficiency of sales technical members. For example, it monitors project progress in real time and immediately proposes countermeasures if risks arise. It also provides personalized learning content and automatically updates and delivers a knowledge base to support the skill development of sales technical members. This allows the generation department to respond to the diverse needs of sales technical members and support improved work efficiency and problem-solving.

[0079] The support department provides one-on-one support based on the solutions provided by the development department. Specifically, support is provided through methods such as individual meetings, online chat, and telephone support. The support department provides concrete support to sales technical members based on the solutions proposed by the development department. For example, when a sales technical member is implementing a new market strategy, the support department will formulate a specific action plan through individual meetings and support its implementation. Support can also be provided in real time through online chat. For example, if a sales technical member asks a question about a problem that arises during work, the support department will answer immediately and provide a solution. Furthermore, detailed explanations can be provided to sales technical members through telephone support. For example, if there is a question about how to use a new system, the support department will provide a detailed explanation by phone to help the sales technical member effectively utilize the system. Through direct dialogue with sales technical members, the support department supports the implementation of solutions and enables rapid problem resolution. In addition, the support department collects feedback from sales technical members and provides it to the development department to continuously improve the accuracy and effectiveness of the solutions. In this way, the support department can support sales technical members in solving their challenges and achieve operational efficiency and organizational revitalization.

[0080] The Solutions Department resolves the challenges faced by sales technical members supported by the Support Department. Specifically, it implements the most appropriate solution depending on the type of problem. Based on the solutions proposed by the Generation Department, the Solutions Department resolves the challenges faced by sales technical members. For example, it provides specific solutions to technical problems. If a sales technical member is having trouble implementing new software, the Solutions Department will configure the software, troubleshoot, and resolve the problem. It can also implement effective solutions to challenges in sales activities. For example, in the implementation of a new market strategy, the Solutions Department will develop a specific marketing plan and support its implementation. The Solutions Department utilizes its expertise and experience to quickly resolve the challenges faced by sales technical members and improve operational efficiency. Furthermore, the Solutions Department follows up after the implementation of the solution and takes measures to prevent the problem from recurring. For example, it evaluates the effectiveness of the solution and provides additional support as needed. The Solutions Department also documents the solution implementation process and adds it to the knowledge base so that other sales technical members can refer to it when they face similar problems. In this way, the Solutions Department can quickly and effectively resolve the challenges faced by sales technical members, achieving operational efficiency and organizational revitalization.

[0081] The emotion analysis unit performs emotion analysis. The emotion analysis unit analyzes emotions using methods such as text analysis, voice analysis, and facial recognition. The emotion analysis unit estimates the user's emotions using generative AI. For example, the emotion analysis unit analyzes the user's text data using text generation AI (e.g., LLM) and estimates emotions. The emotion analysis unit can also analyze the user's voice data using voice recognition technology and estimate emotions. The emotion analysis unit can also analyze the user's facial data using facial recognition technology and estimate emotions. The emotion analysis unit is implemented using emotion estimation functions with emotion engines or generative AI. This allows the emotion analysis unit to grasp the user's emotions in detail and provide more appropriate support. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input the user's text data into a generative AI and have the generative AI perform emotion estimation.

[0082] The Risk Management Department performs real-time risk management. The Risk Management Department manages risks by methods such as risk type, monitoring method, and countermeasures. The Risk Management Department manages risks in real time using generative AI. The Risk Management Department analyzes risk data and manages risks using, for example, text generation AI (e.g., LLM). The Risk Management Department can also analyze risk data and manage risks in real time using speech recognition technology. The Risk Management Department can also analyze risk data and manage risks in real time using facial recognition technology. The Risk Management Department is implemented using real-time risk management functions using an emotion engine or generative AI. This allows the Risk Management Department to grasp risks in detail in real time and take rapid action. Some or all of the above processes in the Risk Management Department may be performed using, for example, AI, or not using AI. For example, the Risk Management Department can input risk data into a generative AI and have the generative AI perform real-time risk management.

[0083] The Schedule Management Unit manages schedules. The Schedule Management Unit manages schedules using methods such as task prioritization, reminder functions, and progress management. The Schedule Management Unit manages schedules using generative AI. The Schedule Management Unit analyzes schedule data and manages schedules using, for example, text generation AI (e.g., LLM). The Schedule Management Unit can also analyze schedule data and manage schedules using speech recognition technology. The Schedule Management Unit can also analyze schedule data and manage schedules using facial expression recognition technology. The Schedule Management Unit is implemented using schedule management functions with an emotion engine or generative AI. This allows the Schedule Management Unit to grasp schedules in detail and perform tasks efficiently. Some or all of the above-described processes in the Schedule Management Unit may be performed using, for example, AI, or without AI. For example, the Schedule Management Unit can input schedule data into a generative AI and have the generative AI perform schedule management.

[0084] The task assignment unit performs automatic task assignment. The task assignment unit automatically assigns tasks using methods such as task type, assignment algorithm, and evaluation criteria. The task assignment unit automatically assigns tasks using generative AI. The task assignment unit analyzes task data and automatically assigns tasks using, for example, text generation AI (e.g., LLM). The task assignment unit can also analyze task data and automatically assign tasks using speech recognition technology. The task assignment unit can also analyze task data and automatically assign tasks using facial recognition technology. The task assignment unit is implemented using an automatic task assignment function with an emotion engine or generative AI. This allows the task assignment unit to understand tasks in detail and perform work efficiently. Some or all of the above-described processes in the task assignment unit may be performed using, for example, AI, or not using AI. For example, the task assignment unit can input task data into a generative AI and have the generative AI perform automatic task assignment.

[0085] The Data Analysis Department performs data analysis and makes recommendations for process optimization. The Data Analysis Department optimizes processes through methods such as data analysis techniques, proposed solutions, and evaluation criteria. The Data Analysis Department uses generative AI to analyze data and make recommendations for process optimization. For example, the Data Analysis Department uses text generation AI (e.g., LLM) to analyze data and make recommendations for process optimization. The Data Analysis Department can also use speech recognition technology to analyze data and make recommendations for process optimization. The Data Analysis Department can also use facial expression recognition technology to analyze data and make recommendations for process optimization. The Data Analysis Department is implemented using data analysis functions for process optimization, such as an emotion engine or generative AI. This allows the Data Analysis Department to gain a detailed understanding of the data and optimize business processes. Some or all of the above-described processes in the Data Analysis Department may be performed using AI, or not. For example, the Data Analysis Department can input data into a generative AI and have the generative AI perform data analysis for process optimization.

[0086] The learning delivery unit provides personalized learning content. The learning delivery unit provides learning content based, for example, on the learner's level, interests, and learning goals. The learning delivery unit uses generative AI to provide learning content best suited to the learner. The learning delivery unit uses, for example, text generation AI (e.g., LLM) to analyze learner data and provide personalized learning content. The learning delivery unit can also use speech recognition technology to analyze learner data and provide personalized learning content. The learning delivery unit can also use facial recognition technology to analyze learner data and provide personalized learning content. The learning delivery unit is implemented using a personalized learning content delivery function that utilizes an emotion engine or generative AI. This allows the learning delivery unit to gain a detailed understanding of learner data and provide individually optimized learning content. Some or all of the above-described processes in the learning delivery unit may be performed using, for example, AI, or not using AI. For example, the learning delivery unit can input learner data into a generative AI and have the generative AI deliver personalized learning content.

[0087] The Knowledge Provision Department automatically updates and provides the knowledge base. The Knowledge Provision Department automatically updates the knowledge base based on criteria such as update frequency, information reliability, and data sources. The Knowledge Provision Department uses generative AI to automatically update the knowledge base and provide the latest information. The Knowledge Provision Department uses text generation AI (e.g., LLM) to analyze and automatically update the data in the knowledge base. The Knowledge Provision Department can also use speech recognition technology to analyze and automatically update the data in the knowledge base. The Knowledge Provision Department can also use facial recognition technology to analyze and automatically update the data in the knowledge base. The Knowledge Provision Department's automatic knowledge base update function is implemented using an emotion engine or generative AI. This allows the Knowledge Provision Department to have a detailed understanding of the knowledge base and provide the latest information. Some or all of the above-described processes in the Knowledge Provision Department may be performed using AI, or not. For example, the knowledge provision department can input data from the knowledge base into a generating AI and have the generating AI perform automatic updates.

[0088] The lending unit lends smart glasses, smartwatches, and tablets. The lending unit lends devices based on, for example, device specifications, usage scenarios, and delivery methods. The lending unit uses generative AI to select and lend the optimal device. The lending unit uses, for example, text generation AI (e.g., LLM) to analyze user data and select the optimal device. The lending unit can also use speech recognition technology to analyze user data and select the optimal device. The lending unit can also use facial recognition technology to analyze user data and select the optimal device. The lending unit implements a device selection function using an emotion engine or generative AI. This allows the lending unit to understand user data in detail and lend the optimal device. Some or all of the above processing in the lending unit may be performed using, for example, AI, or not using AI. For example, the lending unit can input user data into a generative AI and have the generative AI perform device selection.

[0089] The generation unit estimates the user's emotions and adjusts the way the generated solutions are presented based on the estimated emotions. For example, if the user is stressed, the generation unit generates a simple and intuitive solution. If the user is relaxed, the generation unit generates a detailed solution. If the user is in a hurry, the generation unit generates a quickly actionable solution. This allows the generation unit to provide solutions that are appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation 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-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the way the solutions are presented.

[0090] The generation unit generates the optimal solution by referring to the user's past solution usage history during generation. The generation unit generates solutions for similar problems based on solutions the user has used in the past, for example. The generation unit proposes the most effective solution based on the user's past solution usage history, for example. The generation unit analyzes the user's past solution usage history and generates the optimal solution, for example. In this way, the generation unit can provide the optimal solution based on past usage history. Some or all of the above processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's past solution usage history into a generation AI and have the generation AI perform the generation of the optimal solution.

[0091] The generation unit adjusts the priority of solutions based on the user's current work situation during generation. For example, the generation unit analyzes the user's current work situation and generates solutions prioritizing the most urgent issues. For example, the generation unit considers the user's workload and generates solutions that alleviate the burden. For example, the generation unit provides solutions at the optimal time based on the user's work progress. This allows the generation unit to provide solutions with priorities that correspond to the current work situation. Some or all of the above processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's current work situation into the generation AI and have the generation AI adjust the priority of solutions.

[0092] The generation unit estimates the user's emotions and adjusts the level of detail of the solution it generates based on the estimated emotions. For example, if the user is stressed, the generation unit generates a concise and to-the-point solution. If the user is relaxed, the generation unit generates a solution with detailed instructions. If the user is in a hurry, the generation unit generates a solution that can be quickly implemented. This allows the generation unit to provide solutions with a level of detail appropriate to the user's emotions. 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-described processes in the generation unit may be performed using a generative AI or not. For example, the generation unit can input user emotion data into a generative AI and have the generative AI adjust the level of detail of the solution.

[0093] The generation unit generates highly relevant solutions while considering the user's geographical location information. For example, the generation unit proposes the optimal solution based on the user's current location. For example, the generation unit generates solutions for region-specific issues while considering the user's geographical location information. For example, the generation unit proposes solutions that utilize nearby resources based on the user's location information. In this way, the generation unit can provide solutions that take geographical location information into account. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's geographical location information into a generation AI and have the generation AI perform the generation of highly relevant solutions.

[0094] The generation unit analyzes the user's social media activity during generation and generates relevant solutions. For example, the generation unit analyzes the user's social media activity and proposes relevant solutions. For example, the generation unit generates the optimal solution based on the user's social media posts. For example, the generation unit proposes solutions aligned with trends, taking into account the user's social media activity. In this way, the generation unit can provide solutions based on social media activity. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's social media activity into a generation AI and have the generation AI perform the generation of relevant solutions.

[0095] The support unit estimates the user's emotions and adjusts the support method based on the estimated emotions. For example, if the user is stressed, the support unit provides a simple and intuitive support method. For example, if the user is relaxed, the support unit provides a detailed support method. For example, if the user is in a hurry, the support unit provides a support method that can be quickly implemented. This allows the support unit to provide support methods that are appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, 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, for example, or not using AI. For example, the support unit can input user emotion data into a generative AI and have the generative AI adjust the support method.

[0096] The support unit selects the optimal support method by referring to the user's past support history when providing support. For example, the support unit proposes the optimal support method based on the user's past support history. For example, the support unit selects the most effective support method from the user's past support history. For example, the support unit analyzes the user's past support history and provides the optimal support method. In this way, the support unit can provide the optimal support method based on past support history. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's past support history into a generating AI and have the generating AI select the optimal support method.

[0097] The support department adjusts the priority of support based on the user's current work situation when providing assistance. For example, the support department analyzes the user's current work situation and prioritizes providing the most urgent support. For example, the support department considers the user's workload and provides support methods that reduce the burden. For example, the support department provides support at the optimal timing based on the user's work progress. This allows the support department to provide support with priorities according to the current work situation. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input the user's current work situation into a generating AI and have the generating AI perform the adjustment of support priorities.

[0098] The support unit estimates the user's emotions and adjusts the level of detail of the support based on the estimated emotions. For example, if the user is stressed, the support unit provides concise and to-the-point support. For example, if the user is relaxed, the support unit provides support that includes detailed instructions. For example, if the user is in a hurry, the support unit provides support that can be quickly implemented. This allows the support unit to provide support with a level of detail appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, 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 using AI. For example, the support unit can input user emotion data into a generative AI and have the generative AI adjust the level of detail of the support.

[0099] The support unit provides highly relevant support by considering the user's geographical location information during the support process. For example, the support unit proposes the optimal support method based on the user's current location. For example, the support unit provides support for region-specific issues by considering the user's geographical location information. For example, the support unit proposes support methods that utilize nearby resources based on the user's location information. In this way, the support unit can provide support that takes geographical location information into consideration. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing highly relevant support.

[0100] The support department analyzes the user's social media activity and provides relevant support during the support process. For example, the support department analyzes the user's social media activity and proposes relevant support methods. For example, the support department provides the optimal support method based on the user's social media posts. For example, the support department proposes support methods that align with trends, taking into account the user's social media activity. This allows the support department to provide support based on social media activity. Some or all of the above processes in the support department may be performed using AI, for example, or without AI. For example, the support department can input the user's social media activity into a generating AI and have the generating AI perform the provision of relevant support.

[0101] The solution unit estimates the user's emotions and adjusts the method of executing the solution based on the estimated user emotions. For example, if the user is stressed, the solution unit provides a simple and intuitive method of execution. For example, if the user is relaxed, the solution unit provides a detailed method of execution. For example, if the user is in a hurry, the solution unit provides a method that can be executed quickly. In this way, the solution unit can provide an execution method that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the solution unit may be performed using AI, for example, or not using AI. For example, the solution unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the execution method.

[0102] The resolution unit, upon resolving a problem, selects the optimal solution by referring to the user's past resolution history. The resolution unit, for example, proposes solutions for similar problems based on solutions the user has used in the past. The resolution unit, for example, selects the most effective solution from the user's past resolution history. The resolution unit, for example, analyzes the user's past resolution history and provides the optimal solution. In this way, the resolution unit can provide the optimal solution based on past resolution history. Some or all of the above processes in the resolution unit may be performed using AI, for example, or without AI. For example, the resolution unit can input the user's past resolution history into a generating AI and have the generating AI select the optimal solution.

[0103] The resolution unit adjusts the priority of solutions based on the user's current work situation when resolving a problem. For example, the resolution unit analyzes the user's current work situation and prioritizes providing solutions for the most urgent issues. For example, the resolution unit considers the user's workload and provides solutions that reduce the burden. For example, the resolution unit provides solutions at the optimal timing based on the user's work progress. This allows the resolution unit to provide solutions with priorities according to the current work situation. Some or all of the above processes in the resolution unit may be performed using AI, for example, or without AI. For example, the resolution unit can input the user's current work situation into a generating AI and have the generating AI perform the adjustment of the resolution priorities.

[0104] The solution unit estimates the user's emotions and adjusts the level of detail of the solution based on the estimated emotions. For example, if the user is stressed, the solution unit provides a concise and to-the-point solution. For example, if the user is relaxed, the solution unit provides a solution that includes detailed steps. For example, if the user is in a hurry, the solution unit provides a solution that can be quickly implemented. This allows the solution unit to provide solutions with a level of detail appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the solution unit may be performed using AI, for example, or not using AI. For example, the solution unit can input user emotion data into a generative AI and have the generative AI adjust the level of detail of the solution.

[0105] The solution unit provides highly relevant solutions while considering the user's geographical location information. For example, the solution unit proposes the optimal solution based on the user's current location. For example, the solution unit provides solutions for region-specific issues while considering the user's geographical location information. For example, the solution unit proposes solutions that utilize nearby resources based on the user's location information. In this way, the solution unit can provide solutions that take geographical location information into consideration. Some or all of the above processing in the solution unit may be performed using AI, for example, or without AI. For example, the solution unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing highly relevant solutions.

[0106] The solution unit analyzes the user's social media activity and provides relevant solutions during the solution process. For example, the solution unit analyzes the user's social media activity and proposes relevant solutions. For example, the solution unit provides the optimal solution based on the user's social media posts. For example, the solution unit proposes solutions aligned with trends, taking into account the user's social media activity. In this way, the solution unit can provide solutions based on social media activity. Some or all of the above-described processes in the solution unit may be performed using AI, for example, or without AI. For example, the solution unit can input the user's social media activity into a generating AI and have the generating AI perform the task of providing relevant solutions.

[0107] The sentiment analysis unit estimates the user's emotions and adjusts the sentiment analysis method based on the estimated user emotions. For example, if the user is stressed, the sentiment analysis unit provides a simple and intuitive sentiment analysis method. For example, if the user is relaxed, the sentiment analysis unit provides a detailed sentiment analysis method. For example, if the user is in a hurry, the sentiment analysis unit provides a quickly executable sentiment analysis method. In this way, the sentiment analysis unit can provide a sentiment analysis method that is appropriate to the user's emotions. Emotion estimation is achieved using a sentiment estimation function, for example, using 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 sentiment analysis unit may be performed using AI, for example, or without AI. For example, the sentiment analysis unit can input the user's emotion data into the generative AI and have the generative AI perform the adjustment of the sentiment analysis method.

[0108] The sentiment analysis unit selects the optimal analysis method by referring to the user's past emotional data during sentiment analysis. For example, the sentiment analysis unit proposes the optimal sentiment analysis method based on the user's past emotional data. For example, the sentiment analysis unit selects the most effective sentiment analysis method from the user's past emotional data. For example, the sentiment analysis unit analyzes the user's past emotional data and provides the optimal sentiment analysis method. In this way, the sentiment analysis unit can provide the optimal sentiment analysis method based on past emotional data. Some or all of the above processes in the sentiment analysis unit may be performed using AI, for example, or without AI. For example, the sentiment analysis unit can input the user's past emotional data into a generating AI and have the generating AI select the optimal sentiment analysis method.

[0109] The sentiment analysis unit estimates the user's emotions and adjusts the level of detail in the sentiment analysis based on the estimated emotions. For example, if the user is stressed, the sentiment analysis unit provides a concise and to-the-point sentiment analysis method. For example, if the user is relaxed, the sentiment analysis unit provides a sentiment analysis method that includes detailed steps. For example, if the user is in a hurry, the sentiment analysis unit provides a quickly executable sentiment analysis method. This allows the sentiment analysis unit to provide sentiment analysis with a level of detail appropriate to the user's emotions. Emotion estimation is achieved using a sentiment estimation function, for example, using 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-described processes in the sentiment analysis unit may be performed using AI or not using AI. For example, the sentiment analysis unit can input user emotion data into a generative AI and have the generative AI adjust the level of detail in the sentiment analysis.

[0110] The sentiment analysis unit analyzes highly relevant sentiment data while considering the user's geographical location information during sentiment analysis. For example, the sentiment analysis unit proposes the optimal sentiment analysis method based on the user's current location. For example, the sentiment analysis unit analyzes region-specific sentiment data while considering the user's geographical location information. For example, the sentiment analysis unit proposes a sentiment analysis method that utilizes nearby resources based on the user's location information. In this way, the sentiment analysis unit can provide sentiment data that takes geographical location information into account. Some or all of the above processing in the sentiment analysis unit may be performed using AI, for example, or without AI. For example, the sentiment analysis unit can input the user's geographical location information into a generating AI and have the generating AI perform the analysis of highly relevant sentiment data.

[0111] The risk management unit estimates the user's emotions and adjusts the risk management method based on the estimated user emotions. For example, if the user is stressed, the risk management unit provides a simple and intuitive risk management method. For example, if the user is relaxed, the risk management unit provides a detailed risk management method. For example, if the user is in a hurry, the risk management unit provides a risk management method that can be quickly implemented. In this way, the risk management unit can provide a risk management method that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the risk management unit may be performed using AI, for example, or not using AI. For example, the risk management unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the risk management method.

[0112] The risk management department selects the optimal risk management method by referring to the user's past risk data during risk management. For example, the risk management department proposes the optimal risk management method based on the user's past risk data. For example, the risk management department selects the most effective risk management method from the user's past risk data. For example, the risk management department analyzes the user's past risk data and provides the optimal risk management method. In this way, the risk management department can provide the optimal risk management method based on past risk data. Some or all of the above processes in the risk management department may be performed using AI, for example, or without AI. For example, the risk management department can input the user's past risk data into a generating AI and have the generating AI select the optimal risk management method.

[0113] The risk management unit estimates the user's emotions and adjusts the level of detail in risk management based on the estimated emotions. For example, if the user is stressed, the risk management unit provides a concise and to-the-point risk management method. For example, if the user is relaxed, the risk management unit provides a risk management method that includes detailed steps. For example, if the user is in a hurry, the risk management unit provides a risk management method that can be executed quickly. This allows the risk management unit to provide risk management with a level of detail appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the risk management unit may be performed using AI or not using AI. For example, the risk management unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the level of detail in risk management.

[0114] The Risk Management Department manages highly relevant risk data while considering the user's geographical location information during risk management. For example, the Risk Management Department proposes the optimal risk management method based on the user's current location. For example, the Risk Management Department manages region-specific risk data while considering the user's geographical location information. For example, the Risk Management Department proposes a risk management method that utilizes nearby resources based on the user's location information. In this way, the Risk Management Department can provide risk data that takes geographical location information into account. Some or all of the above processes in the Risk Management Department may be performed using AI, for example, or without AI. For example, the Risk Management Department can input the user's geographical location information into a generating AI and have the generating AI perform the management of highly relevant risk data.

[0115] The schedule management unit estimates the user's emotions and adjusts the schedule management method based on the estimated emotions. For example, if the user is stressed, the schedule management unit provides a simple and intuitive schedule management method. For example, if the user is relaxed, the schedule management unit provides a detailed schedule management method. For example, if the user is in a hurry, the schedule management unit provides a schedule management method that can be executed quickly. In this way, the schedule management unit can provide a schedule management method that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 schedule management unit may be performed using AI, for example, or without AI. For example, the schedule management unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the schedule management method.

[0116] The schedule management unit selects the optimal management method by referring to the user's past schedule data when managing schedules. For example, the schedule management unit proposes the optimal schedule management method based on the user's past schedule data. For example, the schedule management unit selects the most effective schedule management method from the user's past schedule data. For example, the schedule management unit analyzes the user's past schedule data and provides the optimal schedule management method. In this way, the schedule management unit can provide the optimal schedule management method based on past schedule data. Some or all of the above processes in the schedule management unit may be performed using AI, for example, or without AI. For example, the schedule management unit can input the user's past schedule data into a generating AI and have the generating AI select the optimal schedule management method.

[0117] The schedule management unit estimates the user's emotions and adjusts the level of detail in schedule management based on the estimated emotions. For example, if the user is stressed, the schedule management unit provides a concise and to-the-point schedule management method. For example, if the user is relaxed, the schedule management unit provides a schedule management method that includes detailed steps. For example, if the user is in a hurry, the schedule management unit provides a schedule management method that can be executed quickly. In this way, the schedule management unit can provide schedule management with a level of detail that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 schedule management unit may be performed using AI, for example, or not using AI. For example, the schedule management unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the level of detail in schedule management.

[0118] The schedule management unit manages highly relevant schedule data while considering the user's geographical location information. For example, the schedule management unit proposes the optimal schedule management method based on the user's current location. For example, the schedule management unit manages region-specific schedule data while considering the user's geographical location information. For example, the schedule management unit proposes a schedule management method that utilizes nearby resources based on the user's location information. In this way, the schedule management unit can provide schedule data that takes geographical location information into account. Some or all of the above processing in the schedule management unit may be performed using AI, for example, or without AI. For example, the schedule management unit can input the user's geographical location information into a generating AI and have the generating AI perform the management of highly relevant schedule data.

[0119] The task assignment unit estimates the user's emotions and adjusts the task assignment method based on the estimated emotions. For example, if the user is stressed, the task assignment unit provides a simple and intuitive task assignment method. For example, if the user is relaxed, the task assignment unit provides a detailed task assignment method. For example, if the user is in a hurry, the task assignment unit provides a quickly executable task assignment method. In this way, the task assignment unit can provide a task assignment method that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the task assignment unit may be performed using AI, for example, or not using AI. For example, the task assignment unit can input user emotion data into the generative AI and have the generative AI adjust the task assignment method.

[0120] The task assignment unit selects the optimal assignment method by referring to the user's past task data when assigning tasks. The task assignment unit proposes the optimal task assignment method based on the user's past task data, for example. The task assignment unit selects the most effective task assignment method from the user's past task data, for example. The task assignment unit analyzes the user's past task data and provides the optimal task assignment method, for example. In this way, the task assignment unit can provide the optimal task assignment method based on past task data. Some or all of the above processes in the task assignment unit may be performed using AI, for example, or without AI. For example, the task assignment unit can input the user's past task data into a generating AI and have the generating AI select the optimal task assignment method.

[0121] The task assignment unit estimates the user's emotions and adjusts the level of detail in task assignments based on the estimated emotions. For example, if the user is stressed, the task assignment unit provides a concise and to-the-point task assignment method. For example, if the user is relaxed, the task assignment unit provides a task assignment method that includes detailed steps. For example, if the user is in a hurry, the task assignment unit provides a task assignment method that can be executed quickly. This allows the task assignment unit to provide task assignments with a level of detail appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the task assignment unit may be performed using AI, for example, or without AI. For example, the task assignment unit can input user emotion data into a generative AI and have the generative AI adjust the level of detail in task assignments.

[0122] The task assignment unit assigns highly relevant task data to users while considering their geographical location information. The task assignment unit proposes an optimal task assignment method based on the user's current location, for example. The task assignment unit assigns region-specific task data while considering the user's geographical location information, for example. The task assignment unit proposes a task assignment method that utilizes nearby resources based on the user's location information, for example. This allows the task assignment unit to provide task data that takes geographical location information into account. Some or all of the above-described processes in the task assignment unit may be performed using AI, for example, or without AI. For example, the task assignment unit can input the user's geographical location information into a generating AI and have the generating AI perform the assignment of highly relevant task data.

[0123] The data analysis unit estimates the user's emotions and adjusts the data analysis method based on the estimated emotions. For example, if the user is stressed, the data analysis unit provides a simple and intuitive data analysis method. For example, if the user is relaxed, the data analysis unit provides a detailed data analysis method. For example, if the user is in a hurry, the data analysis unit provides a data analysis method that can be executed quickly. In this way, the data analysis unit can provide a data analysis method that is appropriate for the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data analysis unit may be performed using AI, for example, or not using AI. For example, the data analysis unit can input user emotion data into a generative AI and have the generative AI adjust the data analysis method.

[0124] The data analysis department selects the optimal analysis method by referring to the user's past data during data analysis. For example, the data analysis department proposes the optimal data analysis method based on the user's past data. For example, the data analysis department selects the most effective data analysis method from the user's past data. For example, the data analysis department analyzes the user's past data and provides the optimal data analysis method. In this way, the data analysis department can provide the optimal data analysis method based on past data. Some or all of the above processes in the data analysis department may be performed using AI, for example, or without AI. For example, the data analysis department can input the user's past data into a generating AI and have the generating AI perform the selection of the optimal data analysis method.

[0125] The data analysis unit estimates the user's emotions and adjusts the level of detail in the data analysis based on the estimated emotions. For example, if the user is stressed, the data analysis unit provides a concise and to-the-point data analysis method. For example, if the user is relaxed, the data analysis unit provides a data analysis method that includes detailed steps. For example, if the user is in a hurry, the data analysis unit provides a data analysis method that can be executed quickly. This allows the data analysis unit to provide data analysis with a level of detail appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data analysis unit may be performed using AI or not using AI. For example, the data analysis unit can input user emotion data into a generative AI and have the generative AI adjust the level of detail in the data analysis.

[0126] The data analysis department analyzes highly relevant data while considering the user's geographical location information. For example, the data analysis department proposes the optimal data analysis method based on the user's current location. For example, the data analysis department analyzes region-specific data while considering the user's geographical location information. For example, the data analysis department proposes a data analysis method that utilizes nearby resources based on the user's location information. In this way, the data analysis department can provide data that takes geographical location information into account. Some or all of the above processes in the data analysis department may be performed using AI, for example, or without AI. For example, the data analysis department can input the user's geographical location information into a generating AI and have the generating AI perform the analysis of highly relevant data.

[0127] The learning delivery unit estimates the user's emotions and adjusts the method of delivering learning content based on the estimated user emotions. For example, if the user is stressed, the learning delivery unit provides simple and intuitive learning content. For example, if the user is relaxed, the learning delivery unit provides detailed learning content. For example, if the user is in a hurry, the learning delivery unit provides content that allows for quick learning. In this way, the learning delivery unit can provide a method of delivering learning content that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning delivery unit may be performed using AI, for example, or not using AI. For example, the learning delivery unit can input user emotion data into the generative AI and have the generative AI adjust the method of delivering learning content.

[0128] The learning delivery unit provides optimal content by referring to the user's past learning data when providing learning. For example, the learning delivery unit proposes optimal learning content based on the user's past learning data. For example, the learning delivery unit selects the most effective learning content from the user's past learning data. For example, the learning delivery unit analyzes the user's past learning data and provides optimal learning content. In this way, the learning delivery unit can provide optimal learning content based on past learning data. Some or all of the above processes in the learning delivery unit may be performed using AI, for example, or without AI. For example, the learning delivery unit can input the user's past learning data into a generating AI and have the generating AI perform the task of providing optimal learning content.

[0129] The learning delivery unit estimates the user's emotions and adjusts the level of detail in the learning content based on the estimated emotions. For example, if the user is stressed, the learning delivery unit provides concise and to-the-point learning content. For example, if the user is relaxed, the learning delivery unit provides learning content that includes detailed instructions. For example, if the user is in a hurry, the learning delivery unit provides content that allows for quick learning. This allows the learning delivery unit to provide learning content with a level of detail appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning delivery unit may be performed using AI or not using AI. For example, the learning delivery unit can input user emotion data into the generative AI and have the generative AI adjust the level of detail in the learning content.

[0130] The learning delivery unit provides highly relevant content while considering the user's geographical location information. For example, the learning delivery unit suggests optimal learning content based on the user's current location. For example, the learning delivery unit provides region-specific learning content while considering the user's geographical location information. For example, the learning delivery unit suggests learning content that utilizes nearby resources based on the user's location information. In this way, the learning delivery unit can provide learning content that takes geographical location information into consideration. Some or all of the above processing in the learning delivery unit may be performed using AI, for example, or without AI. For example, the learning delivery unit can input the user's geographical location information into a generating AI and have the generating AI execute the provision of highly relevant learning content.

[0131] The knowledge delivery unit estimates the user's emotions and adjusts the method of knowledge delivery based on the estimated user emotions. For example, if the user is stressed, the knowledge delivery unit provides a simple and intuitive method of knowledge delivery. For example, if the user is relaxed, the knowledge delivery unit provides a detailed method of knowledge delivery. For example, if the user is in a hurry, the knowledge delivery unit provides a quickly actionable method of knowledge delivery. In this way, the knowledge delivery unit can provide a method of knowledge delivery that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the knowledge delivery unit may be performed using AI or not using AI. For example, the knowledge delivery unit can input user emotion data into the generative AI and have the generative AI adjust the method of knowledge delivery.

[0132] The knowledge provision unit selects the optimal provision method by referring to the user's past knowledge data when providing knowledge. For example, the knowledge provision unit proposes the optimal knowledge provision method based on the user's past knowledge data. For example, the knowledge provision unit selects the most effective knowledge provision method from the user's past knowledge data. For example, the knowledge provision unit analyzes the user's past knowledge data and provides the optimal knowledge provision method. In this way, the knowledge provision unit can provide the optimal knowledge provision method based on past knowledge data. Some or all of the above processes in the knowledge provision unit may be performed using AI, for example, or without AI. For example, the knowledge provision unit can input the user's past knowledge data into a generating AI and have the generating AI select the optimal knowledge provision method.

[0133] The knowledge delivery unit estimates the user's emotions and adjusts the level of detail in the knowledge delivery based on the estimated emotions. For example, if the user is stressed, the knowledge delivery unit provides a concise and to-the-point knowledge delivery method. For example, if the user is relaxed, the knowledge delivery unit provides a knowledge delivery method that includes detailed instructions. For example, if the user is in a hurry, the knowledge delivery unit provides a knowledge delivery method that can be quickly implemented. This allows the knowledge delivery unit to provide knowledge delivery with a level of detail appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the knowledge delivery unit may be performed using AI, for example, or not using AI. For example, the knowledge delivery unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the level of detail in the knowledge delivery.

[0134] The knowledge provision unit provides highly relevant knowledge data while considering the user's geographical location information. For example, the knowledge provision unit proposes the optimal knowledge provision method based on the user's current location. For example, the knowledge provision unit provides region-specific knowledge data while considering the user's geographical location information. For example, the knowledge provision unit proposes a knowledge provision method that utilizes nearby resources based on the user's location information. In this way, the knowledge provision unit can provide knowledge data that takes geographical location information into consideration. Some or all of the above processing in the knowledge provision unit may be performed using AI, for example, or without AI. For example, the knowledge provision unit can input the user's geographical location information into a generating AI and have the generating AI perform the provision of highly relevant knowledge data.

[0135] The lending unit estimates the user's emotions and selects a device to lend based on the estimated emotions. For example, if the user is stressed, the lending unit lends a simple and intuitive device. For example, if the user is relaxed, the lending unit lends a device with detailed functions. For example, if the user is in a hurry, the lending unit lends a device that can be operated quickly. In this way, the lending unit can provide a device that is appropriate for the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the lending unit may be performed using AI or not using AI. For example, the lending unit can input user emotion data into a generative AI and have the generative AI perform the device selection.

[0136] The lending unit selects the optimal device at the time of lending by referring to the user's past device usage history. The lending unit proposes the optimal device based on the user's past device usage history. The lending unit selects the most effective device from the user's past device usage history. The lending unit analyzes the user's past device usage history and provides the optimal device. In this way, the lending unit can provide the optimal device based on past device usage history. Some or all of the above processes in the lending unit may be performed using AI, for example, or without AI. For example, the lending unit can input the user's past device usage history into a generating AI and have the generating AI perform the selection of the optimal device.

[0137] The lending unit estimates the user's emotions and adjusts the settings of the lending device based on the estimated emotions. For example, if the user is stressed, the lending unit will set the settings to be simple and intuitive. For example, if the user is relaxed, the lending unit will set the settings to be detailed. For example, if the user is in a hurry, the lending unit will set the settings to be quickly operable. This allows the lending unit to provide a device with settings that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the lending unit may be performed using AI or not using AI. For example, the lending unit can input user emotion data into a generative AI and have the generative AI perform the device settings.

[0138] The lending unit lends highly relevant devices, taking into account the user's geographical location information at the time of lending. For example, the lending unit suggests the optimal device based on the user's current location. For example, the lending unit lends region-specific devices, taking into account the user's geographical location information. For example, the lending unit suggests devices that utilize nearby resources based on the user's location information. In this way, the lending unit can provide devices that take geographical location information into account. Some or all of the above processing in the lending unit may be performed using AI, for example, or without AI. For example, the lending unit can input the user's geographical location information into a generating AI and have the generating AI perform the lending of highly relevant devices.

[0139] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0140] The generation unit can also generate the optimal solution by referring to the user's past solution usage history. For example, it can generate solutions for similar problems based on solutions the user has used in the past. It can also suggest the most effective solution based on the user's past solution usage history. Furthermore, it can analyze the user's past solution usage history and generate the optimal solution. In this way, the generation unit can provide the optimal solution based on past usage history.

[0141] The support unit can also estimate the user's emotions and adjust the support method based on those estimates. For example, if the user is stressed, it can provide simple and intuitive support. If the user is relaxed, it can provide more detailed support. Furthermore, if the user is in a hurry, it can provide support that can be implemented quickly. In this way, the support unit can provide support methods that are tailored to the user's emotions.

[0142] The solution unit can also adjust the priority of solutions based on the user's current work situation. For example, it can analyze the user's current work situation and prioritize generating solutions for the most urgent issues. It can also consider the user's workload and generate solutions that reduce the burden. Furthermore, it can provide solutions at the optimal time based on the user's work progress. In this way, the solution unit can provide solutions with priorities that match the current work situation.

[0143] The emotion analysis unit can estimate the user's emotions and adjust the emotion analysis method based on the estimated emotions. For example, if the user is stressed, it can provide a simple and intuitive emotion analysis method. If the user is relaxed, it can provide a more detailed emotion analysis method. Furthermore, if the user is in a hurry, it can provide a quickly actionable emotion analysis method. In this way, the emotion analysis unit can provide an emotion analysis method that is appropriate for the user's emotions.

[0144] The risk management department can also select the optimal risk management method by referring to the user's past risk data. For example, it can propose the optimal risk management method based on the user's past risk data. It can also select the most effective risk management method from the user's past risk data. Furthermore, it can analyze the user's past risk data and provide the optimal risk management method. In this way, the risk management department can provide the optimal risk management method based on past risk data.

[0145] The scheduling unit can also estimate the user's emotions and adjust the scheduling method based on those emotions. For example, if the user is stressed, it can provide a simple and intuitive scheduling method. If the user is relaxed, it can provide a more detailed scheduling method. Furthermore, if the user is in a hurry, it can provide a scheduling method that can be implemented quickly. In this way, the scheduling unit can provide scheduling methods that are tailored to the user's emotions.

[0146] The task assignment unit can also select the optimal assignment method by referring to the user's past task data. For example, it can propose the optimal task assignment method based on the user's past task data. It can also select the most effective task assignment method from the user's past task data. Furthermore, it can analyze the user's past task data and provide the optimal task assignment method. In this way, the task assignment unit can provide the optimal task assignment method based on past task data.

[0147] The data analysis department can also estimate the user's emotions and adjust the level of detail in the data analysis based on those emotions. For example, if the user is stressed, it can provide a concise and to-the-point data analysis method. If the user is relaxed, it can provide a data analysis method that includes detailed steps. Furthermore, if the user is in a hurry, it can provide a data analysis method that can be executed quickly. In this way, the data analysis department can provide data analysis with a level of detail that matches the user's emotions.

[0148] The learning delivery unit can also provide optimal content by referring to the user's past learning data. For example, it can suggest the most suitable learning content based on the user's past learning data. It can also select the most effective learning content from the user's past learning data. Furthermore, it can analyze the user's past learning data and provide the most suitable learning content. In this way, the learning delivery unit can provide optimal learning content based on past learning data.

[0149] The knowledge delivery unit can also estimate the user's emotions and adjust the level of detail in the knowledge delivery based on those emotions. For example, if the user is stressed, it can provide a concise and to-the-point knowledge delivery method. If the user is relaxed, it can provide a knowledge delivery method that includes detailed instructions. Furthermore, if the user is in a hurry, it can provide a knowledge delivery method that can be quickly implemented. In this way, the knowledge delivery unit can deliver knowledge delivery with a level of detail that matches the user's emotions.

[0150] The following briefly describes the processing flow for example form 2.

[0151] Step 1: The generation unit provides a generation AI chat service. The generation unit has functions such as text generation, dialogue generation, and problem solving, and analyzes issues entered by sales technical members and proposes the optimal solution. For example, it uses text generation AI (LLM) and multimodal generation AI to analyze the content of the issue and propose a solution. It also has functions such as sentiment analysis, real-time risk management, schedule management, automatic task assignment, data analysis and proposals for process optimization, provision of personalized learning content, and automatic updating and provision of the knowledge base. Step 2: The support team provides one-on-one support based on the solutions provided by the generation team. The support team provides specific assistance to sales technical members through methods such as individual meetings, online chat, and telephone support. For example, they directly interact with sales technical members and support the implementation of solutions. They can also provide real-time support through online chat and detailed explanations through telephone support. Step 3: The Solution Department resolves the challenges faced by sales technical members, supported by the Support Department. The Solution Department implements the most suitable solution according to the type of problem, resolving the challenges faced by sales technical members based on the solutions proposed by the Generation Department. For example, it provides specific solutions to technical problems and implements effective solutions to challenges in sales activities. This allows for the rapid resolution of challenges faced by sales technical members and improves operational efficiency.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the generation unit, support unit, resolution unit, sentiment analysis unit, risk management unit, schedule management unit, task assignment unit, data analysis unit, learning provision unit, knowledge provision unit, and lending unit, is implemented by, for example, at least one of the smart device 14 and the data processing device 12. For example, the generation unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The support unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The resolution unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The sentiment analysis unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The risk management unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The schedule management unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The task assignment unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The data analysis unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The learning provision unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The knowledge provision unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The lending unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0156] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] Each of the multiple elements described above, including the generation unit, support unit, resolution unit, sentiment analysis unit, risk management unit, schedule management unit, task assignment unit, data analysis unit, learning provision unit, knowledge provision unit, and lending unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The support unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The resolution unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The sentiment analysis unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The risk management unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The schedule management unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The task assignment unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The data analysis unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The learning provision unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The knowledge provision unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The lending unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0172] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.).

[0184] 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.

[0185] 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.

[0186] 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.

[0187] Each of the multiple elements described above, including the generation unit, support unit, resolution unit, sentiment analysis unit, risk management unit, schedule management unit, task assignment unit, data analysis unit, learning provision unit, knowledge provision unit, and lending unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The support unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The resolution unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The sentiment analysis unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The risk management unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The schedule management unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The task assignment unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The data analysis unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The learning provision unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The knowledge provision unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The lending unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0188] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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).

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.).

[0201] 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.

[0202] 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.

[0203] 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.

[0204] Each of the multiple elements described above, including the generation unit, support unit, resolution unit, sentiment analysis unit, risk management unit, schedule management unit, task assignment unit, data analysis unit, learning provision unit, knowledge provision unit, and lending unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The support unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The resolution unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The sentiment analysis unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The risk management unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The schedule management unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The task assignment unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The data analysis unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The learning provision unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The knowledge provision unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The lending unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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."

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] (Note 1) The generation unit provides a generated AI chat service, Based on the solution provided by the generation unit, the support unit provides one-on-one support, The system includes a solution unit that resolves issues faced by sales technical members supported by the aforementioned support unit. A system characterized by the following features. (Note 2) It is equipped with an emotion analysis department that performs emotion analysis. The system described in Appendix 1, characterized by the features described herein. (Note 3) It has a risk management department that performs real-time risk management. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a schedule management department for managing schedules. The system described in Appendix 1, characterized by the features described herein. (Note 5) It includes a task assignment unit that performs automatic task assignment. The system described in Appendix 1, characterized by the features described herein. (Note 6) The company has a data analysis department that performs data analysis and makes recommendations for process optimization. The system described in Appendix 1, characterized by the features described herein. (Note 7) It includes a learning delivery unit that provides personalized learning content. The system described in Appendix 1, characterized by the features described herein. (Note 8) It includes a knowledge provision department that automatically updates and provides the knowledge base. The system described in Appendix 1, characterized by the features described herein. (Note 9) The facility includes a lending department that provides smart glasses, smartwatches, and tablets. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is It estimates the user's emotions and adjusts how the solutions generated are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is During generation, the system references the user's past solution usage history to generate the optimal solution. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is During generation, the priority of solutions is adjusted based on the user's current work situation. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the level of detail of the solutions generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, the system considers the user's geographical location to generate highly relevant solutions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, the system analyzes the user's social media activity and generates relevant solutions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned support unit, It estimates the user's emotions and adjusts the support method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned support unit, When providing support, the system selects the most suitable support method by referring to the user's past support history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned support unit, When providing support, we adjust the priority of assistance based on the user's current work situation. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned support unit, It estimates the user's emotions and adjusts the level of detail of support based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned support unit, When providing support, we take the user's geographical location into consideration to provide more relevant assistance. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned support unit, When providing support, we analyze the user's social media activity and offer relevant assistance. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned solution unit is It estimates the user's emotions and adjusts how the solution is implemented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned solution unit is When resolving an issue, the system will refer to the user's past resolution history to select the most suitable solution. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned solution unit is When resolving an issue, the priority of the resolution will be adjusted based on the user's current work situation. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned solution unit is It estimates the user's emotions and adjusts the level of detail in the solution based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned solution unit is When resolving an issue, the system will consider the user's geographical location to provide a more relevant solution. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned solution unit is When resolving an issue, we analyze the user's social media activity and provide relevant solutions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned emotion analysis unit, It estimates the user's emotions and adjusts the emotion analysis method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned emotion analysis unit, During sentiment analysis, the system selects the optimal analysis method by referring to the user's past sentiment data. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned emotion analysis unit, It estimates the user's emotions and adjusts the level of detail in the sentiment analysis based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned emotion analysis unit, When analyzing sentiment, we consider the user's geographical location to analyze highly relevant sentiment data. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned risk management department, We estimate user sentiment and adjust risk management methods based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned risk management department, During risk management, the optimal management method is selected by referring to the user's past risk data. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned risk management department, It estimates user sentiment and adjusts the level of detail in risk management based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned risk management department, When managing risk, consider the user's geographical location to manage highly relevant risk data. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned schedule management unit, It estimates the user's emotions and adjusts the schedule management method based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 37) The aforementioned schedule management unit, When managing schedules, the system selects the optimal management method by referring to the user's past schedule data. The system described in Appendix 4, characterized by the features described herein. (Note 38) The aforementioned schedule management unit, It estimates the user's emotions and adjusts the level of detail in schedule management based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned schedule management unit, When managing schedules, consider the user's geographical location to manage highly relevant schedule data. The system described in Appendix 4, characterized by the features described herein. (Note 40) The task assignment unit, It estimates the user's emotions and adjusts the task assignment method based on the estimated user emotions. The system described in Appendix 5, characterized by the features described herein. (Note 41) The task assignment unit, When assigning tasks, the system selects the optimal assignment method by referring to the user's past task data. The system described in Appendix 5, characterized by the features described herein. (Note 42) The task assignment unit, It estimates the user's emotions and adjusts the level of detail in task assignments based on the estimated emotions. The system according to appended note 5, characterized by the above. (Appended note 43) The task assignment unit When assigning tasks, assigns highly relevant task data considering the user's geographical location information The system according to appended note 5, characterized by the above. (Appended note 44) The data analysis unit Estimates the user's emotion and adjusts the data analysis method based on the estimated user's emotion The system according to appended note 6, characterized by the above. (Appended note 45) The data analysis unit When performing data analysis, selects the optimal analysis method by referring to the user's past data The system according to appended note 6, characterized by the above. (Appended note 46) The data analysis unit Estimates the user's emotion and adjusts the detail level of data analysis based on the estimated user's emotion The system according to appended note 6, characterized by the above. (Appended note 47) The data analysis unit When performing data analysis, analyzes highly relevant data considering the user's geographical location information The system according to appended note 6, characterized by the above. (Appended note 48) The learning content providing unit Estimates the user's emotion and adjusts the learning content providing method based on the estimated user's emotion The system according to appended note 7, characterized by the above. (Appended note 49) The learning content providing unit When providing learning content, provides the optimal content by referring to the user's past learning data The system according to appended note 7, characterized by the above. (Appended note 50) The learning content providing unit It estimates the user's emotions and adjusts the level of detail in the learning content based on those estimated emotions. The system described in Appendix 7, characterized by the features described herein. (Note 51) The aforementioned learning provision unit, When providing learning materials, we will provide highly relevant content while taking into account the user's geographical location. The system described in Appendix 7, characterized by the features described herein. (Note 52) The aforementioned knowledge provision department, We estimate user sentiment and adjust the knowledge delivery method based on the estimated user sentiment. The system described in Appendix 8, characterized by the features described herein. (Note 53) The aforementioned knowledge provision department, When providing knowledge, the optimal delivery method is selected by referring to the user's past knowledge data. The system described in Appendix 8, characterized by the features described herein. (Note 54) The aforementioned knowledge provision department, It estimates the user's emotions and adjusts the level of detail in the knowledge provided based on those estimated emotions. The system described in Appendix 8, characterized by the features described herein. (Note 55) The aforementioned knowledge provision department, When providing knowledge, we consider the user's geographical location to provide highly relevant knowledge data. The system described in Appendix 8, characterized by the features described herein. (Note 56) The aforementioned lending unit is, The system estimates the user's emotions and selects the device to lend based on those estimated emotions. The system described in Appendix 9, characterized by the features described herein. (Note 57) The aforementioned lending unit is, When lending a device, the system selects the most suitable device by referring to the user's past device usage history. The system described in Appendix 9, characterized by the features described herein. (Note 58) The aforementioned lending unit is, It estimates the user's emotions and adjusts the settings of the loaned device based on the estimated user emotions. The system described in Appendix 9, characterized by the features described herein. (Note 59) The aforementioned lending unit is, When lending devices, the system will consider the user's geographical location to provide the most relevant devices. The system described in Appendix 9, characterized by the features described herein. [Explanation of Symbols]

[0224] 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 generation unit provides a generated AI chat service, Based on the solution provided by the generation unit, the support unit provides one-on-one support, The system includes a solution unit that resolves issues faced by sales technical members supported by the aforementioned support unit. A system characterized by the following features.

2. It is equipped with an emotion analysis department that performs emotion analysis. The system according to feature 1.

3. It has a risk management department that performs real-time risk management. The system according to feature 1.

4. It includes a schedule management department for managing schedules. The system according to feature 1.

5. It includes a task assignment unit that performs automatic task assignment. The system according to feature 1.

6. The company has a data analysis department that performs data analysis and makes recommendations for process optimization. The system according to feature 1.

7. It includes a learning delivery unit that provides personalized learning content. The system according to feature 1.

8. It includes a knowledge provision department that automatically updates and provides the knowledge base. The system according to feature 1.

Images